Bland vs telli

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
   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

   telli~partialprobed5/10

   telli's REST API documents endpoints for scheduling calls and importing/attaching phone numbers (telli-docs-17, telli-docs-19), and the MCP server lets AI tools talk to Charlie, who can 'build and refine agents' (telli-docs-11, telli-docs-22), suggesting agent creation is possible without the dashboard. However, there is no explicit API/CLI 'create agent' endpoint documented, no CLI tool is mentioned at all, and the MCP-driven agent creation is only described as an assistant chat capability rather than a deterministic API call — so a full end-to-end create→attach-number→place-call pipeline via one consistent interface is not clearly evidenced. Missing for 10: a documented create-agent API/CLI endpoint, confirmation MCP/Charlie can fully provision agents without any dashboard step, and an example combining all three steps in one flow.

   • [claimed-docs] “Charlie: Build and refine agents, automate workflows, and analyze your calls with telli's built-in AI assistant.”
   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Imports a phone number from your own SIP trunk provider. This allows you to use existing phone numbers with telli by configuring custom SIP …”
   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   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 telli
   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

   telli~partialprobed5/10

   Charlie, telli's built-in AI assistant, is documented as helping to 'build and refine agents, automate workflows, and analyze your calls,' which supports the core idea of AI-assisted agent authoring, and the MCP server lets external AI tools interact with Charlie too. However there's no concrete evidence of generating test cases from a description, or detailed examples of prompt/flow generation output. missing for 10: documented examples of Charlie generating/improving prompts or flows from a natural-language description, evidence of AI-generated test case creation, and independent/hands-on confirmation of these capabilities in practice.

   • [claimed-docs] “Charlie: Build and refine agents, automate workflows, and analyze your calls with telli's built-in AI assistant.”
   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   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 to Bland
   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

   telli~partialclaimed6/10

   telli provides agent builder, test call feature, voice cloning, and phone number setup (forwarding or SIP trunk import) which together support building and testing a voice agent with prompt, voice, and number, but there's no explicit walkthrough or evidence confirming a full end-to-end setup can be completed within an hour or that phone number provisioning is instant/self-serve for new numbers. missing for 10: a documented quick-start/time-to-first-call benchmark, evidence of instant/native number provisioning (vs SIP/forwarding setup), and independent/hands-on confirmation of the full flow's speed.

   • [claimed-docs] “In the agent builder, click Test call to open the dialog. Pick the agent version (your draft with unsaved changes or any saved revision)...”
   • [claimed-docs] “Use the side-by-side view to listen to the recording while following along in the transcript.”
   • [claimed-docs] “Voice cloning lets you give your agent a custom voice (your own, a colleague's, or a professional voice actor's), so the agent sounds exactl…”
   • [claimed-docs] “Keep your public business number and forward incoming calls to a telli number. You configure the forwarding with your phone provider or PBX.”
   • [claimed-docs] “Connect an existing number and phone system for incoming and outgoing calls. You need SIP trunk access from your provider.”
   • [claimed-docs] “Imports a phone number from your own SIP trunk provider. This allows you to use existing phone numbers with telli by configuring custom SIP …”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   telli—none0/10

   No evidence in the pack mentions multilingual support, language detection, or mid-call language switching for telli's voice agents; all documented features concern scheduling, webhooks, tools, analytics, and MCP integration.

   ⚑ flag

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

   weight 2 · round to Bland
   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

   telli~partialclaimed4/10

   telli has an 'agent builder' with test-call and versioning, and workflow triggers (scheduled, SMS/WhatsApp) suggesting some no-code workflow configuration, plus tool-calls like transfer to another agent implying handoffs. But there's no evidence of a visual flow builder with branching/state diagrams — evidence only shows agent settings, tools, and triggers, not a drag-and-drop conversation flow canvas. missing for 10: explicit visual builder UI with branching/state nodes, evidence of designing multi-step flows with conditional paths, and documentation of handoff logic beyond simple 'transfer tool'.

   • [claimed-docs] “In the agent builder, click Test call to open the dialog. Pick the agent version (your draft with unsaved changes or any saved revision)...”
   • [claimed-docs] “Transfer tools | Move the call to another agent or number.”
   • [claimed-docs] “Choose SMS and WhatsApp workflow recipients from contact properties, collected values, or a fixed number”
   • [claimed-docs] “Workflows can now run automatically on a recurring schedule. Add a Scheduled trigger, choose the interval and start time, and let the workfl…”

   proof ↗⚑ flag

Personalization

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

   weight 2 · round to Bland
   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

   telli—none0/10

   The evidence describes contact properties and collected values being used to select SMS/WhatsApp recipients (telli-docs-5) and APIs to manage contacts and schedule calls (telli-docs-12, telli-docs-17), but nowhere does it document injecting dynamic variables or per-caller context into an agent's voice call script/persona at call time. Missing for 10: explicit documentation of variable placeholders in agent prompts, a mechanism for passing per-call custom data via the schedule-call API, and any example of personalized call content driven by caller-specific fields.

