Retell AI vs Bland

Agent ops

1. ai-native userMy coding agent can provision a complete voice agent end to end — create the agent, attach a number, and place a call — through the API, CLI, or MCP without touching the dashboard

   weight 3 · round to Bland
   Retell AI~partialprobed6/10

   Retell ships an official Node/Python SDK with full voice endpoint coverage, a CLI for managing agents and phone numbers, and an MCP server whose meta-tools (list/get/invoke_api_endpoint) expose the entire API — including agent create/update/publish — to MCP clients like Cursor or Claude Code, and a keyless runtime probe confirms the MCP handshake works end-to-end. However, the flagship quick-start walkthrough is dashboard-centric (create in UI, assign number in 'configuration settings', test/call via dashboard button), and no evidence explicitly shows a CLI/API/MCP call sequence that attaches a number and places a live call without touching the UI. Missing for 10: an explicit end-to-end CLI/MCP example showing number-attach and call-placement commands, and independent confirmation that non-dashboard number provisioning/outbound calling works in practice.

   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”
   • [claimed-docs] “Retell supports the Model Context Protocol (MCP) so you can build Retell AI voice agents directly from MCP-capable clients (Cursor, Claude D…”
   • [claimed-docs] “Agents: create, update, publish, list, and fetch agent versions.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”
   • [claimed-docs] “Assign your agent to the number in the configuration settings”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland's docs show all three provisioning steps available programmatically: pathway/agent creation via API (bland-docs-1, bland-docs-18), phone number acquisition/porting/Twilio and SIP attachment (bland-docs-3, bland-docs-16, bland-docs-17), and call placement via API or batch calls (bland-docs-6, bland-docs-23). Both the CLI ('make calls, build and test pathways, configure phone numbers' — bland-docs-12) and the MCP server ('place and inspect calls, build and validate pathways, manage agents' — bland-docs-11/20/27) explicitly cover the full create-agent/attach-number/place-call lifecycle without the dashboard, and runtime probes confirm both the CLI and hosted MCP endpoint are live and functional (bland-probe-rt-1, bland-probe-rt-2). Missing for 10: a single consolidated end-to-end tutorial/example walking through create→attach→call in one flow, and independent (non-vendor) confirmation of the full pipeline working end-to-end.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”
   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

2. ai-native userThe platform's own AI helps me author agents — generating or improving prompts, flows, and test cases from a description

   weight 1 · round to Retell AI
   Retell AI~partialclaimed5/10

   Retell docs mention a 'Generate from prompt' feature where Conductor drafts an agent from a plain-English description (retell-docs-30), directly supporting AI-assisted agent authoring. However, there's no evidence of AI-driven improvement of existing prompts/flows or automatic test-case generation, and no independent/hands-on confirmation of Conductor's output quality. Missing for 10: documentation on iterative prompt/flow refinement by the platform's AI, automated test-case generation, and community or hands-on verification of Conductor's generated agents.

   • [claimed-docs] “click **Generate from prompt** (marked Suggested) to let [Conductor](/conductor/create-agent) draft an agent from a plain-English descriptio…”
   • [claimed-docs] “Retell agents are node-based flows or single prompts, with call handling, a knowledge base, and integrations built in.”
   • [claimed-docs] “Drag-and-drop, node-based flows for structured, high-stakes calls.”
   • [claimed-docs] “Single prompt agent... Define your whole agent with one prompt.”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland's MCP server lets external AI coding agents 'build and validate pathways ... through natural language' (bland-docs-11/20/27), and the testbed/evals tools support iterating on and grading prompts, which loosely supports AI-assisted authoring. However there is no evidence of a built-in, first-party generative feature where Bland's own platform AI drafts a full pathway/flow/test-cases from a plain-language description inside the product itself — the closest capability requires an external AI agent connecting via MCP. Missing for 10: a native 'describe your agent, we generate the pathway/prompts/tests' feature, in-product prompt-improvement AI, and independent/hands-on confirmation of AI-generated flows.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”

   proof ↗⚑ flag

Build

1. developerBuild a working phone voice agent — prompt, voice, and phone number — and take my first live call within an hour

   weight 3 · round drawn
   Retell AI✓fullcommunity8/10

   Docs walk through the exact flow described: create account, pick/generate a template, set prompt and voice, test in dashboard, assign a phone number, and make a live call, with a 15-minute quickstart and free trial credits removing payment friction. Community feedback confirms the demo/agent works end-to-end for real calls, though it also shows occasional conversational glitches (contradictions, confusion) that are quality issues rather than build-flow blockers. missing for 10: independent third-party timing confirmation that a full build-to-first-call truly completes within an hour, and evidence of phone-number provisioning speed in practice.

   • [claimed-docs] “Build your first Retell AI phone agent in 15 minutes: create an account, pick a template, test in the dashboard, deploy to a phone number, a…”
   • [claimed-docs] “Click the "Test" button to start a web call with your agent... This step is free and doesn't need a phone number or payment method.”
   • [claimed-docs] “click **Generate from prompt** (marked Suggested) to let [Conductor](/conductor/create-agent) draft an agent from a plain-English descriptio…”
   • [claimed-docs] “Click the "Test" button to start a web call with your agent”
   • [claimed-docs] “Assign your agent to the number in the configuration settings”
   • [claimed-docs] “New accounts start with \$10 in free trial credits, no payment method required.”
   • [claimed-docs] “Add custom voices to your Retell agent — search ElevenLabs community voices, import a voice clone, or train a clone for a unique brand-speci…”
   • [community] “Curious what model the dentist bot is running on? Tried it out, was surprisingly good, though eventually it contradicted itself (booked a sl…”
   • [community] “I tried to fool the Dental Office demo trying to book on Sunday or outside of the slots it had indicated, and it did a better job than many …”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Docs cover the whole first-call workflow: creating a pathway/prompt (bland-docs-18, bland-docs-26, bland-docs-30), voice cloning/selection (bland-docs-10), phone number setup via own Twilio or new inbound numbers (bland-docs-3, bland-docs-24), and dispatching outbound calls (bland-docs-23), plus a CLI and MCP server confirmed live at runtime (bland-probe-rt-1, bland-probe-rt-2) that let a developer configure and place calls quickly. Missing for 10: no independent hands-on account of a developer actually completing a first call within an hour, and one community comment notes cost concerns rather than time-to-first-call, so onboarding speed is only documented, not externally verified.

   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “A clone needs one clean sample of about ten seconds. Quality of the sample sets the ceiling on quality of the voice”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”
   • [claimed-docs] “Create inbound phone numbers for customer support, etc.”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

2. developerRun conversations in multiple languages, including detecting and switching language mid-call

   weight 2 · round drawn
   Retell AI—none0/10

   No documentation in the evidence pack describes multi-language conversation support, language detection, or mid-call language switching; the only related evidence is a founder comment stating multilingual support is merely 'on our roadmap' rather than shipped, indicating the capability does not currently exist.

   • [community] “Founder said multilingual support is on roadmap: 'It's definitely in our roadmap. After the core product—the voice AI part—becomes humanlike…”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of multi-language support, language detection, or mid-call language switching anywhere in Bland's docs; only pathways, TTS voice cloning, and infrastructure features are documented. Missing for 10: any documentation of multilingual conversation support, automatic language detection, or dynamic language switching mid-call.

   ⚑ flag

3. founderDesign multi-step conversation flows in a visual builder with branching, states, and handoffs without writing code

   weight 2 · round drawn
   Retell AI✓fullclaimed7/10

   Retell's docs describe node-based, drag-and-drop conversation flows for 'structured, high-stakes calls' as an alternative to single-prompt agents, with function calling enabling transfers, call-ending, and API calls that serve as handoff points — directly matching the branching/states/handoff story without requiring code. The quick-start flow (pick template, test in dashboard, deploy) reinforces a no-code workflow. Missing for 10: independent/hands-on evidence of the visual builder's branching UI itself (community evidence only covers conversational behavior, not the builder), and no detail on how 'states' are represented/connected beyond the general node-based description.

   • [claimed-docs] “Retell agents are node-based flows or single prompts, with call handling, a knowledge base, and integrations built in.”
   • [claimed-docs] “Drag-and-drop, node-based flows for structured, high-stakes calls.”
   • [claimed-docs] “Single prompt agent... Define your whole agent with one prompt.”
   • [claimed-docs] “Function calling lets Retell single or multi-prompt agents take real actions — transfer calls, end calls, book appointments, send SMS, and c…”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”
   • [claimed-docs] “Build your first Retell AI phone agent in 15 minutes: create an account, pick a template, test in the dashboard, deploy to a phone number, a…”
   • [claimed-docs] “click **Generate from prompt** (marked Suggested) to let [Conductor](/conductor/create-agent) draft an agent from a plain-English descriptio…”

   proof ↗⚑ flag

   Bland✓fullclaimed7/10

   Bland's 'Conversational Pathways' feature is explicitly a node-based flow builder where founders give instructions at specific conversation points, test individual node interactions in a pathway editor/testbed, and publish drafts separately from production—matching branching/states/handoffs without code (bland-docs-18, 26, 30, 28, 19). Missing for 10: explicit confirmation of a drag-and-drop visual canvas UI (docs describe 'nodes' and 'pathway editor' but no screenshot/UI walkthrough) and independent hands-on corroboration beyond vendor docs.

   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”

   proof ↗⚑ flag

Personalization

1. developerInject dynamic variables and per-caller context at call time so each conversation is personalized

   weight 2 · round to Bland
   Retell AI—none0/10

   The evidence pack covers function calling, webhooks, custom telephony, knowledge base retrieval, CRM field sync, and SDK/CLI/MCP tooling, but nowhere describes injecting dynamic variables or per-caller context (e.g., a startCall/create-call parameter for passing caller-specific data into the prompt/LLM at runtime). This is a fair, plausible capability for a voice-agent platform, so absence of evidence yields 'none' rather than 'na'.

