Vapi
advancedThe developer platform for building custom voice AI agents.
Vapi is a developer-first platform for building, testing, and deploying custom voice AI agents. Unlike turnkey solutions, Vapi gives engineering teams full control over the voice pipeline, from speech-to-text and language model selection to text-to-speech and telephony integration. It supports custom LLM backends, tool calling, and function execution during conversations, making it ideal for complex use cases that require deep integration with internal systems. PxlPeak uses Vapi for clients that need bespoke voice AI solutions beyond what off-the-shelf platforms offer.
10K+
Developers building on Vapi
<500ms
End-to-end latency
100+
Voice and model combinations
Key Features
Modular voice pipeline with swappable STT, LLM, and TTS providers
Function calling and tool use during live voice conversations
Custom LLM backends including GPT-5.6, Claude, Gemini, and open-source models
WebSocket and REST APIs for real-time conversation control
Built-in telephony with SIP trunking and Twilio integration
Conversation analytics, logging, and debugging tools
Use Cases We Implement
Build custom voice assistants with complex business logic
Create voice-driven interfaces for internal tools and dashboards
Deploy multilingual support agents with custom knowledge bases
Prototype and test voice AI experiences before production deployment
How We Implement Vapi
Assess
We analyze your business needs and how Vapi fits into your workflow.
Configure
Set up Vapi with custom settings, integrations, and data connections.
Integrate
Connect to your existing tools: CRM, helpdesk, email, and more.
Train & Launch
Train your team, document everything, and provide ongoing support.
When NOT to Use Vapi
You need a turnkey phone agent with minimal engineering: Synthflow or Bland.ai get you to production faster with less code.
Your team lacks backend engineering capacity: Vapi requires building function call handlers, webhooks, and custom integrations.
You only need basic appointment booking or FAQ handling: Synthflow's pre-built templates handle this in hours, not weeks.
You want a fully managed service with SLA guarantees: Vapi is infrastructure, not a managed service; you own the uptime.
Integration Patterns We Deploy
Custom Voice CRM Agent
Vapi + Claude API + Salesforce + Twilio + Redis
Caller's number triggers CRM lookup → Claude generates contextual greeting with account history → mid-call tool calls check inventory/order status → results spoken naturally → call summary auto-logged to Salesforce.
Healthcare Intake System
Vapi + GPT-5 + Epic/Cerner API + Twilio + HIPAA Vault
Patient calls → AI collects symptoms, insurance, and scheduling preferences → checks provider availability → books appointment → sends confirmation → all data encrypted and HIPAA-compliant.
Multilingual Support with Model Routing
Vapi + Language Detector + Claude/GPT-5 + ElevenLabs + Zendesk
AI detects caller language → routes to appropriate LLM + voice model → handles support in caller's language → creates Zendesk ticket in English for agent follow-up.
Real-Time Order Management
Vapi + Shopify API + Stripe + Twilio + Slack
Customer calls about order → AI pulls real-time status from Shopify → processes refunds via Stripe → updates order notes → alerts fulfillment team in Slack.
Risks & How We Mitigate Them
Function call latency causes awkward silence
Implement filler responses ('Let me check that for you...') while function calls execute. We pre-warm API connections and cache frequent lookups to minimize wait time.
LLM hallucination in high-stakes conversations (medical, legal, financial)
Use RAG with verified knowledge bases instead of general LLM knowledge. We implement confidence scoring and mandatory human escalation for uncertain responses.
Telephony costs spiral from unoptimized conversations
Long calls multiply per-minute STT + LLM + TTS costs. We optimize conversation flows for brevity, implement early-exit logic, and set maximum call duration limits.
System integration failures during live calls
Backend service outages cause call failures. We implement circuit breakers, graceful degradation (apologize + transfer to human), and health monitoring for all integrated services.
