CRM Integration Platform
Contact center integrations.
The problem
Customer data lives in silos. Contact centers and CRMs rarely speak the same language, so agents copy-paste between tabs and AI has no clean data to work with.
Context
This platform is the connective tissue beneath the AI contact center — without reliable, normalised integrations, the smart layer on top has nothing to stand on.
Architecture
A normalised integration layer abstracts each CRM behind a common model, with a sync engine combining webhooks and polling, OAuth-based connection management, and an event bus feeding downstream AI workflows.
- Common data model across heterogeneous CRMs.
- Hybrid webhook + polling sync for reliability.
- Secure OAuth connection lifecycle.
- Event bus that powers the AI layer above.
Technical challenges
Every CRM is a snowflake
APIs differ wildly in shape, rate limits, and reliability. The normalisation layer absorbs that chaos so the rest of the system stays simple.
Engineering decisions
Normalise at the edge
Translating each integration into a common model at ingestion keeps downstream logic vendor-agnostic.
Technologies
Results
A dependable data backbone that turns fragmented customer data into a clean, real-time stream the AI contact center can act on.
Lessons learned
- Boring infrastructure is what makes the exciting layer possible.
- Abstractions earn their keep when vendors misbehave.
What I’d improve today
- A self-serve connector SDK for adding CRMs without core changes.