   ⚑ flag

   Bland~partialclaimed5/10

   Evidence shows personalization mechanisms exist—Memory for per-caller context (bland-docs-9), batch calls that likely carry per-recipient data (bland-docs-6), and pathway webhooks/API integrations that could fetch live data (bland-docs-4, bland-docs-30)—but there is no explicit documentation of a 'dynamic variables' injection API or call-time variable substitution mechanism. missing for 10: explicit dynamic-variable/request_data injection documentation, examples of per-call variable interpolation into prompts, independent confirmation of personalization working in practice.

   • [claimed-docs] “Memory lets Bland agents remember people across conversations, including calls, SMS, and other channels, so each interaction feels continuou…”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

2. developerGround the agent on my documents with a built-in knowledge base or RAG so it answers from my content

   weight 2 · round to Retell AI
   Retell AI~partialclaimed6/10

   Retell explicitly ships a knowledge base feature where you can crawl websites or upload documents for the agent to retrieve from, and AI QA scores 'knowledge base accuracy' as a call metric, confirming grounding is a first-class capability. However, evidence is thin — only brief homepage-level mentions rather than a dedicated deep-dive doc on KB architecture/RAG internals, and there's no independent/hands-on confirmation of retrieval quality. Missing for 10: dedicated knowledge-base documentation page detailing chunking/retrieval mechanics, and independent verification that answers are accurately grounded in uploaded content.

   • [claimed-docs] “Retell agents are node-based flows or single prompts, with call handling, a knowledge base, and integrations built in.”
   • [claimed-docs] “Crawl a website or upload documents your agent retrieves from.”
   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is a voice AI/phone agent platform; evidence covers pathways, memory, tools, MCP, CLI, etc., but nowhere mentions a document knowledge base or RAG capability for grounding agent responses in uploaded content. Since a voice/conversational agent platform could plausibly ship this (many competitors do), absence of evidence makes this 'none' rather than 'na'.

   ⚑ flag

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round to Retell AI
   Retell AI✓fullprobed9/10

   A direct probe confirms an llms.txt file exists at docs.retellai.com/llms.txt returning HTTP 200 with a structured index of the docs, and Retell's docs are also agent-oriented enough to support MCP-based discovery/management of resources. This directly satisfies the story of pointing an agent at llms.txt or agent-oriented docs. Missing for 10: no independent third-party corroboration of llms.txt usage in the wild.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.retellai.com/llms.txt # Retell AI > Explore Retell AI docs to learn how to build, test, deploy, an…”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland has a confirmed live llms.txt at docs.bland.ai/llms.txt (HTTP 200) providing agent-oriented documentation, plus an official MCP server and CLI explicitly designed for AI coding agents to interact with the platform via natural language. missing for 10: independent third-party confirmation that agents actually consume llms.txt successfully in practice, and broader agent-oriented docs beyond the single llms.txt file.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.bland.ai/llms.txt # Bland Documentation Bland is an enterprise voice AI platform for high-volume, …”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to Bland
   Retell AI~partialprobed7/10

   Retell ships official Node.js/Python SDKs with API-key auth and a terminal CLI for managing agents, phone numbers, and resources, both of which are scriptable outside the dashboard, and a runtime probe confirms the CLI installs and runs keylessly via npm — all consistent with headless/CI use. However, there is no explicit CI/automation documentation (e.g., GitHub Actions example, testing-in-pipeline guide) confirming an officially supported headless workflow. Missing for 10: explicit CI/pipeline documentation or example, confirmation of non-interactive auth flow for CI secrets, and independent evidence of real-world CI usage.

   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “The Retell CLI lets you manage Retell resources from your terminal.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “official CLI documented at https://docs.retellai.com/get-started/cli”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland offers a full programmatic/REST API for pathways, calls, batch calls, webhooks, and evals, plus an official CLI (verified runtime installable keylessly via npx) for terminal-based automation, enabling headless/CI usage. missing for 10: no explicit CI/CD pipeline example (e.g., GitHub Actions) or independent case study confirming CI usage beyond docs and CLI probe.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round to Retell AI
   Retell AI✓fullclaimed8/10

   Retell explicitly documents connecting a live voice/chat agent to a remote MCP server so it can invoke that server's tools during calls, directly matching the story of plugging in external MCP servers for tool use. This is a first-party, well-specified capability (not just Retell exposing its own API as MCP) covering both single- and multi-prompt agents. Missing for 10: independent/hands-on verification that a third-party MCP server's tools work reliably mid-call in production.

   • [claimed-docs] “Connect a Retell single- or multi-prompt agent to a remote MCP server so it can call the server's tools during a live voice or chat conversa…”
   • [claimed-docs] “Connect your single- or multi-prompt agent to a remote Model Context Protocol (MCP) server, and the agent can call that server's tools durin…”
   • [claimed-docs] “Connect your single- or multi-prompt agent to a remote [Model Context Protocol (MCP)] server, and the agent can call that server's tools dur…”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”

   proof ↗⚑ flag

   Bland—none0/10

   Bland's evidence only documents it exposing an outbound MCP server so that external AI coding agents can call Bland's own tools (docs-11, docs-20, docs-27, probe-rt-2) — the reverse direction of this story. There is no evidence that Bland itself can consume/plug in third-party MCP servers as a client; its tool integration story is limited to custom HTTP API endpoints and webhooks (bland-docs-4, bland-docs-25).

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

4. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Retell AI
   Retell AI✓fullprobed9/10

   Retell documents and hosts an official MCP server (mcp.retellai.com) that lets MCP-capable clients like Cursor, Claude Desktop, and Claude Code build and manage Retell voice agents via the Retell API, exposing meta-tools for listing/invoking endpoints. A runtime probe confirms the server completes a full keyless JSON-RPC initialize handshake and exposes its tool set, corroborating the vendor docs. Missing for 10: independent/community usage reports of the MCP server in practice beyond vendor docs and the probe.

   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”
   • [claimed-docs] “Retell supports the Model Context Protocol (MCP) so you can build Retell AI voice agents directly from MCP-capable clients (Cursor, Claude D…”
   • [claimed-docs] “Retell supports the [Model Context Protocol (MCP)] so you can build Retell AI voice agents directly from MCP-capable clients (Cursor, Claude…”
   • [claimed-docs] “Agents: create, update, publish, list, and fetch agent versions.”
   • [probe] “official MCP server documented at https://docs.retellai.com/get-started/mcp-server”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland ships an official MCP server (docs and runtime probe confirm it's live and gated by API key) that lets AI coding agents place/inspect calls, build pathways, manage agents, query analytics, run evals, and search docs — directly fulfilling the story. Missing for 10: independent third-party hands-on review of the MCP server beyond vendor docs/probe.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round drawn
   Retell AI✓fullprobed9/10

   Retell ships an official CLI (`@retell-ai/retell-cli`) documented for managing agents, phone numbers, and knowledge bases from the terminal, and a runtime probe confirms it installs and runs keylessly via npx. Missing for 10: independent/community usage reports of the CLI itself (only docs and one probe run corroborate it).

   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “The Retell CLI lets you manage Retell resources from your terminal.”
   • [probe] “official CLI documented at https://docs.retellai.com/get-started/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”

   proof ↗⚑ flag

   Bland✓fullprobed9/10

   Bland ships an official CLI (bland-cli) documented to manage the entire account from the terminal, and a runtime probe confirms it installs and runs via npx keylessly. missing for 10: independent third-party review/usage reports of the CLI beyond the official docs and one probe run.

   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”

   proof ↗⚑ flag

6. ai-native userDrive the product through a documented public API

   weight 3 · round drawn
   Retell AI✓fullprobed9/10

   Retell documents official Node.js/Python SDKs with typed clients and full endpoint coverage, a CLI for managing resources from the terminal, and API references (e.g., clone-voice endpoint) — and a runtime probe confirms the CLI installs/runs keylessly and the hosted MCP server exposes the entire API via meta-tools, proving the API is genuinely agent-drivable. Missing for 10: a publicly discoverable OpenAPI/swagger spec (all standard paths returned 404 in the probe).

   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [claimed-docs] “Type safety: Full TypeScript support with autocomplete ... Error handling: Structured error responses with detailed messages”
   • [probe] “official MCP server documented at https://docs.retellai.com/get-started/mcp-server”
   • [probe] “official CLI documented at https://docs.retellai.com/get-started/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.retellai.com/openapi.json, https://docs.retellai.com/swagger.json, https://docs.retella…”

   proof ↗⚑ flag

   Bland✓fullprobed9/10

   Bland documents a comprehensive public API (pathways, calls, tools, webhooks, evals, batch calls) plus SDKs, CLI, and an official MCP server, and runtime probes confirm the CLI installs and the hosted MCP endpoint is live and gated as documented, showing agentic programmatic control. Missing for 10: a formally published OpenAPI/Swagger spec (probe found 404s at standard OpenAPI paths), so machine-readable spec discoverability is unconfirmed.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

7. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   Retell AI—none0/10

   The evidence pack documents API key auth, SDKs, CLI, and an MCP server, but nowhere describes scoped, role-based, or least-privilege API key/credential issuance (e.g., per-agent or per-permission keys) for Retell. This is a fair axis for an API/voice-agent platform, but no docs or probes show scoped credential support, so it is unproven.

   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”

   proof ↗⚑ flag

   Bland—none0/10

   Evidence shows generic API-key auth and JWT-based webhook verification (bland-docs-15), but nothing about issuing scoped or least-privilege credentials specific to an agent's permissions (e.g., role-based API keys, scoped tokens limiting call/pathway/account access). Missing for 10: documentation of scoped API key creation, permission levels, or per-agent credential restriction.

   • [claimed-docs] “JWT signatures eliminate these risks through asymmetric cryptography - you verify requests using our public JWKS endpoint without storing an…”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Retell AI
   Retell AI✓fullprobed9/10

   Retell documents and ships official typed SDKs for Node.js and Python with API key auth, full endpoint coverage, structured errors, and TypeScript autocomplete, plus an official CLI and MCP server for programmatic/agentic access — all independently confirmed by runtime probes (CLI installs and runs, MCP server completes handshake). Missing for 10: no independent third-party review of SDK quality/DX beyond vendor docs and no public OpenAPI spec discovered.

