AI / Integration
Custom Integration
AI in your systems is an architecture problem, not API glue. The model is the easy part; the system around it decides whether it survives.
Why the glue version fails
Calling an API is an afternoon. Then production arrives: the model needs customer data the permission model was never asked about; latency lands on a user-facing path with a 200ms budget; the provider changes model behaviour under your feet; compliance asks what exactly was sent where, and no one logged it. Every one of those is an architecture decision that got made by default.
What we design deliberately
- Data paths. What leaves your boundary, what gets masked or retrieved instead of sent — designed with the same jurisdiction awareness as our sovereign work.
- Permission inheritance. The model acts on behalf of a user; its access must collapse to that user’s, not to the service account’s.
- Latency and cost budgets. Caching, routing between model tiers, and graceful degradation when the provider has a bad day — because it will.
- Fallbacks. Every AI feature carries its non-AI path: the queue for human review, the deterministic default, the honest error. Features without fallbacks are incidents on a timer.
- Evaluation in CI. Behaviour is pinned by test sets, so a silent model update doesn’t become a silent product change.