   • [claimed-docs] “Choose SMS and WhatsApp workflow recipients from contact properties, collected values, or a fixed number”
   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”

   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 drawn
   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

   telli—none0/10

   No evidence of a built-in knowledge base or RAG feature for grounding agents on uploaded documents; the docs describe custom tools for API integration and a web_search tool for public information, but nothing about ingesting documents or content for retrieval-based answers.

   ⚑ flag

Agent access

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

   weight 2 · round to telli
   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

   telli✓fullprobed9/10

   telli hosts a live llms.txt file (confirmed via probe, HTTP 200) plus machine-readable OpenAPI spec explicitly intended for coding agents, and docs also mention agent-oriented context for downloading API specs. missing for 10: no independent/third-party confirmation of an agent successfully consuming llms.txt in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.telli.com/llms.txt # telli Documentation > telli is the AI voice platform for agents that sell, su…”
   • [claimed-docs] “Download them to generate client libraries, import the API into tools like Postman, or give coding agents machine-readable API context”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

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

   weight 2 · round to Bland
   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

   telli~partialprobed6/10

   telli exposes a full REST API (with OpenAPI spec) to programmatically schedule calls, manage contacts, and trigger workflows, plus webhooks for event-driven automation and scheduled/recurring workflow triggers — all of which support headless, non-UI automation suitable for CI-like pipelines. However there is no explicit CI/CD example, CLI tool, or documented automated pipeline integration beyond the API/webhook primitives. Missing for 10: dedicated CI/CD documentation or examples, a CLI for scripted invocation, and independent confirmation of headless use in production pipelines.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Workflows can now run automatically on a recurring schedule. Add a Scheduled trigger, choose the interval and start time, and let the workfl…”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   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

   telli—none0/10

   Telli's documented MCP support only runs in the opposite direction — it exposes an MCP server so external AI tools (Claude, ChatGPT, Codex) can talk to its Charlie assistant (telli-docs-22, telli-probe-3), not a mechanism for telli's own voice agents to consume external MCP servers' tools. Custom Tools (telli-docs-14, telli-docs-20) let agents call external APIs but are described as custom integrations, not MCP-based tool ingestion.

   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”
   • [claimed-docs] “Custom Tools: Extend telli's capabilities with custom integrations and workflows.”
   • [claimed-docs] “Custom tools allow your AI agents to access external information by connecting to your APIs.”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   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

   telli✓fullprobed8/10

   telli documents an official MCP server that lets external AI tools (Claude, ChatGPT, Codex) connect over the Model Context Protocol to interact with Charlie, telli's assistant, confirmed both in docs and via a live probe of the integrations/mcp page. Missing for 10: no independent/hands-on corroboration beyond first-party docs and the probe, and no detail on server setup/auth specifics.

   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to Bland
   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

   telli—none0/10

   telli offers a REST API, webhooks, and an MCP server for AI assistants, but there is no mention anywhere in the evidence of an official CLI tool for telli. missing for 10: any documentation or reference to a telli CLI, its commands, or installation instructions.

   ⚑ flag

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

   weight 3 · round drawn
   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

   telli✓fullprobed9/10

   telli documents a REST API with endpoints (schedule-call, import-phone-number), API key/bearer auth, webhooks, and a downloadable OpenAPI spec (confirmed live at openapi.json), plus an MCP server for AI tool access — enabling full programmatic/agentic control. missing for 10: independent third-party validation of API robustness beyond vendor docs.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “All endpoints authenticate with an API key from the telli app (Settings > Developer), sent as a bearer token”
   • [claimed-docs] “Download them to generate client libraries, import the API into tools like Postman, or give coding agents machine-readable API context”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Imports a phone number from your own SIP trunk provider. This allows you to use existing phone numbers with telli by configuring custom SIP …”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   telli—none0/10

   telli documents a single API key (bearer token) generated in Settings > Developer, but there's no evidence of scoped permissions, role-based keys, or least-privilege credential issuance for agents specifically.