   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [claimed-docs] “Type safety: Full TypeScript support with autocomplete ... Error handling: Structured error responses with detailed messages”
   • [probe] “official CLI documented at https://docs.retellai.com/get-started/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “official MCP server documented at https://docs.retellai.com/get-started/mcp-server”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”

   proof ↗⚑ flag

   Bland~partialprobed6/10

   Bland documents a Web Agent SDK for embedding voice agents (React/Vanilla JS/Node), a CLI, and a REST API used throughout tutorials, giving AI-native developers concrete building blocks; runtime probes confirm the CLI installs and runs. However, no dedicated 'official SDK' page for server-side languages (Python/Node backend SDK) is evidenced, and openapi/swagger spec endpoints all 404, suggesting the API reference isn't machine-consumable in a standard SDK-generation format. Missing for 10: a clearly documented multi-language backend SDK (Python/Node) beyond the browser widget SDK, and a working OpenAPI spec for auto-generating clients.

   • [claimed-docs] “Embed a Bland voice agent into any web application (React, Vanilla JS, or Node). The SDK handles secure authentication between your server a…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”
   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round to Retell AI
   Retell AI✓fullclaimed8/10

   Retell has dedicated first-party documentation for webhooks describing real-time event notifications pushed to your application as events occur, enabling event-driven integrations rather than polling — directly matching the story of subscribing to events via webhooks. Missing for 10: no independent/hands-on corroboration of webhook reliability or event catalog completeness beyond the docs themselves.

   • [claimed-docs] “Webhooks allow your application to receive real-time notifications about events that occur in your Retell AI account.”
   • [claimed-docs] “webhooks push data to your application as events happen, making your integrations more efficient and responsive.”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland documents post-call webhooks (automatic HTTP notifications sent after a call completes) and pathway-level webhook execution at specific conversation points, showing genuine event-driven webhook support tied to call lifecycle. However, evidence only covers call-related events (completion, in-call triggers) — missing for 10: a general-purpose event subscription/webhook management API covering other account events (e.g., evals, batch campaign status, pathway publishes), and any independent confirmation of webhook reliability/configuration options.

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round to Retell AI
   Retell AI~partialclaimed6/10

   Retell's AI QA feature automatically scores calls on hallucination, knowledge-base accuracy, latency, sentiment, and tool usage to surface quality trends and issues, and custom analytics dashboards let users chart/filter call and chat metrics — both are AI-derived insights from the product's own call data. However, this is scoring/quality analytics rather than proactive generative 'suggestions' (e.g., recommended actions, next-best-response, or coaching tips), and there's no independent/hands-on evidence validating the accuracy or usefulness of these AI-generated insights. Missing for 10: evidence of proactive suggestion/recommendation generation beyond scoring, and third-party corroboration of AI QA insight quality.

   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”
   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”
   • [claimed-docs] “The Analytics dashboard charts your call and chat data so you can see how your agents are performing over time.”
   • [claimed-docs] “Built-in call success and sentiment scoring, plus custom fields synced to your CRM.”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland offers LLM-judge Evals to grade call quality and an MCP integration that can 'query analytics' on your account data, which are AI-generated evaluative outputs derived from your call data, but there's no dedicated insights/suggestions dashboard or proactive recommendation feature described. Missing for 10: a native analytics/insights UI, evidence of proactive suggestions surfaced to users, and independent confirmation of these AI-generated insights in practice.

   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “Memory lets Bland agents remember people across conversations, including calls, SMS, and other channels, so each interaction feels continuou…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”

   proof ↗⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round drawn
   Retell AI~partialcommunity6/10

   Retell agents act autonomously during live calls — function calling lets them independently book appointments, send SMS, transfer calls, and call external APIs without human intervention (retell-docs-5, retell-docs-35), and webhooks push real-time events to downstream systems (retell-docs-12, retell-docs-24). This shows in-call autonomous action-taking, which is a form of background automation once a call is triggered. However, there's no evidence of standalone scheduled/triggered automations running independent of a live voice/chat session (e.g., cron-like outbound campaigns or autonomous multi-step workflows outside conversation context). Missing for 10: evidence of scheduled or event-triggered background automations outside live calls, and independent/hands-on confirmation that autonomous function-calling reliably completes tasks unattended (community reports show some confusion/looping during autonomous task execution, retell-comm-1, retell-comm-3, retell-comm-5).

   • [claimed-docs] “Function calling lets Retell single or multi-prompt agents take real actions — transfer calls, end calls, book appointments, send SMS, and c…”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”
   • [claimed-docs] “Webhooks allow your application to receive real-time notifications about events that occur in your Retell AI account.”
   • [claimed-docs] “webhooks push data to your application as events happen, making your integrations more efficient and responsive.”
   • [community] “The AI contradicted itself when layering conditionals - it got confused about morning vs afternoon time, kept asking to repeat despite corre…”
   • [community] “Voice sounds great, but: told it unavailable until next year, it confirmed Feb 4th 'next year' but thought current year was 2022. Also got s…”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland supports batch calls, webhooks, pathways, and scheduled/triggered call campaigns that run without manual intervention, which constitute a form of autonomous background automation for voice workflows. However, this is scoped to phone-call automation only, not general-purpose background task/agent scheduling. missing for 10: evidence of a generic scheduler/cron-like trigger system, independent hands-on validation of unattended background runs, and confirmation of failure handling/monitoring for long-running autonomous automations.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”

   proof ↗⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round to Bland
   Retell AI~partialclaimed3/10

   Retell's dashboard includes 'Conductor', a built-in AI assistant that can draft an entire agent from a plain-English prompt (docs-30), which is a genuine instance of delegating a task to an in-product AI assistant. However this is a single thin mention with no further detail on scope, limits, or other delegable tasks beyond initial agent creation. Missing for 10: documentation of Conductor's full capabilities/limits, evidence of delegating other tasks (not just agent drafting) to a built-in assistant, and independent/hands-on corroboration of Conductor actually working.

   • [claimed-docs] “click **Generate from prompt** (marked Suggested) to let [Conductor](/conductor/create-agent) draft an agent from a plain-English descriptio…”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland's core product is a built-in AI voice agent that users delegate tasks to (placing/answering calls, executing pathways, calling APIs, remembering context) rather than a separate feature bolted on — e.g., 'Dispatch AI phone calls to call customers, leads, and to streamline operations' and pathway/tool/memory docs show rich task delegation to the built-in agent. Missing for 10: independent/hands-on evidence of real-world task delegation outcomes beyond vendor docs.

   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [claimed-docs] “Memory lets Bland agents remember people across conversations, including calls, SMS, and other channels, so each interaction feels continuou…”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

4. ai-native userOperate the product with natural-language commands

   weight 2 · round to Bland
   Retell AI✓fullprobed7/10

   Retell supports natural-language operation via Conductor's 'Generate from prompt' feature that drafts an agent from a plain-English description, and via its official MCP server which exposes the full API as agent-callable tools so AI-native clients (Cursor, Claude Desktop, Claude Code) can build/manage agents through natural-language MCP tool calls — confirmed live by a keyless JSON-RPC probe against the hosted MCP endpoint. missing for 10: independent/hands-on evaluation of Conductor's prompt-to-agent quality, and no evidence of NL commands for other everyday operations (e.g., dashboard chat-based control) beyond agent creation/management.

   • [claimed-docs] “click **Generate from prompt** (marked Suggested) to let [Conductor](/conductor/create-agent) draft an agent from a plain-English descriptio…”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”
   • [claimed-docs] “Retell supports the Model Context Protocol (MCP) so you can build Retell AI voice agents directly from MCP-capable clients (Cursor, Claude D…”
   • [claimed-docs] “Retell supports the [Model Context Protocol (MCP)] so you can build Retell AI voice agents directly from MCP-capable clients (Cursor, Claude…”
   • [claimed-docs] “Agents: create, update, publish, list, and fetch agent versions.”
   • [probe] “official MCP server documented at https://docs.retellai.com/get-started/mcp-server”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland offers an official MCP server that lets AI agents operate the entire account (calls, pathways, agents, analytics, evals) via natural language, plus an official CLI, both confirmed live via runtime probes. missing for 10: independent/hands-on third-party review of the MCP/CLI natural-language experience beyond vendor docs and probes.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round drawn
   Retell AI—none0/10

   Evidence shows Retell has API reference doc pages (e.g., api-references/clone-voice.md) and SDKs/CLI/MCP tooling, but no evidence of an interactive, runnable API reference (e.g., embedded 'try it' console or Swagger UI). The probe explicitly found no OpenAPI/Swagger spec exposed (all candidate paths 404), undermining any claim of an interactive reference.

   • [claimed-docs] “Clone a voice from audio files”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.retellai.com/openapi.json, https://docs.retellai.com/swagger.json, https://docs.retella…”

   proof ↗⚑ flag

   Bland—none0/10

   Docs reference an API reference for pathways but no evidence of an interactive playground with runnable examples; a probe for OpenAPI/Swagger specs at standard paths returned 404s, suggesting no such interactive reference exists.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   Retell AI—none0/10

   A direct probe for standard OpenAPI/Swagger spec locations (openapi.json, swagger.json, etc.) on Retell's docs domain returned 404 for all candidates, and no evidence pack item shows a downloadable, machine-readable API spec being published or linked from docs. The CLI's version string mentions an internal 'OpenAPI 3.0.0' schema, but this is not shown to be an artifact users can download or fetch programmatically.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.retellai.com/openapi.json, https://docs.retellai.com/swagger.json, https://docs.retella…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”

   proof ↗⚑ flag

   Bland—none0/10

   A direct probe found no OpenAPI/Swagger spec at any expected location (all 404s), and no evidence pack item shows a downloadable machine-readable API spec despite extensive API documentation.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Bland
   Retell AI~partialclaimed4/10

   Retell's quick-start lets you click 'Test' to run a free web call with your agent before it ever touches a phone number or payment method, and new accounts get $10 trial credit to experiment risk-free — a lightweight way to try an agent without production telephony traffic. However, there's no documented dedicated sandbox environment, test-mode API flag, or explicit data isolation guarantee separating test calls from production records/analytics. Missing for 10: explicit sandbox/staging environment concept, test vs prod data isolation guarantees, and independent confirmation that test-call data doesn't mix with production analytics/QA.

