Retell AI vs Deepgram Voice Agent

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 Retell AI
   Retell AI~partialprobed6/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialprobed5/10

   Deepgram documents agent creation via reusable agent configs (UUID-based), telephony connectivity for inbound/outbound calls, and a CLI with a built-in MCP server giving AI coding tools API access — the building blocks for programmatic provisioning exist. However, the evidence shows phone-call handling is done via a Twilio bridge (server code required) rather than a native Deepgram 'attach number/place call' API, and no doc shows the CLI or MCP server actually exposing agent-create + number-attach + call-place as a single end-to-end flow. Missing for 10: explicit CLI/MCP commands for provisioning a phone number and placing a call, and confirmation that this full workflow avoids external glue code/dashboard steps.

   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence covers building agents manually (prompting, function calling, reusable configs, MCP server for coding tools) but there is no mention of the platform's own AI generating or improving prompts, flows, or test cases from a description; the MCP server exposes Deepgram APIs to external coding assistants rather than being an AI author within the platform itself.

   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   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 Retell AI
   Retell AI✓fullcommunity8/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram provides all core building blocks — prompting, voice/LLM selection, telephony via Twilio bridging, and a single WebSocket API — that together could plausibly get a developer to a first live call quickly. However, the telephony path requires standing up your own server to bridge Twilio audio to the Voice Agent WebSocket, which is nontrivial integration work rather than a turnkey 'phone number in one hour' flow, and there's no first-party quickstart or time-to-first-call benchmark cited. missing for 10: an end-to-end quickstart/tutorial demonstrating full setup within an hour, evidence of a managed/no-code telephony number provisioning path, and independent hands-on confirmation of setup speed.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Supported LLM providers | Parameter | open_ai | anthropic | aws_bedrock | google | groq | nvidia”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed5/10

   Deepgram's docs confirm multilingual voice agent support, noting that STT and TTS model choices must be configured for the target language (deepgram-docs-10), but the evidence pack does not explicitly document automatic language detection or dynamic switching mid-call — only static multilingual configuration is described. Missing for 10: explicit documentation of automatic language detection, mid-call language switching mechanics, and independent/hands-on confirmation of this behavior in production.

   • [claimed-docs] “A multilingual voice agent has two model decisions: which STT model transcribes the user, and which TTS model speaks the agent.”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   All evidence describes a code/API-first architecture (WebSocket connections, JSON Settings messages, system prompts, function calling, CLI/SDK) rather than a visual no-code builder; there is no mention of a drag-and-drop flow designer, branching UI, or state-machine editor. missing for 10: any visual builder UI, no-code branching/state design, or drag-and-drop handoff configuration.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Supported LLM providers | Parameter | open_ai | anthropic | aws_bedrock | google | groq | nvidia”

   proof ↗⚑ flag

Personalization

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram supports prompting, reusable agent configs (passed at call time via UUID), function calling, and mid-call message injection, which together allow injecting per-call context/variables into agent behavior; A/B testing of configs also implies runtime parameterization. However there is no explicit documented mechanism for templated dynamic variables (e.g., {{caller_name}}) or a dedicated per-caller context API akin to other platforms. missing for 10: explicit dynamic variable templating syntax, dedicated per-caller metadata injection API, hands-on example of personalizing a call with caller-specific data.

   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”
   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”
   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack covers architecture, function calling, multi-agent handoff, telephony, prompting, and LLM providers, but nowhere documents a built-in knowledge base or RAG feature for grounding the agent on user documents. Function calling ([deepgram-docs-2]) could theoretically be wired to an external retrieval system, but that is not the same as a built-in KB/RAG capability, and no such feature is described.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”

   proof ↗⚑ flag

Agent access

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

   weight 2 · round drawn
   Retell AI✓fullprobed9/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent✓fullprobed9/10

   Direct probe confirms llms.txt exists at developers.deepgram.com/llms.txt (HTTP 200) with explicit AI-agent instructions, and docs pages support .md suffix for clean markdown retrieval, both verified by live probes. missing for 10: no independent third-party confirmation of an agent actually consuming this in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.deepgram.com/llms.txt # Deepgram's Docs ## Instructions for AI Agents - For clean Markdown …”
   • [probe] “PROBE docs-md: HTTP 200 at https://developers.deepgram.com/docs/voice-agent.md > For clean Markdown of any page, append .md to the page URL.…”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialprobed6/10