   • [claimed-docs] “All endpoints authenticate with an API key from the telli app (Settings > Developer), sent as a bearer token”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Bland
   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

   telli~partialprobed5/10

   telli provides a REST API with an OpenAPI spec that can be used to generate client libraries, plus webhooks, custom tools, and an MCP server for agent integration, but there is no evidence of telli-maintained official SDKs in specific languages (e.g., Python/JS packages published by telli) — only self-generated clients from the OpenAPI spec. missing for 10: dedicated first-party SDK packages/repos, language-specific SDK documentation, versioning/release notes for such SDKs.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “All endpoints authenticate with an API key from the telli app (Settings > Developer), sent as a bearer token”
   • [claimed-docs] “Download them to generate client libraries, import the API into tools like Postman, or give coding agents machine-readable API context”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round to telli
   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

   telli✓fullclaimed8/10

   telli documents a webhooks system that pushes real-time POST notifications about call events, status changes, and outcomes, explicitly described as event-driven subscription for external systems (telli-docs-13, telli-docs-21). Missing for 10: no evidence of configurable event-type filtering/subscription granularity or independent third-party confirmation of reliability.

   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”

   proof ↗⚑ flag

Agentic features

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

   weight 2 · round to telli
   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

   telli~partialclaimed6/10

   telli ships a built-in AI assistant (Charlie) that can 'analyze your calls' and help build/refine agents, plus dashboards for reach rate, call duration, success rate, and per-call transcript/outcome analysis, which together provide AI-driven insight into product data. However, the docs don't detail proactive, autonomous 'suggestions' surfaced to the user (e.g., recommended actions, anomaly detection) beyond on-demand analysis via Charlie or manual dashboard review. Missing for 10: concrete examples of Charlie proactively surfacing insights/recommendations, independent user corroboration of insight quality.

   • [claimed-docs] “Charlie: Build and refine agents, automate workflows, and analyze your calls with telli's built-in AI assistant.”
   • [claimed-docs] “Track key metrics like reach rate, call duration, and success rate.”
   • [claimed-docs] “Analyze individual calls, transcripts, and call outcome fields.”
   • [claimed-docs] “Submit feedback on calls to continuously improve your agent.”

   proof ↗⚑ flag

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

   weight 2 · round to telli
   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

   telli✓fullclaimed7/10

   telli supports Scheduled triggers so workflows run automatically without a call event, plus retry/dialing-window configuration, webhooks, and an AI assistant (Charlie) to build/automate workflows — together enabling background autonomous automations. Missing for 10: independent/hands-on verification of scheduled automations running reliably in production, and more detail on monitoring/error-handling for autonomous runs beyond docs claims.

   • [claimed-docs] “Workflows can now run automatically on a recurring schedule. Add a Scheduled trigger, choose the interval and start time, and let the workfl…”
   • [claimed-docs] “Configure dialing windows, calling strategies, and retry behavior.”
   • [claimed-docs] “Charlie: Build and refine agents, automate workflows, and analyze your calls with telli's built-in AI assistant.”
   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   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

   telli✓fullprobed8/10

   telli documents "Charlie," a built-in AI assistant that can build/refine agents, automate workflows, and analyze calls, and this assistant is also exposed via an MCP server for external AI tools to interact with. This directly matches delegating tasks to a built-in assistant within the product. Missing for 10: independent/hands-on verification of Charlie's task delegation in practice and more detail on the scope of tasks it can autonomously execute.

   • [claimed-docs] “Charlie: Build and refine agents, automate workflows, and analyze your calls with telli's built-in AI assistant.”
   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   proof ↗⚑ flag

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

   weight 2 · round to Bland
   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

   telli~partialprobed6/10

   telli offers Charlie, a built-in AI assistant that can build/refine agents, automate workflows, and analyze calls via natural language, plus an MCP server letting external AI tools (Claude, ChatGPT, Codex) converse with Charlie — both point to natural-language operation. However, most of the product surface (workflows, scheduling, dialing windows, phone number setup) is described as configured through UI/API rather than conversational commands, and there's no independent/hands-on evidence of Charlie's NL command scope or reliability. missing for 10: hands-on evidence of Charlie executing complex natural-language commands, documentation detailing the breadth of actions controllable purely via NL, independent corroboration beyond vendor docs.

   • [claimed-docs] “Charlie: Build and refine agents, automate workflows, and analyze your calls with telli's built-in AI assistant.”
   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   proof ↗⚑ flag

Api quality

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

   weight 2 · round to telli
   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

   telli~partialprobed5/10

   telli publishes an OpenAPI spec (openapi.json) and API docs with downloadable schemas for generating client libraries or feeding coding agents, but there is no evidence of an interactive reference with runnable/'try it' examples in-browser. missing for 10: an interactive try-it console, live runnable code samples, or sandboxed request execution within the docs site.

