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LLM application development
Applications built around your business logic, not around a demo.
What this actually involves
We build end-to-end LLM applications that integrate with the APIs, databases, and auth you already run. The hard part is rarely the model call. It is latency budgets, failure modes, cost per request, and what the interface does when the model is wrong.
- Prompt and context architecture, versioned in the repo
- Model selection benchmarked against your task, not a leaderboard
- Streaming interfaces with real loading and error states
- Cost and latency instrumentation from the first deploy
When it fits
- You have a specific workflow, such as drafting, triage, or search, and can name the person who will use it.
- Your data sits behind your own APIs and auth, and the app must respect who can see what.
- A wrong answer has a cost you can describe, so accuracy targets can be set before the build.
When it does not
- A vendor product already does the job. Buying it costs less than building it.
- Nobody owns the outcome. Without an owner, the pilot stalls after the demo.
- The task needs perfect accuracy with no human review. Current models do not give that.
What we need from you
- Ten to fifty real examples of the input and the answer you want.
- Read access to the systems the app will call, in a test environment.
- One person who can approve scope changes within a day.
Ready to move from AI experiments to AI in production?
A thirty-minute call. We assess the use case, name the risks, and tell you honestly whether we are the right fit.