   The Voice Agent is a WebSocket API (no GUI dependency) and Deepgram ships a terminal-based `dg` CLI for scripting Deepgram operations (transcribe, synthesize, manage account) which is inherently automatable/CI-friendly, and temporary tokens support secure automated auth. However, there is no explicit documentation of running the Voice Agent itself headlessly in CI pipelines, no CI/CD examples, and the CLI's primary use-cases described are transcription/synthesis rather than orchestrating full voice-agent sessions programmatically. Missing for 10: dedicated CI/headless automation guide for Voice Agent sessions, example CI pipeline configs, and confirmation the CLI can drive the Agent API end-to-end rather than just STT/TTS.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “Unlike traditional API keys, temporary tokens are ideal for real-time applications requiring secure, temporary access to Deepgram's services…”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent supports function calling for custom tool use, but there is no evidence the Voice Agent can act as an MCP client to consume external MCP servers' tools. The only MCP-related evidence is a built-in MCP *server* in the `dg` CLI that lets coding tools access Deepgram's APIs — the opposite direction of the story.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent✓fullprobed7/10

   Deepgram ships an official MCP server (bundled in the dg CLI) that lets AI coding tools/agents connect directly to Deepgram's APIs, confirmed both in docs and via a dedicated agentic-tools page. This directly satisfies the story of connecting an agent to Deepgram via an official MCP server. Missing for 10: independent/hands-on verification that the MCP server works reliably in practice, and more detail on which Voice Agent capabilities specifically are exposed through it.

   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent✓fullprobed8/10

   Deepgram ships an official 'dg' CLI for transcribing files, streaming audio, synthesizing speech, and managing accounts from the terminal, and it even embeds an MCP server for AI coding tools, directly supporting agentic/AI-native workflows. Missing for 10: independent/hands-on corroboration beyond first-party docs and more detail on CLI coverage of Voice Agent-specific features.

   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   Retell AI✓fullprobed9/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent✓fullprobed9/10

   Deepgram publishes a documented public API (WebSocket-based Voice Agent API plus OpenAPI spec confirmed live), extensive docs covering endpoints, function calling, LLM providers, telephony, and auth, and even a CLI/MCP server for programmatic/agentic access, making it clearly drivable by an AI-native user. Missing for 10: independent third-party corroboration of API usage beyond vendor docs.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”
   • [claimed-docs] “Unlike traditional API keys, temporary tokens are ideal for real-time applications requiring secure, temporary access to Deepgram's services…”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed4/10

   Deepgram documents temporary, short-lived tokens as an alternative to traditional API keys for secure, limited-duration access (deepgram-docs-14), which partially satisfies a least-privilege credential story, but there is no evidence of granular scopes/permissions (e.g., restricting a key to specific endpoints or agent capabilities) or role-based access control for agents. Missing for 10: documented scope/permission granularity, per-agent key restriction, and evidence of enforcement/verification of least-privilege in practice.

   • [claimed-docs] “Unlike traditional API keys, temporary tokens are ideal for real-time applications requiring secure, temporary access to Deepgram's services…”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialprobed5/10

   Evidence shows an official 'Browser Agent SDK' with composable packages, plus an OpenAPI spec, CLI, and MCP server for developer tooling, indicating some official SDK/build tooling exists. However, evidence does not mention broader server-side/language SDKs (Python, Node, Go, etc.) commonly expected for building agentic applications, nor independent corroboration of SDK quality. Missing for 10: multi-language SDK documentation, independent/hands-on developer reports, broader agentic build examples beyond browser.

   • [claimed-docs] “Browser Agent SDK: add voice AI to any web application via four composable packages.”
   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack shows Deepgram Voice Agent operates via a persistent WebSocket connection for real-time audio streaming, not webhook-based event subscription; no documentation mentions webhooks for event notifications (e.g., call completion, transcript ready, errors). Absence of evidence for an applicable capability yields 'none'.

