I Built a CRM That Actually Works for How Developers Think
After years of watching perfectly good CRMs gather dust because "nobody wants to update it," I finally understood why.
The problem isn't the tools. It's that CRMs were built for sales reps, not engineers.
The Developer CRM Problem
Every developer I've talked to shares the same complaints:
"Our engineers spend 3+ hours/week on manual CRM updates"
"Our pipeline data is always stale by the time I see it"
"We built custom integrations but they break constantly"
Sound familiar?
Why CRMs Fail Developers
1. Manual Data Entry is Anti-DRY
As developers, we live by DRY principles. Don't Repeat Yourself.
But CRMs? They're built on repeat-after-me.
- Email a new prospect? Retype everything into the CRM.
- Close a deal? Update 5 fields manually.
- Get meeting notes? Copy-paste into the system.
This isn't just annoying. It violates everything we believe about software design.
2. Stale Data is Unacceptable
In engineering, we have tests, CI/CD, observability. We know when things break.
But in most CRMs? The data is stale the moment you enter it.
By the time a manager sees the pipeline, it's already outdated.
3. Integrations Break Constantly
Custom integrations are brittle. When something changes (API, webhook, auth), everything breaks.
And who's stuck fixing it? The developer.
So I Built Coherence
Not because the market needed another CRM. Because developers needed a CRM that works the way they think.
The core principle: Instead of asking humans to update the CRM, the CRM updates itself.
How It Works
1. AI agents monitor: Email, Calendar, Slack, GitHub
2. Auto-create/update: Contacts, Deals, Tasks, Notes
3. Surface insights: Without any manual data entry
4. Trigger actions: Based on signals, not schedules
The API-First Design
Everything in Coherence is API-accessible:
// Create a contact
const contact = await coherence.contacts.create({
email: 'dev@example.com',
name: 'Sarah Chen',
company: 'TechCorp'
});
// AI agent automatically enriches with:
// - Company data from web research
// - Social profiles
// - Recent activity
// - Engagement history
Integration Architecture
// Webhooks for real-time sync
coherence.webhooks.on('deal.updated', async (event) => {
await syncToExternalSystem(event.data);
});
// Zapier/Make integration
// Native integrations with 100+ tools
// REST API for custom integrations
Real Results
From early customers:
| Metric | Before | After |
|---|---|---|
| CRM data entry time | 3+ hrs/week | 45 min/week |
| Pipeline report accuracy | 60% | 95% |
| Lead response time | 4 hours | 15 minutes |
| Forecast accuracy | 40% | 78% |
What This Means for Developer Teams
- No more manual entry: AI handles data capture
- Real-time accuracy: Pipeline updates automatically
- API-first: Build whatever you need on top
- Your stack, your rules: Works with GitHub, Linear, Notion, etc.
The Technical Stack
For the curious:
- Backend: Node.js + PostgreSQL
- AI: GPT-4 + Claude for agent logic
- Real-time: WebSockets for live updates
- API: REST + GraphQL
- Integrations: Zapier, Make, native webhooks
What I'd Do Differently
If I were building this again:
- Start with the API — The UI should wrap the API, not the other way around
- Make agents configurable — Users should control what the AI does
- Design for failure — Integrations break; handle it gracefully
Try It
I'm not going to pretend this isn't a pitch. It is.
But if you're a developer who's frustrated with CRMs, I think you'll find Coherence different.
What CRM pain point would you pay anything to solve?
👇 Comments welcome.
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