   • [claimed-docs] “Click the "Test" button to start a web call with your agent... This step is free and doesn't need a phone number or payment method.”
   • [claimed-docs] “Click the "Test" button to start a web call with your agent”
   • [claimed-docs] “New accounts start with \$10 in free trial credits, no payment method required.”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland provides draft/staging pathway editing where live production calls are unaffected, plus a testbed for isolated node testing, canary deployments, and staged version adoption — all functioning as sandbox-like mechanisms distinct from production. However, there's no explicit 'sandbox environment' or dedicated test account/data isolation concept described, and testing still appears to involve real calls/production infrastructure rather than a fully isolated non-production environment. missing for 10: a dedicated sandbox/test-mode account distinct from production billing and phone infrastructure, explicit documentation of synthetic/non-production test data, and independent confirmation that testbed/draft testing never touches real production call data or costs.

   • [claimed-docs] “You can also promote a version to staging to test it before it goes live, or send an individual call against a specific”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “This is how you A/B test a new agent release against your live production version, with real calls, before committing to a full rollout.”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round to Bland
   Retell AI—none0/10

   The evidence pack covers SDKs, CLI, MCP server, and API endpoints extensively, but there is no documentation of API versioning scheme or a deprecation policy for breaking changes. The probe notes an OpenAPI version string (3.0.0, catalog v4) but this is not evidence of a documented deprecation/versioning policy for API consumers.

   ⚑ flag

   Bland~partialprobed4/10

   Bland documents infrastructure versioning concepts (staged/canary rollouts, choosing when to adopt a new release, draft vs. production pathway versions) but there is no evidence of a documented API versioning scheme (e.g., v1/v2 endpoints) or an explicit deprecation policy for its APIs, and the OpenAPI spec probe returned 404s. missing for 10: documented API version numbering/endpoints, an explicit deprecation/sunset policy for APIs, published OpenAPI spec, and independent confirmation of versioning practices.

   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Bland
   Retell AI~partialprobed4/10

   Retell exposes a full-coverage SDK, CLI, and MCP server that let an AI agent script repeated single-item operations (create/update/list agents, phone numbers, knowledge bases) programmatically, enabling scripted bulk-like loops, but there is no documented native batch/bulk endpoint operating on many items in a single call. missing for 10: explicit batch/bulk API endpoints or CLI commands operating on multiple items in one call, evidence of rate-limit-safe bulk workflows, and hands-on confirmation of bulk usage at scale

   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”
   • [claimed-docs] “Agents: create, update, publish, list, and fetch agent versions.”

   proof ↗⚑ flag

   Bland✓fullprobed7/10

   Batch calls let users upload a CSV of recipients to initiate high-volume call campaigns, directly enabling bulk operations across many items, and the CLI/MCP server extend programmatic/bulk management of pathways, agents, and calls. Missing for 10: independent/hands-on verification of batch call performance at scale, and documentation of bulk operations beyond calls (e.g., bulk pathway or number management).

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Bland
   Retell AI~partialclaimed6/10

   Retell supports event-driven automation via webhooks that fire on account events (retell-docs-12, retell-docs-24), function calling that lets agents automatically transfer calls, end calls, book appointments, or call external APIs based on conversation logic (retell-docs-5, retell-docs-35), and node-based flows with conditional branching (retell-docs-25) plus AI QA rules evaluated against configured metrics (retell-docs-43). However, this is scattered across call-flow logic and webhook notifications rather than a unified 'if event then action' rule-definition interface. Missing for 10: a dedicated rules/trigger engine UI for arbitrary account-wide events, documentation of webhook-to-action automation chains, and independent verification that rule-based automation works reliably (community reports note conversational logic errors, e.g. retell-comm-1, retell-comm-3, retell-comm-7).

   • [claimed-docs] “Function calling lets Retell single or multi-prompt agents take real actions — transfer calls, end calls, book appointments, send SMS, and c…”
   • [claimed-docs] “Webhooks allow your application to receive real-time notifications about events that occur in your Retell AI account.”
   • [claimed-docs] “webhooks push data to your application as events happen, making your integrations more efficient and responsive.”
   • [claimed-docs] “Drag-and-drop, node-based flows for structured, high-stakes calls.”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”

   proof ↗⚑ flag

   Bland✓fullclaimed7/10

   Bland's pathways let users define conditional logic that executes webhooks/API calls at specific conversation nodes, and post-call webhooks automatically fire HTTP notifications when a call-completion event occurs — this is a documented rules-trigger-action-on-event mechanism. missing for 10: independent/hands-on verification of the webhook triggering in production, and evidence of event types beyond call-based ones (e.g., generic account-level event automation).

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   Retell AI—none0/10

   Retell's docs cover webhooks, function calling, MCP tool-calling, and telephony integration, but nothing describes scheduling recurring jobs, campaigns, or workflows (e.g., cron-like recurring outbound call batches or repeating automations). This is a fair question for a voice-agent platform (buyers often want scheduled/recurring outbound campaigns), so the axis applies, but no evidence in the pack shows this capability. missing for 10: any mention of a scheduler, recurring campaign/job feature, or cron-style automation trigger.

   ⚑ flag

   Bland—none0/10

   Bland's docs cover batch calls, pathways, webhooks, and API integrations, but nothing describes scheduling recurring jobs or workflows (e.g., cron-like triggers for calls or campaigns) — batch calls are one-off CSV uploads, not recurring schedules.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”

   proof ↗⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round to Bland
   Retell AI~partialclaimed4/10

   Retell supports 'agent versions' with create/update/publish/list/fetch operations via CLI and MCP (retell-docs-47), implying some versioning and possibly rollback via republishing an older version, but there is no documented review workflow (diffs, approvals, changelogs) or explicit rollback command/UI. missing for 10: explicit rollback mechanism, version diff/review UI, changelog or audit trail, and any independent/hands-on confirmation that version history and rollback actually work as described.

   • [claimed-docs] “Agents: create, update, publish, list, and fetch agent versions.”
   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland pathways support draft/staging/production versioning, A/B testing new releases against live production, and canary/staged rollout with adoption control (bland-docs-2, bland-docs-14, bland-docs-19, bland-docs-21, bland-docs-29, bland-docs-31), plus a testbed and evals for reviewing behavior before shipping (bland-docs-7, bland-docs-8, bland-docs-22, bland-docs-28). However, there is no explicit documentation of an automated 'rollback' mechanism to revert a live pathway/release to a prior version — only forward-adoption and canary controls are described. Missing for 10: explicit rollback/revert-to-previous-version capability, version history/diff view, and independent confirmation of rollback in practice.

   • [claimed-docs] “You can also promote a version to staging to test it before it goes live, or send an individual call against a specific”
   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “This is how you A/B test a new agent release against your live production version, with real calls, before committing to a full rollout.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”

   proof ↗⚑ flag

Compliance

1. founderMeet call-recording consent and disclosure obligations with per-call recording controls and configurable data retention

   weight 2 · round to Retell AI
   Retell AI~partialclaimed5/10

   Retell documents per-agent configurable data retention that auto-deletes transcripts, recordings, and logs after a set period, directly addressing retention obligations. However, there is no evidence of explicit per-call recording enable/disable controls or built-in consent-disclosure features (e.g., automated recording announcements) that founders would need for two-party consent compliance. Missing for 10: per-call recording on/off toggle, consent-disclosure/announcement mechanism, and any documentation tying these to legal consent requirements.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of call recording consent/disclosure features, per-call recording toggles, or configurable data retention policies anywhere in Bland's docs; while this is a fair compliance axis for a voice AI platform, nothing in the pack substantiates it.

   ⚑ flag

2. platform-engineerRun regulated workloads with HIPAA/BAA support, SOC 2, and data-residency options

   weight 2 · round drawn
   Retell AI—none0/10

   Evidence shows only generic data-retention controls (docs-15/27/44) but no mention of HIPAA, BAA, SOC 2 certification, or data-residency options anywhere in the docs or community pack, despite this being a fair compliance axis for a voice-AI platform handling call recordings/transcripts.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of HIPAA, BAA, SOC 2 certification, or data-residency options anywhere in the docs or probes; only a generic tagline calling Bland an 'enterprise' platform for 'regulated' workflows without specifics. Missing for 10: HIPAA/BAA documentation, SOC 2 report or certification evidence, data-residency/region controls, and any compliance attestations.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.bland.ai/llms.txt # Bland Documentation Bland is an enterprise voice AI platform for high-volume, …”

   proof ↗⚑ flag

Scale

1. platform-engineerSee documented concurrency limits and scale to many simultaneous calls without manual capacity begging

   weight 2 · round to Retell AI
   Retell AI~partialclaimed5/10

   Retell documents a concurrency configuration feature (reserved inbound concurrency vs. outbound limit) showing platform-level concurrency controls exist, but there is no documented default/numeric concurrency limits per plan, no described self-service process to raise limits without contacting support, and no evidence (docs or community) confirming scaling to many simultaneous calls happens without manual requests. missing for 10: published default concurrency limits, self-service limit-increase mechanism, and independent confirmation of large-scale concurrent call handling without support intervention.