   ⚑ flag

Agentic features

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack covers Deepgram Voice Agent's API capabilities (function calling, multi-agent orchestration, telephony, LLM providers, prompting) but contains no mention of an in-product analytics dashboard, conversation insights, or AI-generated suggestions derived from a user's own data. While such a feature (e.g., call analytics/insights) is plausible for a voice AI platform, no evidence shows Deepgram surfaces this.

   ⚑ flag

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

   weight 2 · round to Retell AI
   Retell AI~partialcommunity6/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—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

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—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 userOperate the product with natural-language commands

   weight 2 · round to Deepgram Voice Agent
   Retell AI✓fullprobed7/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent✓fullclaimed8/10

   Deepgram Voice Agent is fundamentally a conversational system: users interact via natural spoken language, and the agent supports function calling to perform tasks, live prompting to shape behavior, and mid-call message injection—all driven by natural-language conversation rather than rigid commands. This is core, well-documented functionality (docs-2, docs-7, docs-11) directly matching the story's intent for an ai-native/agentic persona. Missing for 10: independent/hands-on validation of natural-language command accuracy and no evidence of complex multi-turn command chaining reliability from third-party sources.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”

   proof ↗⚑ flag

Api quality

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

   weight 2 · round drawn
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence shows an OpenAPI spec exists and docs pages are available as clean Markdown, but there is no evidence of an interactive API reference with runnable/try-it-now examples (e.g., embedded playground, live code execution, or Swagger/Postman-style explorer). Missing for 10: interactive explorer UI, runnable/executable code snippets, live request/response testing.

   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.deepgram.com/llms.txt # Deepgram's Docs ## Instructions for AI Agents - For clean Markdown …”
   • [probe] “PROBE docs-md: HTTP 200 at https://developers.deepgram.com/docs/voice-agent.md > For clean Markdown of any page, append .md to the page URL.…”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent✓fullprobed9/10

   A probe confirms Deepgram publishes a machine-readable OpenAPI spec at openapi.json containing the 'openapi' key, directly satisfying the story. Missing for 10: no first-party docs page explicitly announcing/describing the OpenAPI spec's coverage or versioning.

   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions a sandbox environment, test/dev API keys, or a way to test without touching production data or usage; only production endpoints, EU endpoint, and opt-out flags are documented. Missing for 10: sandbox/test-mode environment, documentation of non-production keys or test credits, any guidance on isolating test traffic from production data.

   ⚑ flag

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

   weight 2 · round drawn
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack shows extensive Voice Agent API documentation, an OpenAPI spec, CLI, and MCP server, but contains no mention of API versioning scheme or a documented deprecation policy for the Voice Agent API.

   ⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—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

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed5/10

   Deepgram Voice Agent supports function calling that lets the agent trigger actions during a live conversation, and features like InjectAgentMessage and multi-agent handoff suggest some event-driven behavior, but this is developer-defined logic (via function calling code) rather than a declarative rules engine for automatically triggering actions on arbitrary events. missing for 10: a documented rules/trigger engine (if-this-then-that style), event subscription system, or automation workflow builder distinct from manual function-calling code.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent—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 userVersion, review, and roll back my automations

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed3/10

   Deepgram's Reusable Agent Configurations let you persist a named agent config and reference it by UUID, and support A/B testing two configs in parallel, which is adjacent to versioning, but there is no documented version history, diff/review workflow, or explicit rollback mechanism. missing for 10: version history tracking, review/approval workflow, explicit rollback capability, changelog or diffing between config versions.

