Software
OpenAI Integration
Model calls are the easy part; evaluation, cost control and failure behaviour are the engagement.
We build production integrations rather than demos: retrieval over your data, structured outputs the receiving system can validate, an evaluation set that catches regressions when a model version changes, caching and routing to control cost, and defined behaviour when the provider is unavailable.
- 01
Retrieval architecture over client data
- 02
Structured outputs with schema validation
- 03
Evaluation sets and regression testing
- 04
Cost control through caching and model routing
- 05
Fallback behaviour and rate-limit handling
FAQ
How do you control API cost?
Route simple tasks to smaller models, cache repeated retrievals, cap context aggressively and monitor cost per transaction rather than per month.
What happens when a model version changes?
The evaluation set is re-run before any upgrade, so regressions are caught in testing rather than by a customer.
Can we use a different provider?
Yes — we build behind an abstraction so the provider can be changed without rewriting the application.
Claude AI Consulting
Strong on long documents and careful drafting — which suits regulated Canadian workflows.
AI Assistants
Assistants grounded in your own material, with citations, permissions and a defined refusal behaviour.
Custom Software Development
Software written only where the market does not already sell the answer.