   • [claimed-docs] “Reserved inbound concurrency protects inbound calls from being crowded out by outbound traffic.”
   • [claimed-docs] “When `reserved_inbound_concurrency` is configured, outbound calls can use at most your concurrency limit minus the reserved amount.”

   proof ↗⚑ flag

   Bland~partialclaimed3/10

   Docs mention batch calling for high-volume campaigns and enterprise infrastructure controls (canary releases, staged rollout), implying some capacity for scale, but there is no documented concurrency limit, rate ceiling, or auto-scaling guarantee, and no evidence that capacity increases don't require contacting sales/support. Missing for 10: explicit concurrent-call limits, auto-scaling documentation, and evidence that scaling doesn't require manual requests to Bland.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”

   proof ↗⚑ flag

Self host

1. platform-engineerSelf-host the voice agent runtime from open-source code on my own infrastructure

   weight 3 · round drawn
   Retell AI—none0/10

   Retell AI is a hosted SaaS voice-agent platform; all evidence points to cloud-hosted APIs, SDKs, CLI, and MCP server that call Retell's own servers, with no mention of open-source runtime code or self-hosting instructions. There is no evidence a platform-engineer can download and run the voice agent runtime on their own infrastructure.

   • [claimed-docs] “Build your first Retell AI phone agent in 15 minutes: create an account, pick a template, test in the dashboard, deploy to a phone number, a…”
   • [claimed-docs] “Retell starts at $0, pay only for what you use.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is presented throughout as a hosted enterprise SaaS platform (managed infrastructure, release adoption controls, canary deployments on Bland's own containers) with no mention of open-source code or a self-hostable runtime; a community post even shows a user asking for an open-source alternative because Bland itself isn't one.

   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

Latency

1. platform-engineerSee documented end-to-end voice latency numbers or tuning guidance backing the platform's speed claims

   weight 3 · round to Retell AI
   Retell AI~partialclaimed5/10

   Retell docs offer tuning guidance for latency (transcription mode trade-offs, endpointing settings) and let customers monitor latency via analytics dashboards and AI QA scoring, but there are no documented end-to-end latency benchmarks or numbers substantiating speed claims. missing for 10: published end-to-end latency figures/benchmarks, and independent verification of speed claims.

   • [claimed-docs] “Pick the right Retell transcription mode to balance accuracy against latency — interim results for speed or context-aware results for higher…”
   • [claimed-docs] “Real time transcription is often a trade off between latency and accuracy.”
   • [claimed-docs] “optimize for speed: uses the latest interim results with a low endpointing setting... optimize for accuracy: uses the results with a higher …”
   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”
   • [claimed-docs] “The Analytics dashboard charts your call and chat data so you can see how your agents are performing over time.”
   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack items mention latency numbers, benchmarks, or tuning guidance for end-to-end voice response time; documentation covers pathways, SIP, MCP, CLI, and enterprise features but nothing about speed/latency metrics or optimization guidance.

   ⚑ flag

Turn taking

1. developerRely on the agent to handle interruptions (barge-in) gracefully — stopping speech, updating context, and recovering the turn

   weight 3 · round drawn
   Retell AI—none0/10

   The evidence pack covers transcription latency/accuracy trade-offs (retell-docs-9, retell-docs-39, retell-docs-48) but nowhere describes explicit barge-in/interruption handling — stopping agent speech, updating context, and recovering the turn when a caller interrupts. Community threads show turn-taking confusion (contradictions, loops, disconnects) but do not address interruption handling specifically. Missing for full/partial credit: any documentation or hands-on report confirming barge-in detection, speech-stop behavior, or turn recovery mechanics.

   ⚑ flag

   Bland—none0/10

   The evidence pack covers pathways, webhooks, batch calls, evals, MCP/CLI integrations, and infrastructure features, but contains no mention of barge-in, interruption handling, stopping TTS mid-utterance, or turn recovery logic — a core voice-agent capability that would be a fair and expected axis for this product type. missing for 10: any documentation or claim about detecting user interruptions, halting agent speech, updating context after a barge-in, and resuming/recovering the conversational turn.

   ⚑ flag

2. developerEnable noise suppression or audio filtering so the agent stays coherent on noisy real-world calls

   weight 1 · round drawn
   Retell AI—none0/10

   No evidence pack item mentions noise suppression, background-noise filtering, or audio-preprocessing features for handling noisy real-world call environments; the closest related feature (transcription-mode trade-off between latency and accuracy) addresses turn-taking speed, not noise robustness. This is a plausible axis for a voice-agent platform, so absence of evidence yields 'none' rather than 'na'.

   ⚑ flag

   Bland—none0/10

   No evidence pack item mentions noise suppression, audio filtering, or handling of noisy real-world call environments; documentation covers pathways, integrations, MCP, CLI, SIP, and enterprise release features but nothing about audio quality/noise handling.

   ⚑ flag

3. developerUse model-based end-of-turn detection beyond simple VAD silence timeouts so the agent doesn't talk over slow speakers

   weight 2 · round drawn
   Retell AI—none0/10

   The transcription-mode docs only describe tuning an 'endpointing setting' (low vs high) as a speed/accuracy tradeoff for VAD-style silence detection, not a distinct model-based semantic end-of-turn detector that understands incomplete utterances from slow speakers. No evidence pack item describes a dedicated turn-detection model or algorithm beyond adjusting silence-timeout thresholds.

   • [claimed-docs] “Pick the right Retell transcription mode to balance accuracy against latency — interim results for speed or context-aware results for higher…”
   • [claimed-docs] “Real time transcription is often a trade off between latency and accuracy.”
   • [claimed-docs] “optimize for speed: uses the latest interim results with a low endpointing setting... optimize for accuracy: uses the results with a higher …”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of turn-taking, end-of-turn detection, VAD, or interruption handling mechanisms of any kind — nothing addresses how Bland decides when a speaker has finished talking. This is a fair and applicable axis for a voice AI platform, but no capability is documented; missing for 10: any mention of end-of-turn/turn-taking model, VAD configuration, or handling of slow speakers/pauses.

   ⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Retell AI
   Retell AI✓fullprobed8/10

   Retell exposes full API coverage via official Node/Python SDKs, a CLI, and a hosted MCP server that dynamically exposes the entire API (list/get/invoke endpoint tools), confirmed by keyless runtime probes actually reaching the CLI and MCP handshake — meaning nearly anything doable in the dashboard (agents, phone numbers, knowledge bases, function calling, voice cloning, analytics) is API/CLI/MCP accessible. missing for 10: no public OpenAPI spec was found (404s across candidate paths), and no independent hands-on confirmation that every single UI feature (e.g., analytics dashboards, AI QA config) has a 1:1 API equivalent.

   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Use Retell's MCP server to build and manage voice agents from MCP-capable clients like Cursor, Claude Desktop, and Claude Code via Retell AP…”
   • [claimed-docs] “Retell supports the Model Context Protocol (MCP) so you can build Retell AI voice agents directly from MCP-capable clients (Cursor, Claude D…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.retellai.com/openapi.json, https://docs.retellai.com/swagger.json, https://docs.retella…”
   • [claimed-docs] “Agents: create, update, publish, list, and fetch agent versions.”

   proof ↗⚑ flag

   Bland✓fullprobed7/10

   Bland's docs show extensive programmatic control mirroring UI features: pathway creation/versioning, testbed, evals, batch calls, webhooks, SIP/phone number management, and a CLI/MCP server that explicitly lets users 'manage your entire Bland account from the terminal' and perform the same actions (calls, pathways, agents, analytics) as the UI. This breadth strongly supports API parity, though there's no explicit first-party statement guaranteeing 100% feature parity and no discoverable OpenAPI spec (all candidate URLs 404), so full parity isn't independently confirmed. Missing for 10: an explicit parity guarantee/documentation and a machine-readable OpenAPI spec, plus independent hands-on confirmation that every UI action has an API equivalent.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round drawn
   Retell AI—none0/10

   Docs describe API/SDK/CLI access, data retention (auto-deletion) and analytics dashboards, but nowhere document a data-export or portability feature that would let a user pull all call/chat data, transcripts, and recordings in an open format to leave the platform; retention docs only cover automatic deletion, not export.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence of a data export feature or open-format export of account data (pathways, call logs, memories, etc.); the docs cover CLI, MCP, and SDK integrations but nothing about exporting user data for portability/exit.

   ⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round drawn
   Retell AI—none0/10

   Retell AI is a closed, proprietary SaaS platform; the evidence pack shows SDKs, CLI, MCP server and docs but no mention of source code being published under any open license, nor any GitHub repo for the core platform. Missing for 10: any open-source license grant, public source repository, or licensing terms for the core agent/voice engine.

   ⚑ flag

   Bland—none0/10

   Bland is a closed, commercial SaaS voice AI platform; there is no evidence of any open-source license or public source code repository. A community comment explicitly asks for an open-source alternative, implying Bland itself is not open source. This is an applicable axis (a product could publish open-source components) but no evidence supports it.

   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round drawn
   Retell AI—none0/10

   Retell AI is presented entirely as a hosted SaaS (cloud dashboard, hosted APIs, hosted MCP server, usage-based pricing) with no mention anywhere in docs of a self-hosted or on-prem deployment option; all evidence points to a fully managed cloud product.

   • [claimed-docs] “Retell starts at $0, pay only for what you use.”
   • [claimed-docs] “Build your first Retell AI phone agent in 15 minutes: create an account, pick a template, test in the dashboard, deploy to a phone number, a…”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”
   • [claimed-docs] “Integrate Retell voice agents with your own telephony provider using elastic SIP trunking or imported numbers from Twilio, Telnyx, and Vonag…”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is documented as a hosted enterprise voice AI platform (call dispatch, pathways, SIP, MCP, CLI) with no mention of an on-prem/self-hosted deployment option; 'enterprise' release controls (docs-14, docs-29, docs-31) only govern version adoption timing on Bland's own infrastructure, not customer self-hosting. A community post explicitly looks for an open-source self-hostable alternative because Bland itself doesn't offer this.

