Who this is for
For teams connecting model APIs to an existing product or developing a constrained AI-assisted workflow. We begin with the task and its success criteria: what information the model can use, what actions are permitted, what must be verified, and what happens when a provider fails. We distinguish a research hypothesis from a feature that has been measured in the product. A generated answer is not automatically a successful outcome.
What we build
A useful scope
- Provider adapters, bounded tool access and structured outputs
- Routing, retries and explicit user-facing fallbacks
- Task-specific evaluation sets, observable costs and failure analysis
The exact scope, acceptance criteria and delivery boundaries are agreed after reviewing your product. These capabilities describe the work we can discuss; they are not a promise of a fixed package.
How delivery works
From context to delivery
- Define a narrow task, evaluation examples and acceptable failure behavior.
- Integrate one complete workflow with bounded inputs and actions.
- Measure results against the examples before expanding providers or autonomy.
Technical choices
Architecture and tradeoffs
A hosted model API can remove the need to operate model infrastructure. It does not remove the need for evaluation, privacy boundaries, budget limits or explicit action permissions. We keep deterministic validation around generated data and avoid promising a benchmark result before running an appropriate evaluation.