   • [claimed-docs] “Download them to generate client libraries, import the API into tools like Postman, or give coding agents machine-readable API context”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”
   • [claimed-docs] “All endpoints authenticate with an API key from the telli app (Settings > Developer), sent as a bearer token”

   proof ↗⚑ flag

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

   weight 2 · round to telli
   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

   telli✓fullprobed9/10

   telli provides a downloadable OpenAPI spec confirmed live at /openapi.json, and docs explicitly state it can be downloaded to generate client libraries, import into tools, or give coding agents machine-readable API context. Missing for 10: no independent third-party confirmation of spec completeness/versioning beyond first-party docs and probe.

   • [claimed-docs] “Download them to generate client libraries, import the API into tools like Postman, or give coding agents machine-readable API context”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

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

   weight 1 · round to Bland
   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

   telli~partialclaimed4/10

   telli's agent builder includes a 'Test call' feature that lets users dial a draft or saved agent version and review the recording/transcript before going live, which functions as a lightweight test mode (telli-docs-28, telli-docs-29). However, there is no documentation of a dedicated sandbox environment, separate test dataset, or explicit guarantee that test calls don't write to production contacts/analytics. missing for 10: explicit sandbox/staging environment separate from production, confirmation that test calls don't affect production data/metrics, and any mention of test API keys or isolated test accounts.

   • [claimed-docs] “In the agent builder, click Test call to open the dialog. Pick the agent version (your draft with unsaved changes or any saved revision)...”
   • [claimed-docs] “Use the side-by-side view to listen to the recording while following along in the transcript.”

   proof ↗⚑ flag

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

   weight 2 · round to Bland
   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

   telli—none0/10

   Evidence shows an OpenAPI spec and API key auth exist, but nothing in the pack mentions API versioning (e.g., v1/v2 paths, version headers) or any documented deprecation/sunset policy for endpoints. Missing for 10: explicit API version scheme, changelog of breaking changes, and a stated deprecation/sunset policy.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “All endpoints authenticate with an API key from the telli app (Settings > Developer), sent as a bearer token”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

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

   weight 2 · round to Bland
   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

   telli~partialprobed6/10

   Telli's platform is built around calling large contact lists (dialing windows, calling strategies, retry behavior, reach-rate metrics) and its REST API lets users 'programmatically manage contacts, schedule calls' at scale, implying bulk campaign operations. However, there is no explicit documentation of a bulk-upload/bulk-schedule endpoint or UI for acting on many items simultaneously — the schedule-call endpoint documented is single-call oriented. Missing for 10: explicit bulk-contact import/schedule endpoint, documented batch size limits, and independent evidence of large-scale bulk execution.

   • [claimed-docs] “Configure dialing windows, calling strategies, and retry behavior.”
   • [claimed-docs] “Track key metrics like reach rate, call duration, and success rate.”
   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.telli.com/llms.txt # telli Documentation > telli is the AI voice platform for agents that sell, su…”

   proof ↗⚑ flag

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

   weight 3 · round to Bland
   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

   telli~partialclaimed5/10

   telli's workflows can be triggered by call events (implied default) and now by a recurring schedule, and webhooks notify external systems on call events, showing some event-driven automation. However there's no documented rule-definition interface (conditions, event-type selection, if/then logic) for arbitrary events beyond calls/schedule. Missing for 10: explicit rule/condition builder, broader event-type coverage beyond calls, and evidence of user-defined trigger logic rather than fixed schedule/call triggers.

   • [claimed-docs] “Workflows can now run automatically on a recurring schedule. Add a Scheduled trigger, choose the interval and start time, and let the workfl…”
   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round to telli
   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

   telli✓fullclaimed7/10

   telli documents scheduled triggers that let workflows run recurringly on an interval/start time without a call event, directly matching the recurring-workflow story, and also supports scheduling individual calls via API. missing for 10: independent/hands-on corroboration of recurrence reliability and detail on editing/monitoring scheduled workflow runs.

   • [claimed-docs] “Workflows can now run automatically on a recurring schedule. Add a Scheduled trigger, choose the interval and start time, and let the workfl…”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Optional flag to bypass dialing window restrictions for the scheduled call. When true, calls at the scheduled time regardless of dialing win…”

   proof ↗⚑ flag

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

   weight 1 · round to Bland
   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

   telli~partialclaimed3/10

   The only hint of versioning is in agent testing, where you can pick 'your draft with unsaved changes or any saved revision' (telli-docs-28), implying saved agent versions exist. However there is no documented review interface for comparing versions, no explicit rollback/restore action, and no versioning mentioned for broader automations/workflows (only agents). Missing for 10: explicit rollback UI/API, version diff/review tooling, and versioning coverage for workflows beyond agent test calls.

