Sometimes the right answer is not an automation attached to an existing tool, but a new application designed around a specific AI capability.
We start with the capability itself: what the model has to do reliably, on which inputs, and how its output will be checked. Once that holds up on real examples, we build the application around it, from interface and accounts to deployment, until it is a tool people use every day.
What we deliver
- AI product discovery and MVP scoping
- Interface and experience design
- Application development
- Model integration with structured outputs
- Accounts, roles and data storage
- Usage limits and cost tracking
- Evaluation sets and testing
- Deployment, monitoring and iteration
Technology & implementation
Underneath the AI feature is an ordinary, well-built web application: accounts, a database, permissions, file storage, billing if needed. That part decides whether the product is reliable.
Model calls run on the server, never from the browser, behind a provider layer so the model can be changed as prices and capabilities move. Outputs the application depends on are requested as structured data and validated before use. Each call is logged with its cost, and per-user limits keep spending predictable.
Before each release, changes to prompts, models or data are checked against a set of real examples.
Example use
An illustrative scenario, not a description of a completed project.
A business needs a secure web application where employees upload documents, search their content, compare selected information and generate structured drafts for review.