   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”
   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

Pricing

1. founderSee published per-minute or usage pricing and estimate cost per call before committing

   weight 2 · round to Retell AI
   Retell AI✓fullcommunity8/10

   Retell's pricing page explicitly states pay-as-you-go pricing starting at $0 with no annual contracts, offers an interactive calculator to estimate approximate monthly cost by adjusting key parameters, and new accounts get $10 free trial credit to test before paying — directly enabling a founder to estimate call costs before committing. Community discussion also confirms a concrete per-minute rate (17¢/min) is being charged in practice, corroborating usage-based billing exists. missing for 10: the per-minute rate itself is not shown in first-party docs (only inferred from a community comment), and there's no independent verification of the calculator's accuracy.

   • [claimed-docs] “Retell starts at $0, pay only for what you use.”
   • [claimed-docs] “Adjust key parameters to see an approximate monthly cost for your Retell AI voice agents.”
   • [claimed-docs] “Most voice AI platforms require annual contracts before you write a single line of code. Retell starts at $0, pay only for what you use.”
   • [claimed-docs] “New accounts start with \$10 in free trial credits, no payment method required.”
   • [community] “Founder (yanyan_evie) noted voice provider costs differ: 'The different providers have different prices. openai tts & deepgram are cheaper, …”
   • [community] “A commenter noted per-minute pricing (17¢/min) could be exploited: a malicious caller could keep the bot on the line for hours across many p…”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack item shows published per-minute or usage pricing, a pricing page, or any cost calculator; the only pricing-adjacent mention is a community complaint that Bland 'gets expensive' with no figures. Missing for 10: published price list, per-minute rate documentation, cost calculator or estimator tool.

   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Retell AI—none0/10

   No evidence in the pack mentions data residency, region selection, or geographic storage options; the closest related feature is per-agent data retention/deletion (retell-docs-15/27/44), which addresses retention duration, not location of storage. This axis is applicable to a cloud AI voice platform handling call/chat data, so absence of evidence yields 'none'. Missing for 10: any documentation of selectable regions, data residency guarantees, or geo-specific hosting.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence in the pack mentions data residency, regional hosting options, or geographic storage controls for Bland; enterprise/infra docs discuss release versioning and SIP/canary deployments but not region selection. Missing for 10: any mention of data residency options, region-specific hosting, or compliance-driven storage location controls.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Retell AI—none0/10

   The evidence only covers configurable data retention (auto-deleting transcripts/recordings/logs after a set period) for compliance, not an explicit opt-out or guarantee against using customer data for AI model training. No docs mention training-data usage policy, opt-out toggles, or contractual no-train clauses.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence in the pack addresses data-training opt-out or AI-training privacy policies for Bland; nothing about training-data usage or opt-out controls is mentioned across any of the docs or probes.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to Retell AI
   Retell AI!disputedcontradicted5/10

   Retell's docs describe per-agent configurable data retention with automatic, permanent deletion of call/chat transcripts, recordings, and logs after a set period (retell-docs-15, retell-docs-27, retell-docs-44), which is solid first-party evidence for the retention-control axis. However, an independent community report describes the company refusing to delete a user's stored data (credit card) on request, forcing the user to escalate to bank disputes/consumer-affairs complaints — a concrete real-world case where deletion did not work as a customer expected (retell-comm-14). Missing for 10: independent verification that call/chat data deletion itself (not just billing data) works as documented, and no public response/resolution to the deletion complaint.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”
   • [community] “Thread titled 'Retellai won't delete my credit card' - a user complained the company would not delete their stored credit card; commenters s…”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack items address data retention policies, data deletion controls, or privacy/compliance settings for call recordings, transcripts, or memory data. This axis clearly applies to an enterprise voice AI platform handling call data, but nothing in the evidence documents retention periods, deletion APIs, or GDPR/CCPA-style data controls. Missing for 10: retention policy docs, data deletion API/endpoint, compliance certifications, memory/data purge mechanism.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Retell AI—none0/10

   The evidence pack covers data retention for call/chat data (transcripts, recordings, logs) but contains no mention of telemetry or usage-tracking opt-out controls for the product itself (e.g., CLI/SDK/dashboard analytics collection). Data retention (retell-docs-15/27/44) addresses deletion of customer call data, not opting out of Retell's own telemetry collection.

   ⚑ flag

   Bland—none0/10

   No evidence pack items mention telemetry, analytics opt-out, or usage-tracking controls for Bland's own platform; this is a fair question for an enterprise SaaS product but is simply unaddressed in the evidence.

   ⚑ flag

Call control

1. developerEscalate a live call to a human with warm or blind transfer, passing context along

   weight 2 · round to Retell AI
   Retell AI~partialclaimed4/10

   Retell's function-calling docs list 'transfer calls' as a built-in agent action alongside ending calls, booking appointments, etc., which supports the general concept of escalating a call to a human. However, the evidence never distinguishes warm vs. blind transfer modes nor describes passing conversational context/metadata to the receiving human agent. missing for 10: explicit warm-transfer vs blind-transfer configuration, evidence of context/data hand-off to the human agent, and any hands-on confirmation of this feature working in practice.

   • [claimed-docs] “Function calling lets Retell single or multi-prompt agents take real actions — transfer calls, end calls, book appointments, send SMS, and c…”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack covers pathways, webhooks, SIP, MCP, CLI, and other Bland features, but contains no mention of call transfer (warm or blind) or handing off a live call to a human agent with context. This is a standard telephony capability that could plausibly be documented, so absence of evidence yields 'none' rather than 'na'.

   ⚑ flag

2. developerMy agent can send DTMF keypresses, navigate IVR menus, and detect or leave voicemail

   weight 1 · round drawn
   Retell AI—none0/10

   The evidence pack covers function calling, custom telephony/SIP trunking, transcription, and MCP tool integration, but nowhere mentions DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving capabilities. This is a fair capability question for a telephony voice-agent platform, but no evidence confirms it is supported.

   ⚑ flag

   Bland—none0/10

   The evidence pack covers pathways, SIP, webhooks, evals, and MCP/CLI tooling, but nowhere mentions DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving — capabilities that are plausible for a voice telephony platform but are simply undocumented here.

   ⚑ flag

Campaigns

1. founderRun batch outbound call campaigns with scheduling and throughput controls

   weight 2 · round to Bland
   Retell AI—none0/10

   Docs describe outbound vs inbound concurrency reservation (retell-docs-11, retell-docs-41) and custom telephony/SIP integration, but there is no evidence of a batch outbound campaign feature — no mention of scheduling calls, uploading contact lists, or campaign-level throughput controls beyond general concurrency limits. Axis is plausible for a voice-agent platform but unsupported by the evidence pack.

   • [claimed-docs] “Reserved inbound concurrency protects inbound calls from being crowded out by outbound traffic.”
   • [claimed-docs] “When `reserved_inbound_concurrency` is configured, outbound calls can use at most your concurrency limit minus the reserved amount.”
   • [claimed-docs] “Integrate Retell voice agents with your own telephony provider using elastic SIP trunking or imported numbers from Twilio, Telnyx, and Vonag…”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland's docs explicitly describe batch calls for uploading CSV recipient lists to run high-volume outbound campaigns, backed by scheduling/throughput-related infrastructure like SIP integration, phone number management, and analytics/evals to monitor campaign performance. Missing for 10: explicit documentation of scheduling controls (e.g., call windows/timing) and rate-limiting/throughput knobs specifically, plus independent hands-on verification of batch campaign behavior at scale.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”

   proof ↗⚑ flag

Numbers

1. developerProvision phone numbers and run both inbound and outbound calls through the platform's API

   weight 3 · round drawn
   Retell AI✓fullprobed8/10

   Retell's docs show phone-number provisioning via native assignment (docs-32) or importing/SIP-trunking your own numbers from Twilio/Telnyx/Vonage (docs-10,40,49), and both inbound and outbound calling are explicitly supported and distinguished (reserved inbound concurrency vs. outbound traffic in docs-11/41). Full API/SDK coverage for voice endpoints (docs-2,33,46) and a CLI to manage phone numbers (docs-3,21) round out programmatic control, with a working keyless CLI/MCP probe corroborating API-level access (retell-probe-rt-1/2). Missing for 10: explicit documentation of a native 'buy a new number' provisioning endpoint (as opposed to importing existing numbers) and a concrete outbound-call code example.

   • [claimed-docs] “Build your first Retell AI phone agent in 15 minutes: create an account, pick a template, test in the dashboard, deploy to a phone number, a…”
   • [claimed-docs] “Integrate Retell voice agents with your own telephony provider using elastic SIP trunking or imported numbers from Twilio, Telnyx, and Vonag…”
   • [claimed-docs] “Reserved inbound concurrency protects inbound calls from being crowded out by outbound traffic.”
   • [claimed-docs] “This guide shows how to integrate Retell agents with your telephony provider and use your own numbers.”
   • [claimed-docs] “When `reserved_inbound_concurrency` is configured, outbound calls can use at most your concurrency limit minus the reserved amount.”
   • [claimed-docs] “You set up a SIP trunk, configure your number to point to it, and import that number to Retell.”
   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [claimed-docs] “Install the Retell CLI to manage agents, phone numbers, knowledge bases, and other Retell resources from your terminal with simple commands.”
   • [claimed-docs] “The Retell CLI lets you manage Retell resources from your terminal.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @retell-ai/retell-cli --version` printed `retell 0.13.0 (OpenAPI 3.0.0, catalog v4)` keylessly …”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland's docs explicitly document creating/managing inbound phone numbers, outbound call dispatch via API, bringing your own Twilio numbers, and SIP for both inbound/outbound, all programmatically accessible, plus a CLI/MCP that manage phone numbers and calls end-to-end. Missing for 10: no explicit REST API reference/OpenAPI spec confirmed (probe shows openapi.json 404s) and no independent hands-on developer report of a full provision+call round trip.