   • [claimed-docs] “In the agent builder, click Test call to open the dialog. Pick the agent version (your draft with unsaved changes or any saved revision)...”

   proof ↗⚑ flag

Compliance

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

   weight 2 · round to telli
   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

   telli~partialclaimed6/10

   telli documents conditional recording based on caller consent and a configurable data-retention policy that auto-deletes contacts and call data after a defined age, directly addressing the founder's compliance need. However, evidence lacks detail on per-call granular controls (e.g., pause/resume, jurisdiction-specific disclosure scripts), audit logging of consent events, or independent verification that these features work as described. Missing for 10: per-call recording toggle/audit trail details, jurisdiction-specific disclosure templates, retention configuration granularity (e.g., per-data-type settings), and independent/hands-on confirmation of compliance behavior.

   • [claimed-docs] “Data retention deletes contacts and call data automatically once they reach an age you define.”
   • [claimed-docs] “In telli, you can easily make audio recording conditional on the caller's consent — so a call is only recorded once they agree.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   telli—none0/10

   No evidence in the pack mentions HIPAA, BAA, SOC 2, or data-residency options; only data retention and consent-based recording are covered, which don't establish formal compliance certifications.

   ⚑ flag

Scale

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

   weight 2 · round to Bland
   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

   telli—none0/10

   No evidence pack item documents concurrency limits, per-account call caps, or scaling guarantees for simultaneous calls; docs mention dialing windows/retry strategy but not concurrency ceilings or auto-scaling behavior. This is a fair question for a voice-calling platform (scaling many simultaneous calls is core to the product's value), so 'none' applies rather than 'na'.

   ⚑ flag

Self host

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

   weight 3 · round drawn
   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

   telli—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

Latency

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

   weight 3 · round drawn
   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

   telli—none0/10

   No evidence pack item mentions latency numbers, turn-taking performance, or tuning guidance for voice response speed; documentation covers workflows, API, webhooks, and agent building but nothing on end-to-end latency benchmarks or tuning.

   ⚑ 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
   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

   telli—none0/10

   No evidence in the pack addresses barge-in, interruption handling, speech-stopping, or turn recovery mechanics; the docs cover scheduling, workflows, analytics, and integrations but never mention conversational turn-taking behavior.

   ⚑ flag

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

   weight 1 · round drawn
   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

   telli—none0/10

   No evidence pack item mentions noise suppression, audio filtering, or any mechanism for handling noisy call environments; documentation covers scheduling, workflows, analytics, and voice cloning but not 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
   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

   telli—none0/10

   No evidence in the pack discusses turn-taking, end-of-turn detection, VAD, or interruption handling in telli's voice agent; the docs cover scheduling, workflows, tools, and integrations but not conversational latency/turn-taking mechanics.

   ⚑ flag

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

   weight 2 · round to Bland
   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

   telli~partialprobed6/10

   telli exposes a REST API (with OpenAPI spec) covering contacts, scheduling calls, phone number import, custom tools, and webhooks, and also offers an MCP server so external AI tools can interact with Charlie. However, several UI-native features—agent builder/test-call dialog, live monitoring dashboard, side-by-side transcript/recording view, voice cloning setup, workflow trigger/recurring schedule configuration UI, and analytics dashboards—are not documented as API-accessible endpoints, so full UI/API parity is not evidenced. missing for 10: API endpoints for agent building/testing, live monitoring, voice cloning, workflow scheduling configuration, and analytics/reporting equivalent to UI dashboards.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “All endpoints authenticate with an API key from the telli app (Settings > Developer), sent as a bearer token”
   • [claimed-docs] “Download them to generate client libraries, import the API into tools like Postman, or give coding agents machine-readable API context”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Imports a phone number from your own SIP trunk provider. This allows you to use existing phone numbers with telli by configuring custom SIP …”
   • [claimed-docs] “Custom tools allow your AI agents to access external information by connecting to your APIs.”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   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

   telli—none0/10

   No evidence of a bulk data-export feature or open-format data portability; only API endpoints to manage/read specific records and a data-retention deletion policy are documented, not a full self-service export/leave capability. Missing for 10: documented full-account export tool/endpoint, open-format (CSV/JSON) bulk export, and any explicit data-portability statement.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Data retention deletes contacts and call data automatically once they reach an age you define.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   telli—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round drawn
   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

   telli—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

Pricing

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

   weight 2 · round drawn
   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

   telli—none0/10

   No evidence in the pack mentions pricing, per-minute rates, usage costs, or any cost calculator/estimator; the docs cover features (workflows, appointments, API, MCP, etc.) but nothing about published pricing.

