Peake.ai: We Built Our Own AI Phone System in 1 Hour

Industry: AI Communications

The Origin Story

It was a Monday afternoon when I finally snapped. Another $300 bill from our VoIP provider for a service that barely worked, dropped calls during demos, and had a UI that looked like it was designed in 2003. I turned to my team and said, "We build AI systems for a living. Why are we paying someone else for this?"

Two hours later, Peake.ai was born.

The Challenge

Like many small businesses, we were paying too much for VoIP services that didn't fit our needs:

• $300+/month for basic calling features
• No AI integration - every call had to be manually logged
• Poor call quality - dropped calls during important demos
• Clunky interface - took 6 clicks to make a call
• Zero automation - transcripts? summaries? dream on.

We needed something better. Something that fit the way we actually work.

Our Approach

Hour 1: The MVP

We started with the basics:

• Twilio for the telephony backbone (reliable, affordable)
• Next.js for the dashboard (what we know)
• PostgreSQL for call logging (already running)

In 60 minutes, we had:

• Inbound/outbound calling
• Basic call routing
• Simple web interface

It wasn't pretty, but it worked. And it was ours.

Hour 2: The AI Layer

This is where it got interesting. We connected LangChain to handle:

• Real-time transcription - Every call transcribed as it happens
• Automatic summaries - AI generates call notes in seconds
• Intent detection - Classify calls by purpose automatically
• CRM sync - Call data flows directly to our systems

Technical Stack

Component	Technology	Why
Telephony	Twilio	Reliable, affordable, great API
Backend	Next.js API Routes	Fast to build, easy to deploy
Database	PostgreSQL	Already using it, handles JSON well
AI	LangChain + GPT-4	Orchestration + intelligence
Hosting	Vercel	Zero-config deployment

Key Code Decisions

1. WebSocket for real-time - Instant transcription updates
2. Edge functions - Low latency for voice handling
3. Streaming responses - AI summaries appear as they generate
4. Webhook architecture - Twilio events trigger our workflows

Results

Cost Savings

Before	After	Savings
$300/mo VoIP	$47/mo Twilio	$253/mo
$0 (manual notes)	$0 (AI included)	Hours saved
Total		$500+/mo value

Productivity Gains

• 100% of calls automatically transcribed
• 30 seconds to get call summary (was 10+ minutes of note-taking)
• Zero manual CRM entry
• 90% reduction in "who was that call with?" moments

Quality Improvements

• Better call quality than our previous provider
• Instant search across all call transcripts
• AI-suggested follow-ups based on call content
• Automatic sentiment analysis for sales calls

What We Learned

1. Build What You Need

We didn't build a phone system. We built our phone system. Features we actually use, nothing we don't.

2. Start Ugly

V1 looked terrible. It didn't matter. It worked, we could iterate, and we learned what actually mattered by using it.

3. AI Integration from Day One

Building AI-native beats retrofitting every time. We didn't add AI to a phone system - we built a system where AI was assumed from the start.

4. Iterate Weekly

Every Monday, we ship a new feature or improvement. This week: automatic meeting scheduling from call intent.

What's Next

We're adding:

• Multi-channel - SMS, WhatsApp, email from one interface
• Voice AI - Agent that can handle initial call screening
• Integrations - Slack notifications, calendar sync
• Team features - Call transfer, team analytics

Want Your Own?

We built this for ourselves, but the approach works for anyone. The key insights:

1. Modern telephony APIs (Twilio, etc.) are mature and affordable
2. AI tooling (LangChain, etc.) makes intelligence accessible
3. You don't need a massive team - we built V1 with 2 people
4. Custom beats generic when your needs are specific

If you're paying too much for tools that don't fit, maybe it's time to build your own.

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This is how we work at Virgent AI: identify friction, build fast, iterate constantly. Sometimes the best solution is the one you make yourself.

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Originally published on Virgent AI Case Studies