   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”

   proof ↗⚑ flag

Compliance

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed4/10

   Deepgram documents general data-privacy controls (per-request opt-out from retention via mip_opt_out, EU-region processing endpoint) that touch on data retention/residency, but there is no explicit documentation of per-call recording toggles, consent/disclosure workflows, or configurable retention windows tied to call recordings specifically. Missing for 10: explicit per-call recording enable/disable controls, documented retention period configuration for stored call audio/transcripts, and consent/disclosure feature support.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed4/10

   Evidence shows EU data-residency endpoint and an opt-out from data retention/training, indicating some compliance/trust infrastructure, but there is no mention of HIPAA/BAA support or SOC 2 certification anywhere in the pack. missing for 10: HIPAA/BAA documentation, SOC 2 attestation evidence, broader regional residency options beyond EU.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ flag

Scale

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions concurrency limits, rate limits, per-account call caps, scaling guidance, or capacity request processes for the Voice Agent API; the docs cover architecture, features, and integrations but nothing about scale/concurrency documentation.

   ⚑ flag

Self host

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

   weight 3 · round drawn
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent is a hosted, cloud-only API accessed via WebSocket; nothing in the evidence indicates open-source runtime code that can be self-hosted on customer infrastructure. All references point to Deepgram-hosted endpoints (api.deepgram.com, api.eu.deepgram.com) and a CLI/SDK for calling the cloud service, not deploying the runtime itself.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”

   proof ↗⚑ flag

Latency

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack covers architecture, features (barge-in, telephony, function calling, multi-agent), and CLI/SDK tooling, but contains no documented latency numbers (e.g., ms end-to-end) or tuning guidance for reducing turn-taking latency. missing for 10: quantified latency benchmarks, latency tuning/configuration guidance, any performance dashboard or whitepaper on speed claims.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”

   proof ↗⚑ 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 to Deepgram Voice Agent
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Docs explicitly claim barge-in support where users can talk over the agent and it stops instantly, plus mid-call context tools like InjectAgentMessage for updating conversation state. However, evidence lacks detail on how turn recovery/context updating works technically after interruption, and there's no independent/hands-on corroboration of graceful recovery in practice. missing for 10: technical details on context-state recovery post-interruption, independent verification of barge-in reliability, edge-case handling (e.g., rapid interruptions, latency of stop).

   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”
   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”

   proof ↗⚑ flag

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

   weight 1 · round drawn
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions noise suppression, background noise filtering, or audio denoising features for the Voice Agent; coverage focuses on barge-in, telephony, multi-agent orchestration, and LLM/TTS configuration instead.

   ⚑ flag

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

   weight 2 · round drawn
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence only mentions barge-in (interrupting agent mid-speech) and generic prompting/architecture docs; there is no mention of model-based end-of-turn detection or endpointing that goes beyond VAD/silence timeouts to avoid cutting off slow speakers.

   ⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialprobed6/10

   Deepgram is fundamentally API-first — the Voice Agent is a WebSocket API with full configuration (prompting, function calling, multi-agent, telephony, reusable configs) exposed programmatically, and even the CLI/MCP server let AI-native users manage things from the terminal or via agentic tools rather than a GUI. However, evidence doesn't show a full-featured UI to compare against (e.g., a dashboard/console) nor confirm that every console feature (like A/B testing dashboards, analytics views) has a documented API equivalent. missing for 10: explicit mapping of console/UI-only features (analytics dashboards, A/B test result views) to API endpoints, and confirmation no UI-exclusive functionality exists.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”
   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence describes any data export, account data portability, or open-format export/deletion workflow letting a user take their data and leave; docs cover retention opt-out and EU processing but not export tooling. missing for 10: data export mechanism, open-format export documentation, account closure/data portability guarantees.

   ⚑ flag

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

   weight 2 · round drawn
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent is a proprietary cloud API/SDK product; no evidence anywhere in the pack points to an open-source license or public source repository for the core Voice Agent service. Only API docs, CLI, and SDK usage are documented, none of which imply open-licensed source availability.

   ⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round drawn
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent is a cloud API/SaaS product; all evidence points to hosted WebSocket endpoints, EU regional endpoints, and cloud-based configuration — there is no mention of a self-hostable or on-premises deployment package for the core voice agent model/inference stack. missing for 10: any self-hosting/on-prem deployment option, downloadable model weights or container, docs on running the core service outside Deepgram's cloud.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ flag

Pricing

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions pricing, per-minute rates, usage-based cost, or any pricing page/calculator for the Voice Agent product; all citations focus on architecture, features, and developer tools.

   ⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram documents an EU-specific endpoint (api.eu.deepgram.com) for customers requiring EU data processing, and a per-request opt-out for model improvement data retention, showing some regional/data-handling control. However, this is limited to EU vs. default (US) with no evidence of broader region selection (e.g., APAC, other jurisdictions) or a general residency configuration option across the platform. Missing for 10: documented list of all available regions, self-serve region selection in dashboard/API config, and independent confirmation of residency guarantees beyond docs.

   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”
   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”

   proof ↗⚑ flag

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

   weight 3 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram documents a per-request opt-out (mip_opt_out=true) from its Model Improvement Program, with opted-out requests not retained, directly addressing training-data use. However, the evidence only shows a per-request flag rather than an account-wide default-off setting, and there's no independent verification that opt-out requests are truly excluded from training. Missing for 10: account/org-level opt-out default, third-party audit or independent confirmation of non-training use, and clarity on default behavior when the flag is omitted.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”

   proof ↗⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to Deepgram Voice Agent
   Retell AI!disputedcontradicted5/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed5/10

   Deepgram documents a per-request opt-out (mip_opt_out=true) that prevents retention, and references data-privacy compliance including an EU-specific endpoint, giving users some retention control. However, there is no documented self-service deletion mechanism for already-retained data, no stated retention periods, and no dashboard/API for managing or purging stored voice data. Missing for 10: explicit data deletion API/console, documented retention duration policy, and confirmation of deletion for non-opted-out data.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent~partialclaimed4/10

   Deepgram documents an opt-out mechanism (mip_opt_out=true) for model improvement/data retention on a per-request basis, which is a form of telemetry/usage-data opt-out, but this is narrowly scoped to training data retention rather than general telemetry/usage tracking (e.g., analytics, product usage metrics). Missing for 10: documentation of a broader telemetry/usage-tracking opt-out setting, confirmation this covers all usage data beyond model-improvement retention, and independent verification that opting out has no side effects.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”

   proof ↗⚑ flag

Call control

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence covers telephony connectivity, function calling, multi-agent handoff between AI agents, and mid-call message injection, but nothing documents a warm/blind transfer to a human agent (e.g., SIP REFER, call transfer function, or context handoff to a live operator).

   ⚑ flag

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

   weight 1 · round drawn
   Retell AI—none0/10

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

   ⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence covers telephony connectivity, function calling, barge-in, and multi-agent orchestration, but nothing documents DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving capabilities.

   ⚑ flag

Campaigns

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

   weight 2 · round drawn
   Retell AI—none0/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Deepgram's docs confirm outbound telephony connectivity (docs-5, docs-18) and per-call features like barge-in and prompting, but there is no mention of campaign-level batch dialing, call scheduling, or throughput/rate controls for outbound campaigns — this is a real-time conversation API, not a dialer/campaign manager.

   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”

   proof ↗⚑ flag

Numbers

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

   weight 3 · round to Retell AI
   Retell AI✓fullprobed8/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Docs confirm Deepgram Voice Agent supports inbound/outbound calls and telephony integration (e.g., via Twilio bridging audio to a single WebSocket), but there is no evidence Deepgram itself provisions or manages phone numbers—developers must bring their own Twilio account/number and build a bridging server. Missing for 10: native phone-number provisioning API, first-party telephony number management, and evidence of outbound call initiation directly through Deepgram's API without a third-party carrier.

   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”

   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 to Retell AI
   Retell AI✓fullclaimed8/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence only documents a generic WebSocket bridge pattern for Twilio (docs-18, docs-8) and a general 'Telephony' feature bullet (docs-5), but there is no mention of SIP trunking, PBX connectivity, or Telnyx number import — the specific mechanisms a platform engineer would need to bring their own carrier.

   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”

   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 Retell AI
   Retell AI~partialclaimed6/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack covers Deepgram Voice Agent's real-time conversation infrastructure (STT/TTS, function calling, telephony, prompting, A/B testing of voices/prompts) but contains no mention of post-call AI review, automated quality scoring, failure flagging, or resolution analysis of completed calls.

   ⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Deepgram is a developer API/infrastructure product; evidence shows mentions of A/B testing metrics (conversion, CSAT, containment rate) as data points but no evidence of an actual built-in dashboard for call analytics, success rates, durations, sentiment, or outcomes that founders can view without building their own.

   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”

   proof ↗⚑ flag

Monitoring

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence of live monitoring dashboards, error-rate alerting, or observability/analytics tooling for production calls; docs cover agent architecture, function calling, telephony, and configuration but nothing about monitoring or alerting on agent misbehavior.

   ⚑ flag

Testing

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence covers live-call features (function calling, prompting, telephony, A/B testing of live configs) but nothing describes a simulated-conversation or eval framework for pre-production testing of agents before real phone calls. Missing for 10: any documentation of a test/sandbox mode, conversation simulation tool, or eval harness for agents.

   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”

   proof ↗⚑ flag

Post call

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack covers live function calling during a call and various agent configuration/telephony features, but there is no mention of post-call structured data extraction (summaries, entities, dispositions) or webhook delivery of such analytics after a call ends.

   ⚑ flag

Tools

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence confirms mid-call function calling exists for the Voice Agent (deepgram-docs-2) but only documents an MCP *server* built into the CLI that lets coding tools access Deepgram APIs (deepgram-docs-13, deepgram-probe-4) — this is the reverse direction, not the Voice Agent acting as an MCP client to pull in external toolsets mid-call. No evidence shows the Voice Agent itself can connect to MCP servers as a tool source.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   Retell AI~partialprobed6/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Docs confirm function calling is a supported feature for performing tasks mid-conversation (deepgram-docs-2), and the agent architecture is a single low-latency WebSocket for audio in/out (deepgram-docs-1) with barge-in and inject-message features (deepgram-docs-8, deepgram-docs-11) suggesting attention to latency/dead-air. However there's no explicit documentation or example describing how function-call latency is masked (e.g., filler speech, streaming partial results) while awaiting an API response. Missing for 10: concrete guidance/examples on avoiding dead air during function execution, sample code showing async function calls with the agent speaking a holding phrase, and independent/hands-on verification of smooth latency handling.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”

   proof ↗⚑ flag

Recording

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   The evidence pack covers Voice Agent architecture, function calling, telephony bridging, LLM providers, and data privacy opt-outs, but nowhere describes an API or mechanism to retrieve full call recordings or persisted transcripts after a call ends. No endpoint, storage feature, or retrieval workflow is documented for this specific capability.

   ⚑ flag

Transcription

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

   weight 2 · round to Deepgram Voice Agent
   Retell AI~partialcommunity4/10

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent~partialclaimed6/10

   Docs confirm real-time transcription via the unified WebSocket API, selectable LLM providers (docs-9), and multilingual STT/TTS model choices (docs-10), giving developers meaningful control over STT and LLM models. However, there is no evidence of 'key terms' or keyword-boosting controls specific to the Voice Agent's transcription pipeline. Missing for 10: explicit key-term/keyword-boost configuration support, independent benchmarking of transcription accuracy.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Supported LLM providers | Parameter | open_ai | anthropic | aws_bedrock | google | groq | nvidia”
   • [claimed-docs] “A multilingual voice agent has two model decisions: which STT model transcribes the user, and which TTS model speaks the agent.”

   proof ↗⚑ flag

Voices

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   No evidence of a voice cloning feature or consent process; docs cover STT/TTS model selection, multilingual voices, and agent configuration but never mention custom voice cloning or consent workflows.

   ⚑ flag

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

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

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

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

   proof ↗⚑ flag

   Deepgram Voice Agent—none0/10

   Evidence confirms TTS model selection exists (docs-10 references choosing a TTS model) but there is no documentation of a broad voice library or of plugging in multiple third-party TTS providers analogous to the multi-LLM-provider list (docs-9). Missing for 10: evidence of voice catalog/library breadth, evidence of multiple supported TTS providers/vendors, and any provider-switching mechanism for voice output.

   • [claimed-docs] “A multilingual voice agent has two model decisions: which STT model transcribes the user, and which TTS model speaks the agent.”

   proof ↗⚑ flag