   ⚑ flag

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

   weight 2 · round drawn
   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

   telli—none0/10

   No evidence in the pack mentions data residency, region selection, or data storage location controls; data retention (deletion timing) is covered but that is a different capability from choosing storage region.

   ⚑ flag

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

   weight 3 · round drawn
   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

   telli—none0/10

   No evidence in the pack addresses AI training data usage or opt-out policies; data retention docs cover deletion of contacts/call data but not model-training exclusion.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to telli
   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

   telli~partialclaimed6/10

   telli documents automatic data retention deletion (contacts and call data purged once they reach a defined age) and conditional consent-based recording, giving users some control over data lifecycle and privacy. However, there's no evidence of user-initiated manual deletion, export/right-to-access tools, or granular per-record deletion controls beyond the age-based policy. missing for 10: manual/on-demand deletion controls, data export capability, independent verification of retention enforcement.

   • [claimed-docs] “Data retention deletes contacts and call data automatically once they reach an age you define.”
   • [claimed-docs] “In telli, you can easily make audio recording conditional on the caller's consent — so a call is only recorded once they agree.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   telli—none0/10

   telli is a voice-agent platform, not a developer tool where telemetry opt-out is typically discussed, but the axis is still fair to ask (does the product track usage, and can a user disable it). The evidence pack (data retention, consent-based recording, etc.) never mentions any telemetry/usage-tracking opt-out mechanism for the product itself. Missing for 10: any documentation of telemetry collection practices or an opt-out setting/toggle.

   ⚑ flag

Call control

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

   weight 2 · round to telli
   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

   telli~partialclaimed4/10

   Docs confirm a transfer tool that can move a call to another agent or number, matching the core escalation-to-human idea, but there's no mention of distinct warm vs. blind transfer modes or explicit context-passing to the human recipient. missing for 10: explicit warm/blind transfer distinction, evidence of context/data hand-off during transfer, hands-on confirmation of the feature in practice.

   • [claimed-docs] “Transfer tools | Move the call to another agent or number.”

   proof ↗⚑ flag

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

   weight 1 · round drawn
   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

   telli—none0/10

   No evidence in the pack mentions DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving; telli's docs cover scheduling, transfers, web search tool, and webhooks but nothing about touch-tone or answering machine handling.

   ⚑ flag

Campaigns

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

   weight 2 · round to Bland
   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

   telli~partialclaimed6/10

   telli documents dialing windows, calling strategies, and retry behavior (telli-docs-9) plus a schedule-call API endpoint that respects dialer windows or can bypass them (telli-docs-17, telli-docs-18) and reach-rate/success metrics (telli-docs-7), which together support scheduled, throughput-controlled outbound calling. However there is no explicit mention of a 'campaign' concept, bulk contact upload, or concurrency/rate limits for large batches. Missing for 10: explicit batch/campaign creation UI or endpoint, bulk contact import for calling, and documented concurrency/throughput limits beyond dialing windows.

   • [claimed-docs] “Configure dialing windows, calling strategies, and retry behavior.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Optional flag to bypass dialing window restrictions for the scheduled call. When true, calls at the scheduled time regardless of dialing win…”
   • [claimed-docs] “Track key metrics like reach rate, call duration, and success rate.”
   • [claimed-docs] “Workflows can now run automatically on a recurring schedule. Add a Scheduled trigger, choose the interval and start time, and let the workfl…”

   proof ↗⚑ flag

Numbers

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

   weight 3 · round to Bland
   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

   telli~partialprobed7/10

   telli's docs show a REST API with a schedule-call endpoint for outbound calls, an import-phone-number endpoint to bring SIP-trunk numbers under API control, webhooks for call events, and an OpenAPI spec confirming the API surface. However, inbound handling is documented mainly as manual forwarding/SIP trunk setup rather than a clear 'provision a new number via API' flow, and there's no explicit doc of purchasing/creating a fresh telli-hosted number through the API. missing for 10: a documented endpoint to provision/purchase brand-new phone numbers (not just import existing SIP trunk numbers), and clearer API-driven inbound call handling beyond forwarding.

   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Schedules a call at the earliest opportunity within the dialer window. May occur immediately or on the next business day depending on settin…”
   • [claimed-docs] “Imports a phone number from your own SIP trunk provider. This allows you to use existing phone numbers with telli by configuring custom SIP …”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [claimed-docs] “Keep your public business number and forward incoming calls to a telli number. You configure the forwarding with your phone provider or PBX.”
   • [claimed-docs] “Connect an existing number and phone system for incoming and outgoing calls. You need SIP trunk access from your provider.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   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
   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

   telli✓fullclaimed8/10

   Docs explicitly support importing numbers from a customer's own SIP trunk provider via a dedicated API endpoint, plus general SIP trunk connection for incoming/outgoing calls and call forwarding from an existing PBX/provider number. This directly matches the platform-engineer's need to bring their own carrier rather than being locked into bundled telephony. Missing for 10: no explicit mention of native Twilio/Telnyx number import (only generic SIP trunk import), and no independent/hands-on confirmation of the SIP trunk setup working in practice.