Implementation Checklist
Define your use case complexity: if it's simple booking/FAQ, use Synthflow instead; Vapi is for custom logic
Choose your AI stack: STT provider (Deepgram/Whisper), LLM (GPT-5 is Vapi's default; Claude also works), TTS (ElevenLabs: PlayHT was acquired by Meta and shut down)
Design conversation flows with explicit function calls mapped to each decision point
Build and test all backend function handlers (CRM lookups, booking, payments) before connecting to Vapi
Provision phone numbers and configure SIP/Twilio integration for your telephony setup
Implement filler speech and loading states for any function call that takes >500ms
Set up call recording, transcript logging, and real-time analytics from day one
Configure human fallback paths: every conversation flow must have an escape hatch to a live agent
Load test with 50+ concurrent calls to validate infrastructure scaling
Monitor latency, error rates, and customer satisfaction for the first 2 weeks post-launch
Implementation Guide: Vapi
2-4 weeksVapi is the developer platform for building AI phone agents. It handles the hard parts, telephony, speech-to-text, text-to-speech, and turn-taking, so you can focus on the conversation logic. Think of it as the infrastructure layer between your LLM and the phone system. We've built dozens of Vapi agents and the platform's flexibility is its biggest strength.
Before You Start
Vapi account with API access
Phone number(s): Vapi provides them or bring your own via Twilio
LLM API key (OpenAI, Anthropic, or custom)
Conversation flows documented for each use case
CRM or booking system API access for integrations
Step-by-Step
Design conversation flows
2-3 daysMap out every conversation path: greetings, questions, responses, edge cases, and handoff triggers. The LLM handles natural language, but you need to define the business logic.
Test your conversation flows with real humans first. If a human can't follow the flow, the AI certainly won't.
Configure voice and model
1-2 daysSelect voice provider (ElevenLabs or Deepgram: PlayHT was acquired by Meta and shut down), choose your LLM, and set up the system prompt with personality and rules.
Build function calls
3-5 daysCreate the tools your agent can use: check appointment availability, look up customer records, transfer calls, send SMS confirmations.
Set up telephony
1-2 daysProvision phone numbers, configure call routing, set up voicemail fallbacks, and integrate with your existing phone system.
Start with a dedicated number for AI calls. Don't replace your main business line until the agent is thoroughly tested.
Test extensively
2-3 daysCall the agent yourself. Have others call it. Test edge cases: angry callers, unclear requests, simultaneous calls, background noise.
Deploy with monitoring
1-2 daysGo live with call recording, transcript logging, and performance dashboards. Set up alerts for failed calls or high hang-up rates.
Common Mistakes to Avoid
Skipping conversation design
The LLM is smart but not psychic. Without clear instructions on business rules, appointment logic, and edge cases, it will make things up. Document everything.
Using the wrong voice for your brand
A casual startup voice on a medical practice line feels wrong. Match the voice personality to your brand and audience expectations.
Not testing with real callers
Internal testing catches 60% of issues. Real callers with accents, background noise, and unexpected questions find the other 40%.
Launching without a human fallback
Always configure call transfer to a human agent. Some calls can't be handled by AI and forcing them through creates terrible experiences.
Pro Tips
Use Vapi's server-side events to track conversation state in real-time. This lets you build dashboards showing live call status.
Implement conversation memory across calls. If a customer calls back, the agent should know their history.
Set up A/B testing different system prompts to optimize conversion rates and call duration.
The webhook architecture lets you trigger any backend action mid-call. Use this for real-time inventory checks, price lookups, or CRM updates.
Architecture
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Caller │────▶│ Twilio/SIP │────▶│ Vapi │
│ (Phone) │◀────│ Telephony │◀────│ Orchestrator│
└──────────────┘ └──────────────┘ └──────┬───────┘
│
┌──────────────────────┼──────────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ STT │ │ LLM │ │ TTS │
│ (Deepgram) │ │ (GPT-5.6 / │ │ (ElevenLabs │
│ │ │ Claude) │ │ etc.) │
└──────────────┘ └──────┬───────┘ └──────────────┘
│
┌────────────────────┼────────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ CRM │ │ Calendar │ │ Backend │
│ (Salesforce/ │ │ (Google/ │ │ (Custom │
│ HubSpot) │ │ Calendly) │ │ Webhooks) │
└──────────────┘ └──────────────┘ └──────────────┘Quick-Start Config
{
"assistant": {
"model": {
"provider": "openai",
"model": "gpt-5",
"temperature": 0.3,
"systemPrompt": "You are a helpful receptionist for [Company]. Your job is to: 1) Greet callers warmly, 2) Determine the reason for their call, 3) Book appointments or answer FAQs, 4) Transfer to a human when needed. Always confirm details before taking action."
