Retell AI is a voice agent platform for building AI that handles phone calls — inbound and outbound. Its distinguishing feature is a no-code agent builder that lets non-developers configure and deploy voice agents without writing code, combined with a comprehensive analytics dashboard for monitoring call performance.
Voice Agents
What Retell AI does
Retell AI builds voice agents that handle phone calls. Like Vapi, it combines speech-to-text, a language model, and text-to-speech into a real-time pipeline that can hold a natural phone conversation and take actions during it. The key differentiator: Retell puts more emphasis on the no-code configuration experience and conversation analytics.
Where Vapi is more developer-focused (API-first, highly configurable, technically flexible), Retell AI is more accessible to operators and business users — the agent builder is visual, the analytics dashboard is more detailed out of the box, and the setup process requires less technical knowledge to get a first agent working.
Retell vs Vapi — the practical difference: Both can build the same voice agents. Retell is easier to configure without engineering support. Vapi gives more low-level control and flexibility. For teams with a developer available, Vapi offers more customisation. For teams without, Retell gets you to a working agent faster.
Core capabilities
No-code agent builder — configure agent behaviour, conversation flow, and responses visually without writing code
Custom LLM support — use OpenAI, Anthropic, or your own fine-tuned model
Real-time function calling — connect to external systems during the call (CRMs, booking systems, databases)
Batch outbound calling — upload a contact list and launch outbound campaigns at scale
Conversation analytics — detailed dashboard showing call outcomes, sentiment, common topics, and agent performance metrics
Multi-language — support for major global languages with auto-detection
Building an agent in Retell AI
The Retell dashboard guides you through agent creation: define the agent's persona and purpose, configure the conversation flow (what it says to open, what questions it asks, how it responds to common inputs), connect any external tools, select voices, and test with a live call before deploying. The visual flow builder makes it easier to model branching conversations than writing a single long system prompt.
Configure a customer service agent
I want to build a Retell AI agent for customer service at [company type]. The agent should handle: [list 4-5 common query types]. For each query type, describe: the information the agent needs to collect, where it gets the answer (knowledge base / database lookup / fixed response), and when to escalate to a human. Also specify the agent's tone: [professional / friendly / empathetic] and the opening greeting.
Build an outbound survey agent
Design a Retell AI outbound survey agent that calls [target audience] to ask [number] questions about [survey topic]. For each question: write the exact wording, specify if it is multiple choice or open-ended, and describe how to record the answer. The agent should: introduce the survey honestly, keep the call under [X] minutes, thank respondents, and handle call-backs if the respondent asks to continue later.
Analyse call performance
I have been running a Retell AI voice agent for [time period]. Explain: (1) which metrics in the Retell analytics dashboard I should focus on, (2) what good vs poor performance looks like for each metric, (3) how to identify the most common points in the conversation where calls are going wrong, (4) what changes to the agent configuration would likely improve performance based on the patterns.
Set up a knowledge base for the agent
I want my Retell AI agent to answer questions accurately about [business/product/service]. I have the following source documents: [describe — e.g. a FAQ page, a product manual, pricing tables]. Help me: (1) structure the knowledge base for a voice agent (voice needs different formatting than text — shorter answers, no bullet points), (2) handle questions the knowledge base doesn't cover, (3) keep the knowledge base updated when information changes.
Design conversation flow for complex scenarios
My voice agent needs to handle this complex scenario: [describe — e.g. a caller who wants to change an existing order but the order is already shipped / a caller who is upset about a billing error]. Design the full conversation flow: what the agent says at each step, how it handles the caller's likely responses, what information it needs to collect, what actions it takes, and when it escalates. Write this as a decision tree.
Integrate with a CRM or booking system
I want my Retell AI agent to look up and update records in [CRM/booking system name — e.g. Salesforce / HubSpot / Calendly / a custom API]. During a call the agent needs to: (1) look up [what — e.g. the customer's account details / available appointment slots], (2) create or update [what]. Describe the API integration setup, the data the agent needs to pass, and how to handle cases where the lookup fails or returns no results.
Compare Retell to building without a platform
My team is deciding whether to use Retell AI or build our own voice agent from scratch using Twilio + OpenAI + ElevenLabs. We need [describe requirements — e.g. X calls/day, Y languages, specific CRM integrations]. Give me an honest comparison of: total cost of ownership over 12 months, time to first working agent, ongoing maintenance burden, and flexibility for future changes.
Handle edge cases and failure modes
What are the most common ways a voice agent fails in real production use? For each failure mode, explain: why it happens, how to detect it in Retell analytics, and how to fix it in the agent configuration. Cover: caller silence or inaudible audio, caller speaking a language the agent doesn't support, caller asking questions outside the agent's knowledge, repeated misunderstandings, aggressive or upset callers, and technical call drops.
Retell AI's technical stack
Retell AI runs on a WebRTC-based real-time audio pipeline similar to Vapi. The platform supports multiple STT providers (Deepgram is the primary default, noted for lowest latency), multiple LLMs (GPT-4o, Claude 3.5, Gemini, and custom model endpoints), and multiple TTS providers (11Labs, OpenAI TTS, Cartesia, Deepgram TTS). The per-minute pricing reflects the underlying model costs plus Retell's orchestration layer.
Conversation flow vs prompt-based configuration
A key architectural difference between Retell and Vapi: Retell offers both a system-prompt approach (write a detailed prompt describing the agent) and a visual conversation flow builder (define explicit nodes and transitions for each conversation state). The flow builder approach is more deterministic — the agent follows the defined paths — while the prompt approach is more flexible but less predictable. For high-stakes or regulated use cases (healthcare, financial services), the flow builder produces more auditable, testable behaviour.
Analytics architecture
Retell's analytics pipeline processes call recordings and transcripts post-call to extract: sentiment scores, topic classification, task completion detection, and key entity extraction (dates, names, account numbers mentioned). These are computed using a separate NLP pipeline rather than the real-time LLM, enabling richer analysis without adding to call latency. The analytics API allows exporting data to external BI tools.
Compliance features
Retell provides: call recording consent message injection (plays a "this call may be recorded" disclosure automatically), PII redaction in transcripts (masks phone numbers, credit card numbers, and other identifiers), and data residency options for regulated industries. HIPAA compliance configuration is available on Enterprise plans. Full compliance documentation at docs.retellai.com/compliance.
Source note: Pricing from retellai.com/pricing. Technical specifications from Retell AI documentation at docs.retellai.com. All verified April 2026.