   • [claimed-docs] “Imports a phone number from your own SIP trunk provider. This allows you to use existing phone numbers with telli by configuring custom SIP …”
   • [claimed-docs] “Keep your public business number and forward incoming calls to a telli number. You configure the forwarding with your phone provider or PBX.”
   • [claimed-docs] “Connect an existing number and phone system for incoming and outgoing calls. You need SIP trunk access from your provider.”

   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
   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

   telli~partialclaimed5/10

   telli provides analytics like reach rate, success rate, call outcome fields, transcripts, and manual feedback submission for agent improvement, but there's no evidence of automated AI-driven quality scoring, failure flagging, or resolution analysis performed autonomously by the platform's AI — feedback and analysis appear human-driven rather than AI-automated review. missing for 10: automated AI scoring of call quality, automatic failure/anomaly flagging, AI-generated resolution analysis without human review.

   • [claimed-docs] “Track key metrics like reach rate, call duration, and success rate.”
   • [claimed-docs] “Analyze individual calls, transcripts, and call outcome fields.”
   • [claimed-docs] “Submit feedback on calls to continuously improve your agent.”

   proof ↗⚑ flag

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

   weight 2 · round to telli
   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

   telli~partialclaimed6/10

   telli's docs describe built-in dashboards tracking reach rate, call duration, and success rate, plus per-call analysis of transcripts and outcome fields, and a live monitoring overview—covering most of the story without needing custom tooling. However, there is no explicit mention of sentiment analysis in dashboards. missing for 10: sentiment scoring/analysis in dashboards, independent/hands-on corroboration of dashboard usability.

   • [claimed-docs] “Track key metrics like reach rate, call duration, and success rate.”
   • [claimed-docs] “Analyze individual calls, transcripts, and call outcome fields.”
   • [claimed-docs] “Live Monitoring > Real-time call overview of your contacts”

   proof ↗⚑ flag

Monitoring

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

   weight 1 · round to telli
   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

   telli~partialclaimed5/10

   telli offers a real-time Live Monitoring call overview (telli-docs-32) and webhooks that push real-time notifications on call events/status/outcomes (telli-docs-13, telli-docs-21), which a platform engineer could wire into an alerting pipeline, plus metric tracking like success/reach rate (telli-docs-7). However there is no documented built-in alerting mechanism, error-rate-spike detection, or agent-misbehavior alerting system. Missing for 10: native alert/notification thresholds, anomaly or error-rate-spike detection, and evidence of alert delivery (email/Slack/PagerDuty) tied to live monitoring.

   • [claimed-docs] “Live Monitoring > Real-time call overview of your contacts”
   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [claimed-docs] “Track key metrics like reach rate, call duration, and success rate.”

   proof ↗⚑ flag

Testing

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

   weight 2 · round to Bland
   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

   telli~partialclaimed5/10

   telli's agent builder includes a 'Test call' feature to trial a draft or saved agent version and review the recording/transcript side-by-side before going live, which covers pre-production testing, and feedback submission helps iterate on agent quality. However, there is no evidence of simulated/synthetic conversation runs, automated eval suites, or scripted test scenarios independent of an actual phone call. Missing for 10: automated evals framework, non-call simulated conversation testing, batch/regression testing tools.

   • [claimed-docs] “In the agent builder, click Test call to open the dialog. Pick the agent version (your draft with unsaved changes or any saved revision)...”
   • [claimed-docs] “Use the side-by-side view to listen to the recording while following along in the transcript.”
   • [claimed-docs] “Submit feedback on calls to continuously improve your agent.”

   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 drawn
   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

   telli~partialclaimed6/10

   telli documents webhooks pushing real-time call event/status/outcome data and a REST API for programmatic access, plus conversation views showing appointment/outcome fields and call outcome analysis, supporting structured post-call data delivery. However, there's no explicit documentation of a structured schema for entities/dispositions extraction or example payloads showing the full breadth of structured fields promised. missing for 10: example webhook/API payload showing structured entities and disposition fields, documentation of a dedicated 'call outcomes' data model, independent/hands-on confirmation of data completeness.