   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Create inbound phone numbers for customer support, etc.”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”
   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

Sip

1. platform-engineerConnect my own carrier or PBX via SIP trunking (or import Twilio/Telnyx numbers) instead of being locked to bundled telephony

   weight 2 · round drawn
   Retell AI✓fullclaimed8/10

   Official docs explicitly describe elastic SIP trunking and importing numbers from Twilio, Telnyx, and Vonage, walking through setting up a SIP trunk and pointing/importing numbers into Retell — directly matching the story. This is first-party documentation without independent hands-on corroboration of the SIP flow itself. Missing for 10: independent/community verification of a real SIP trunk setup working end-to-end, and detail on carrier-specific edge cases (codecs, failover, latency).

   • [claimed-docs] “Integrate Retell voice agents with your own telephony provider using elastic SIP trunking or imported numbers from Twilio, Telnyx, and Vonag…”
   • [claimed-docs] “This guide shows how to integrate Retell agents with your telephony provider and use your own numbers.”
   • [claimed-docs] “You set up a SIP trunk, configure your number to point to it, and import that number to Retell.”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland's docs explicitly document inbound/outbound SIP trunking to connect a customer's own carrier or PBX, plus number porting, and separately support connecting an existing Twilio account/numbers. Missing for 10: no mention of Telnyx import specifically and no independent/hands-on validation of SIP setup success.

   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”

   proof ↗⚑ flag

Analytics

1. ai-native userThe platform's AI reviews my calls for me — scoring quality, flagging failures, and analyzing resolution automatically

   weight 2 · round to Bland
   Retell AI~partialclaimed6/10

   Retell's AI QA feature automatically scores sampled calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage, and the Analytics dashboard tracks success rate and other metrics, directly matching the story's core ask of automated call scoring and quality flagging. However, evidence shows AI QA only evaluates a 'sampled set' of calls (not all calls) against user-configured rules, and there's no documented automatic 'resolution analysis' narrative beyond metrics/dashboards, nor independent hands-on validation of AI QA accuracy. missing for 10: evidence of full (non-sampled) coverage, independent/third-party validation of AI QA scoring accuracy, and detail on automated resolution/failure-cause analysis beyond configured metrics.

   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”
   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”
   • [claimed-docs] “The Analytics dashboard charts your call and chat data so you can see how your agents are performing over time.”
   • [claimed-docs] “Built-in call success and sentiment scoring, plus custom fields synced to your CRM.”

   proof ↗⚑ flag

   Bland~partialclaimed7/10

   Bland's Evals feature explicitly lets users define LLM-judge agents that grade calls on custom dimensions (quality, resolution, etc.), and the Testbed lets you replay and analyze specific call nodes to spot failures — directly matching automated call review/scoring. However, this requires the user to configure eval criteria rather than being a fully out-of-the-box automatic analysis, and there's no evidence of a pre-built default 'failure flagging' report. Missing for 10: evidence of fully automatic, no-setup call scoring/dashboard, and independent/hands-on validation of eval accuracy.

   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”

   proof ↗⚑ flag

2. founderSee call analytics — success rates, durations, outcomes, sentiment — in dashboards without building my own

   weight 2 · round to Retell AI
   Retell AI✓fullclaimed8/10

   Retell docs describe a built-in Analytics dashboard with charts, filters, and breakdowns for success rate, latency, cost, and concurrency, plus AI QA scoring for sentiment, hallucination, and tool usage—directly matching the founder's need for out-of-box call analytics. Missing for 10: independent/hands-on confirmation of dashboard usability and no evidence of exportable reports or deeper outcome breakdowns beyond what's documented.

   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”
   • [claimed-docs] “The Analytics dashboard charts your call and chat data so you can see how your agents are performing over time.”
   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”
   • [claimed-docs] “Built-in call success and sentiment scoring, plus custom fields synced to your CRM.”

   proof ↗⚑ flag

   Bland~partialprobed4/10

   Evidence confirms Bland has an analytics layer (MCP server can 'query analytics') and quality-grading tools like Evals (LLM judges scoring call dimensions) and a testbed for reviewing call interactions, implying some built-in metrics exist. However, there is no direct evidence of an actual dashboard UI showing success rates, call durations, outcomes, or sentiment trends over time — analytics access shown is via MCP/API query rather than a visual dashboard. Missing for 10: screenshots or docs of a native analytics dashboard, explicit mention of success-rate/duration/sentiment metrics, and independent confirmation the dashboard requires no custom building.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”

   proof ↗⚑ flag

Monitoring

1. platform-engineerMonitor live calls in production and get alerts when agents misbehave or error rates spike

   weight 1 · round to Retell AI
   Retell AI~partialclaimed6/10

   Retell provides real building blocks for production monitoring — a customizable analytics dashboard tracking success rate, latency, cost and concurrency (retell-docs-13/42), AI QA that scores calls for hallucination, sentiment and tool-usage issues (retell-docs-14/43), and webhooks that push real-time event notifications (retell-docs-12/24) which a platform engineer could wire into an alerting pipeline. However there is no documented native alerting/threshold system (e.g., automatic notification when error rates spike or an agent misbehaves) or a live in-call monitoring view — engineers must build that themselves on top of webhooks/dashboard APIs. Missing for 10: built-in threshold-based alerts or anomaly detection, a real-time 'in-progress calls' monitoring view, and any independent evidence that alerting/monitoring works reliably in production.

   • [claimed-docs] “Webhooks allow your application to receive real-time notifications about events that occur in your Retell AI account.”
   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”
   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “webhooks push data to your application as events happen, making your integrations more efficient and responsive.”
   • [claimed-docs] “The Analytics dashboard charts your call and chat data so you can see how your agents are performing over time.”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland provides post-call webhooks, evals with LLM judges, and an MCP integration that can 'query analytics' — giving some after-the-fact quality/analytics visibility — but there is no documented live-call monitoring dashboard, real-time alerting, or error-rate-spike notification system in the evidence pack. Missing for 10: live/real-time call monitoring UI, configurable alert thresholds, error-rate spike detection/paging, independent confirmation these exist in production.

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”

   proof ↗⚑ flag

Testing

1. developerTest agents with simulated conversations or evals before putting them on real phone calls

   weight 2 · round to Bland
   Retell AI~partialclaimed5/10

   Retell's dashboard has a free 'Test' web-call button that lets developers try an agent before assigning it to a real phone number, and AI QA can score calls on hallucination, accuracy, sentiment, etc., which supports some testing/analytics workflow. However there's no documented feature for automated simulated-conversation test suites or eval scripts run pre-deployment — AI QA appears to operate on sampled real calls rather than synthetic scripted evals. Missing for 10: dedicated simulation/eval framework for scripted test conversations, batch eval tooling, and any independent verification that pre-call testing catches issues before production use.

   • [claimed-docs] “Click the "Test" button to start a web call with your agent... This step is free and doesn't need a phone number or payment method.”
   • [claimed-docs] “Click the "Test" button to start a web call with your agent”
   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”
   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland provides explicit pre-production testing tools: Evals (LLM-judge grading of call quality), the Testbed (replay/edit/re-run node interactions on historical or test chats), staging promotion and draft-vs-production pathway separation, and canary/A/B rollout testing against real calls before full deployment. Together these let a developer simulate conversations and grade agent behavior before real phone calls go live. missing for 10: no independent/hands-on evidence of eval accuracy or testbed usage from outside vendor docs, and no explicit description of a pure text-based conversation simulator separate from testbed/staging.

   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “You can also promote a version to staging to test it before it goes live, or send an individual call against a specific”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “This is how you A/B test a new agent release against your live production version, with real calls, before committing to a full rollout.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”

   proof ↗⚑ flag

Post call

1. developerExtract structured data from every call — outcomes, entities, dispositions — delivered via API or webhook after the call

   weight 2 · round to Retell AI
   Retell AI✓fullclaimed7/10

   Retell explicitly supports post-call structured extraction: built-in call success/sentiment scoring plus custom fields synced to CRM, delivered via real-time webhooks and viewable/aggregated in the analytics/AI QA dashboards. This directly covers outcomes (success rate), dispositions (sentiment), and entities (custom fields) delivered via API/webhook as the story requires. Missing for 10: no independent/hands-on confirmation of the specific post-call-analysis JSON schema or webhook payload structure, and no detail on how custom entity fields are defined/configured.

   • [claimed-docs] “Built-in call success and sentiment scoring, plus custom fields synced to your CRM.”
   • [claimed-docs] “Webhooks allow your application to receive real-time notifications about events that occur in your Retell AI account.”
   • [claimed-docs] “webhooks push data to your application as events happen, making your integrations more efficient and responsive.”
   • [claimed-docs] “Build custom Retell analytics dashboards to track call and chat metrics like success rate, latency, cost, and concurrency, with charts, filt…”
   • [claimed-docs] “The Analytics dashboard charts your call and chat data so you can see how your agents are performing over time.”
   • [claimed-docs] “AI QA scores Retell calls on hallucination, knowledge base accuracy, latency, sentiment, and tool usage to surface quality trends and issues…”
   • [claimed-docs] “AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure.”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland documents post-call webhooks that automatically deliver call data to a developer's server after each call, and evals let you grade/classify calls (dispositions) via LLM judges — both align with the API/webhook delivery and outcome-tagging parts of the story. However, there's no explicit documentation of structured entity extraction (e.g., named fields like names, dates, custom entities) as a distinct capability, nor a described webhook payload schema. Missing for 10: explicit entity-extraction feature docs, sample webhook payload showing structured outcome/entity/disposition fields, and independent confirmation of the data delivered.

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

Tools

1. ai-native userMy voice agent can plug in MCP servers as tool sources so one integration grants it whole toolsets mid-call

   weight 2 · round to Retell AI
   Retell AI✓fullclaimed8/10

   Retell documents connecting single/multi-prompt voice agents to remote MCP servers so the agent can call the server's tools mid-call (retell-docs-6/23/36), directly matching the story, and this is distinct from Retell's own client-side MCP server for building agents. missing for 10: independent third-party corroboration of mid-call MCP tool invocation in production (only first-party docs and a probe of the client-facing MCP server, not the agent-as-MCP-client tool-call path, are available).

