AI integration without
rebuilding the software you already run.
AI connected to your CRM, ERP, helpdesk, and data systems — least-privilege access, rollback paths, and no forced migration you did not ask for.
Your stack,
not a replacement for it.
CRM, ERP, helpdesk, document stores, identity providers, data warehouses, communications platforms, and product APIs. If it has an API or a reasonably current export, it's very likely integrable.
This page covers the systems-integration layer broadly. For foundation-model-specific architecture — providers, routing, structured outputs — see LLM integration.
Four ways in,
chosen by the job.
A dedicated service calls your systems directly — the default for most real-time needs.
AI reacts to events (a new ticket, a new order) instead of polling.
AI capability shipped inside your own product's UI, not a separate tool.
Scheduled processing for high-volume, non-real-time work — often the cheapest option.
Designed for the system
you actually have.
- Authentication — working within whatever auth model the target system actually uses, not an idealized one
- Data contracts — explicit schemas for what moves between systems, versioned when they change
- Rate limits — respected by design, with backoff and queuing rather than brute-force retries
- Idempotency — a retried operation never double-processes
- Rollback — a defined path to undo a bad write before it compounds
- Observability — every write logged with enough context to debug it after the fact
- Blast-radius control — permission scopes narrow enough that a bug can't touch everything
By function,
not by buzzword.
| Function | Typical integration |
|---|---|
| Support | AI reads ticket history and account state from the helpdesk before responding |
| Sales | AI qualifies a lead and writes structured notes back to the CRM |
| Operations | AI extracts data from inbound documents and writes it into the ERP |
| Product | AI feature embedded directly in your own application via internal API |
Least privilege,
by default.
Every integration gets a permission scope narrower than "full access" by default — read where only reading is needed, write scoped to the specific fields a workflow touches. See our Trust & Safety page for how this fits our broader security posture.
Operations, end
to end.
A manufacturing client needed inbound supplier invoices extracted and posted to their ERP without a human re-keying every line item. The integration had three risk points, each handled explicitly rather than assumed away.
Testing ran in three passes before go-live: unit tests against the data-contract schema, integration tests against a staging copy of the ERP (including deliberately replayed/duplicate webhooks to verify idempotency), and a two-week shadow run where the pipeline extracted and validated invoices without posting, so accuracy was measured against real volume before anything touched production data.
Architecture first,
then the build.
We map the data contracts and permission boundaries before writing integration code. Acceptance criteria — test cases, rollback paths, rate-limit behavior — are agreed in writing before launch. At handoff, you get documentation of every connection point, not just working code.
No sales pitch,
just how we work.
We integrate with what you have; a platform change is your call, not ours to bundle in.
A bad write has a defined undo path, not a scramble after the fact.
Access scoped to what each workflow actually needs to touch.
Every connection point documented so your team can maintain it without us.
Before you
book a call.
How is this different from LLM integration?
LLM integration is about foundation-model architecture — providers, routing, structured outputs. AI integration is about connecting an AI feature to the business systems around it. Most projects need both.
Will this require migrating off our current systems?
No. We build on top of what you run via API, event, or batch integration. A platform migration is a separate decision we won't bundle in.
What if the integration breaks something downstream?
We scope write permissions narrowly, test for idempotency, and build rollback paths before anything goes live.
How do you handle rate limits and legacy API quirks?
We design for the constraints of the actual system — rate limiting, retry logic, and graceful degradation are part of the architecture.
What's the acceptance process?
We agree on integration test cases and rollback criteria before build starts, so acceptance is a checklist, not a judgment call.
How do you test for duplicate or retried operations before launch?
Unit tests against the data-contract schema, integration tests against a staging copy of your system with deliberately replayed and duplicate requests, and usually a shadow run — the pipeline processes real volume without writing anywhere — before it's allowed to touch production data.
Tell us which system
needs the AI feature.
Book a 30-minute call — we'll tell you honestly what the integration actually requires.