   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [claimed-docs] “Analyze individual calls, transcripts, and call outcome fields.”
   • [claimed-docs] “Conversations now show appointments saved during a call, including the time, status, hosts, and provider link.”
   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”

   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 drawn
   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

   telli—none0/10

   telli documents custom tools connecting to external APIs (telli-docs-20) and even an MCP server (telli-docs-22, telli-probe-3), but that MCP server lets external AI tools like Claude/ChatGPT talk to telli's Charlie assistant — it is telli acting as an MCP server, not the voice agent consuming external MCP servers as a tool source mid-call. There is no evidence the voice agent itself can plug in MCP servers to gain toolsets during a call.

   • [claimed-docs] “The telli MCP server lets AI tools like Claude, ChatGPT, and Codex talk to Charlie, telli's AI assistant, over the Model Context Protocol.”
   • [probe] “official MCP server documented at https://docs.telli.com/integrations/mcp”
   • [claimed-docs] “Custom tools allow your AI agents to access external information by connecting to your APIs.”

   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
   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

   telli~partialclaimed6/10

   Docs confirm custom tools let agents call external APIs mid-call (telli-docs-20) and built-in tool calls like web_search operate during live calls (telli-docs-30), plus transfer tools show mid-call action execution (telli-docs-31). However, there is no documentation addressing latency masking or filler-speech to avoid 'dead air' while awaiting API responses. missing for 10: explicit evidence of latency-hiding/filler speech during tool calls, independent hands-on confirmation of smooth voice continuity.

   • [claimed-docs] “Custom tools allow your AI agents to access external information by connecting to your APIs.”
   • [claimed-docs] “For a Duo agent, open Tools > System and enable web_search to let the agent search the web for current public information during calls.”
   • [claimed-docs] “Transfer tools | Move the call to another agent or number.”

   proof ↗⚑ flag

Recording

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

   weight 2 · round to telli
   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

   telli~partialprobed5/10

   Docs confirm transcripts and recordings exist and can be reviewed in-app (side-by-side view, consent-based recording) and telli exposes a general REST API plus webhooks for call events, but no endpoint is documented that explicitly returns full call recordings or transcripts programmatically for every call. missing for 10: a documented GET/recording or GET/transcript API endpoint, confirmation of bulk/enumerable retrieval, and independent verification that recordings are accessible via the API rather than only in the UI.

   • [claimed-docs] “Analyze individual calls, transcripts, and call outcome fields.”
   • [claimed-docs] “Use the side-by-side view to listen to the recording while following along in the transcript.”
   • [claimed-docs] “In telli, you can easily make audio recording conditional on the caller's consent — so a call is only recorded once they agree.”
   • [claimed-docs] “API Endpoints: Use telli's REST API to programmatically manage contacts, schedule calls, and integrate with your existing systems.”
   • [claimed-docs] “Webhooks: Receive real-time notifications about call events, status changes, and outcomes.”
   • [claimed-docs] “Webhooks push data from telli to your systems in real time. When something happens on a call, telli sends a POST request to the endpoint you…”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.telli.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

Transcription

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

   weight 2 · round drawn
   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

   telli—none0/10

   telli documents transcript viewing and side-by-side playback (telli-docs-29) but provides no evidence of developer-facing control over STT provider selection, choice of underlying language models, or custom key-term/vocabulary boosting for transcription accuracy.

   • [claimed-docs] “Use the side-by-side view to listen to the recording while following along in the transcript.”

   proof ↗⚑ flag

Voices

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

   weight 2 · round to telli
   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

   telli~partialclaimed5/10

   Voice cloning is documented as a real feature (telli-docs-27) letting founders give agents a custom voice, but the pack's only consent-related documentation concerns call recording consent (telli-docs-26), not a consent process specifically for cloning someone's voice. Missing for 10: explicit documented consent/authorization workflow for the voice being cloned (e.g., proof of rights from the voice owner), and any legal/compliance guardrails for cloning third-party voices.

   • [claimed-docs] “Voice cloning lets you give your agent a custom voice (your own, a colleague's, or a professional voice actor's), so the agent sounds exactl…”
   • [claimed-docs] “In telli, you can easily make audio recording conditional on the caller's consent — so a call is only recorded once they agree.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   telli~partialclaimed3/10

   Docs mention voice cloning to give an agent a custom voice, but there is no evidence of a curated voice library to choose from or the ability to plug in multiple external TTS providers as the story requires. missing for 10: evidence of a selectable voice library, multi-provider TTS integration, developer API/config for switching TTS engines.

   • [claimed-docs] “Voice cloning lets you give your agent a custom voice (your own, a colleague's, or a professional voice actor's), so the agent sounds exactl…”

   proof ↗⚑ flag