   • [claimed-docs] “Connect a Retell single- or multi-prompt agent to a remote MCP server so it can call the server's tools during a live voice or chat conversa…”
   • [claimed-docs] “Connect your single- or multi-prompt agent to a remote Model Context Protocol (MCP) server, and the agent can call that server's tools durin…”
   • [claimed-docs] “Connect your single- or multi-prompt agent to a remote [Model Context Protocol (MCP)] server, and the agent can call that server's tools dur…”
   • [claimed-docs] “Function calling lets Retell single or multi-prompt agents take real actions — transfer calls, end calls, book appointments, send SMS, and c…”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”

   proof ↗⚑ flag

   Bland—none0/10

   Bland ships an MCP *server* so coding agents can control the Bland account (docs-11/20/27, probe-3/probe-rt-2), which is the opposite role from what the story asks — the voice agent itself acting as an MCP *client* that plugs in external MCP servers as tool sources mid-call. Tool/function calling is documented only via custom HTTP API integrations (bland-docs-4, bland-docs-25), with no mention of the agent consuming MCP servers as a toolset source during calls.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”

   proof ↗⚑ flag

2. developerMy agent can call external APIs and custom functions mid-conversation and speak the result without awkward dead air

   weight 3 · round drawn
   Retell AI~partialprobed6/10

   Retell's docs clearly document function calling that lets agents call external APIs mid-conversation (retell-docs-5, retell-docs-35) and even connect to remote MCP servers to invoke tools live during a call (retell-docs-6, retell-docs-23, retell-docs-36), confirmed by a keyless runtime probe showing the hosted MCP server exposing invoke_api_endpoint tools (retell-probe-rt-2). However, no evidence specifically addresses the 'no awkward dead air' requirement — there's no documented filler-speech, streaming-response, or latency-masking mechanism specifically tied to function-call execution; transcription-mode docs (retell-docs-9/39/48) discuss general STT latency tradeoffs, not function-call dead-air handling. missing for 10: explicit filler/backchannel speech during function execution, hands-on demonstration of seamless mid-call tool latency, independent confirmation of no dead-air behavior.

   • [claimed-docs] “Function calling lets Retell single or multi-prompt agents take real actions — transfer calls, end calls, book appointments, send SMS, and c…”
   • [claimed-docs] “Function calling transforms your AI agent from a conversational interface into an action-oriented assistant.”
   • [claimed-docs] “Connect a Retell single- or multi-prompt agent to a remote MCP server so it can call the server's tools during a live voice or chat conversa…”
   • [claimed-docs] “Connect your single- or multi-prompt agent to a remote Model Context Protocol (MCP) server, and the agent can call that server's tools durin…”
   • [claimed-docs] “Connect your single- or multi-prompt agent to a remote [Model Context Protocol (MCP)] server, and the agent can call that server's tools dur…”
   • [probe] “PROBE runtime (recorded 2026-09-05): the hosted MCP server at https://mcp.retellai.com completed a full KEYLESS JSON-RPC initialize handshak…”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland's pathway/tools docs confirm agents can call external APIs and execute webhooks mid-conversation (bland-docs-4, bland-docs-25, bland-docs-30), which supports live function calling during a call. However, no evidence describes mechanisms for avoiding dead air (e.g., filler speech, streaming partial responses) while waiting on API results. missing for 10: explicit documentation of latency-masking/filler-speech behavior during API calls, and independent/hands-on confirmation of smooth conversational flow.

   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”

   proof ↗⚑ flag

Recording

1. platform-engineerRetrieve full call recordings and transcripts programmatically for every call

   weight 2 · round to Retell AI
   Retell AI~partialclaimed6/10

   Docs confirm that Retell stores per-call transcripts and recordings (referenced in data-retention docs) and that full voice/chat API endpoint coverage exists via official SDKs, implying programmatic retrieval, but no evidence pack item explicitly documents a 'get call' or 'list calls' API endpoint returning recording URLs/transcript text, nor examples of pulling them via SDK/CLI. Missing for 10: explicit API reference for call/recording/transcript retrieval endpoints, CLI/SDK code samples showing recording download or transcript fetch, and any independent confirmation of this working end-to-end.

   • [claimed-docs] “Configure per-agent data retention to automatically delete call and chat data — transcripts, recordings, and logs — after a set period for c…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent. After the retention period expires, call and chat data associated with tha…”
   • [claimed-docs] “Retell allows you to configure a data retention period per agent.”
   • [claimed-docs] “Official Retell SDKs for Node.js and Python. Typed clients with API key auth, structured errors, and full voice and chat endpoint coverage.”
   • [claimed-docs] “Retell provides official SDKs for Node.js and Python to simplify integration with our platform.”
   • [claimed-docs] “Webhooks allow your application to receive real-time notifications about events that occur in your Retell AI account.”
   • [claimed-docs] “webhooks push data to your application as events happen, making your integrations more efficient and responsive.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack item explicitly documents an API or endpoint for retrieving full call recordings or transcripts programmatically; references to call logs (testbed) and post-call webhooks hint at underlying data but never confirm a recording/transcript retrieval capability.

   ⚑ flag

Transcription

1. developerGet accurate real-time transcription with control over the STT provider, language models, or key terms

   weight 2 · round to Retell AI
   Retell AI~partialcommunity4/10

   Retell docs confirm real-time transcription with a documented latency/accuracy trade-off (interim vs context-aware endpointing), giving developers some control over accuracy tuning, and community notes hint at underlying provider choices (e.g., Deepgram) for voice pipelines. However, there is no documented ability to select or swap STT providers, choose an ASR language model, or configure custom vocabulary/key-term boosting for transcription accuracy. Missing for 10: explicit STT provider selection API, language model choice for transcription, custom vocabulary/key-term boosting support, and independent accuracy benchmarks.

   • [claimed-docs] “Pick the right Retell transcription mode to balance accuracy against latency — interim results for speed or context-aware results for higher…”
   • [claimed-docs] “Real time transcription is often a trade off between latency and accuracy.”
   • [claimed-docs] “optimize for speed: uses the latest interim results with a low endpointing setting... optimize for accuracy: uses the results with a higher …”
   • [community] “Founder (yanyan_evie) noted voice provider costs differ: 'The different providers have different prices. openai tts & deepgram are cheaper, …”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is a phone-call AI platform where transcription accuracy is clearly relevant, but the evidence pack contains no mention of STT provider selection, language model choice for transcription, or key-term/vocabulary boosting features. missing for 10: STT provider selection, transcription accuracy documentation, custom key terms/vocabulary support, language model configuration for transcription.

   ⚑ flag

Voices

1. founderClone a custom brand voice and use it for my agents, with a documented consent process

   weight 2 · round to Retell AI
   Retell AI~partialclaimed5/10

   Retell documents voice cloning from uploaded audio files (up to 25 files) and lets you attach the cloned voice to an agent via the voice selector, covering the core 'clone and use a custom voice' capability. However, none of the evidence describes any documented consent-verification step (e.g., consent recording, rights attestation) as part of the cloning flow. Missing for 10: an explicit consent-collection/verification mechanism in the clone-voice API or dashboard docs, and any compliance guidance tying voice cloning to consent requirements.

   • [claimed-docs] “Add custom voices to your Retell agent — search ElevenLabs community voices, import a voice clone, or train a clone for a unique brand-speci…”
   • [claimed-docs] “Clone a voice from audio files”
   • [claimed-docs] “Audio files to use for voice cloning. Up to 25 files allowed.”
   • [claimed-docs] “You can also add a voice clone by clicking "Add custom voice" in the voice selector.”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland documents voice cloning itself (a ~10-second clean sample sets the quality ceiling) which supports the 'clone a custom brand voice' half of the story, but no evidence describes a documented consent process, verification, or authorization requirement for cloning someone's voice. missing for 10: documented consent/verification workflow for voice cloning, legal/compliance guidance on brand-voice rights, independent confirmation of the cloning feature's fidelity.

   • [claimed-docs] “A clone needs one clean sample of about ten seconds. Quality of the sample sets the ceiling on quality of the voice”

   proof ↗⚑ flag

2. developerChoose from a broad voice library or plug in multiple TTS providers to get the voice I want

   weight 2 · round to Retell AI
   Retell AI✓fullcommunity7/10

   Docs confirm a searchable voice library (ElevenLabs community voices) plus voice cloning options (retell-docs-7, retell-docs-37, retell-docs-38), and a founder community comment independently confirms multiple underlying TTS providers (OpenAI TTS, Deepgram, ElevenLabs) with different pricing tiers (retell-comm-10), showing developers can indeed pick across providers/voices. Missing for 10: a first-party docs page enumerating all supported TTS providers and API-level provider-switching parameters beyond the voice-selector UI, and independent hands-on comparison of voice quality across providers.

   • [claimed-docs] “Add custom voices to your Retell agent — search ElevenLabs community voices, import a voice clone, or train a clone for a unique brand-speci…”
   • [claimed-docs] “You can also add a voice clone by clicking "Add custom voice" in the voice selector.”
   • [claimed-docs] “In the voice selector, you can click "Add custom voice" to search and add publicly available community voices.”
   • [community] “Founder (yanyan_evie) noted voice provider costs differ: 'The different providers have different prices. openai tts & deepgram are cheaper, …”

   proof ↗⚑ flag

   Bland~partialclaimed3/10

   Docs show voice cloning support (a 10-second sample sets voice quality) but there is no evidence of a broad pre-built voice library to choose from, nor any mention of plugging in multiple third-party TTS providers. Missing for 10: documented voice library/catalog, multi-provider TTS integration, and any comparison of voice options.

   • [claimed-docs] “A clone needs one clean sample of about ten seconds. Quality of the sample sets the ceiling on quality of the voice”

   proof ↗⚑ flag