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Vapi

advanced

The 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.

Implementation: 2-4 weeks
Pricing: Usage-based (per-minute STT + LLM + TTS costs) / Custom enterprise plans
Official site

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-4, 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

1

Assess

We analyze your business needs and how Vapi fits into your workflow.

2

Configure

Set up Vapi with custom settings, integrations, and data connections.

3

Integrate

Connect to your existing tools — CRM, helpdesk, email, and more.

4

Train & Launch

Train your team, document everything, and provide ongoing support.

Implementation Guide: Vapi

2-4 weeks

Vapi 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

1

Design conversation flows

2-3 days

Map 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.

2

Configure voice and model

1-2 days

Select voice provider (ElevenLabs, PlayHT, Deepgram), choose your LLM, and set up the system prompt with personality and rules.

3

Build function calls

3-5 days

Create the tools your agent can use: check appointment availability, look up customer records, transfer calls, send SMS confirmations.

4

Set up telephony

1-2 days

Provision 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.

5

Test extensively

2-3 days

Call the agent yourself. Have others call it. Test edge cases: angry callers, unclear requests, simultaneous calls, background noise.

6

Deploy with monitoring

1-2 days

Go 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.

Want us to handle the implementation?

Our team has deployed Vapi for dozens of businesses. We handle setup, integration, training, and ongoing support.

Get Vapi Implemented

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.

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