},
"voice": {
"provider": "elevenlabs",
"voiceId": "your-voice-id",
"stability": 0.7,
"similarityBoost": 0.8
},
"transcriber": {
"provider": "deepgram",
"model": "nova-3",
"language": "en"
},
"firstMessage": "Hi, thanks for calling [Company]. How can I help you today?",
"endCallMessage": "Thanks for calling! Have a great day.",
"serverUrl": "https://your-backend.com/vapi/webhook",
"maxDurationSeconds": 600
}
}Integration Recipes
CRM-Connected Receptionist
Incoming call triggers CRM lookup by caller ID. Claude generates personalized greeting with account history. Mid-call function calls check inventory, book appointments, or process requests. Call summary auto-logged to Salesforce with recording link.
// Vapi server-side webhook handler
app.post("/vapi/webhook", async (req, res) => {
const { type, call, functionCall } = req.body;
if (type === "function-call") {
switch (functionCall.name) {
case "lookupCustomer":
const customer = await salesforce.query(
`SELECT Name, Account FROM Contact WHERE Phone = '${call.customer.number}'`
);
return res.json({ result: customer });
case "bookAppointment":
const slot = await calendar.createEvent(functionCall.parameters);
return res.json({ result: `Booked for ${slot.start}` });
}
}
});Multilingual Voice Router
Vapi detects caller language from first utterance via Deepgram. Routes to appropriate LLM system prompt and ElevenLabs voice model for that language. Handles full conversation in detected language, creates English-language summary for internal records.
Healthcare Intake with HIPAA Compliance
Patient calls in, AI collects symptoms, insurance info, and scheduling preferences. Function calls check provider availability in real-time via Epic API. Books appointment, sends encrypted confirmation. All data flows through HIPAA-compliant infrastructure with BAA-covered storage.
Want us to handle the implementation?
Our team handles Vapi setup, integration, training, and ongoing support.
Get Vapi ImplementedCompare Vapi
Bland AI vs Vapi vs Retell
Bland for speed-to-market, Vapi for flexibility, Retell for latency
Bland.ai vs Vapi
Bland.ai for plug-and-play, Vapi for developer flexibility
Synthflow vs Vapi
Synthflow for fast no-code deployment, Vapi for developer-controlled customization
Vapi vs Retell AI
Vapi for maximum flexibility, Retell AI for the most natural conversations
Vapi Use Cases
Frequently Asked Questions
When should I choose Vapi over Bland.ai or Synthflow?
Choose Vapi when you need full control over the voice pipeline, custom LLM backends, complex function calling during conversations, or integration with proprietary systems. Bland.ai and Synthflow are better for standard use cases like appointment booking and lead qualification.
What technical expertise is required?
Vapi is a developer platform that requires engineering resources to build and maintain. PxlPeak provides the technical team to architect, develop, and deploy Vapi-based solutions so you get custom voice AI without needing in-house AI expertise.
Can Vapi use our own AI models?
Yes. Vapi supports custom LLM backends, including self-hosted open-source models, fine-tuned models on Azure or AWS, and any provider accessible via API. PxlPeak configures the optimal model stack for your latency and accuracy requirements.
How long does a custom Vapi deployment take?
PxlPeak builds custom Vapi voice agents in 2-4 weeks, including architecture design, LLM selection, conversation flow development, telephony integration, and production hardening.
Other AI Voice & Phone Tools
Related Reading
Vapi in Action
Composite Model: Cutting Med-Spa Missed-Appointment Revenue Loss With an AI Voice Receptionist
How PxlPeak Scaled Operations with AI Automation
Pita House LLC: Building a Scalable Digital Infrastructure for Multi-Location Success
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