Expertise
Full Stack AI Products
Most AI features fail on the parts that are not AI. Someone has to design the schema, handle the request that times out, and make the interface explain what the model is doing. Building the whole stack means those seams are decided rather than inherited.
In practice
In practice: AI products built from an empty repository, and AI features added to software that already has users and cannot go down.
What it gets used for
- AI products built from scratch
- Adding AI to an existing product
- Dashboards and internal tools
- REST and GraphQL APIs
Stack
ReactNext.jsTypeScriptFastAPIPostgreSQLAWS
See it running
The projects and case studies show this work in production, with the architecture and the results.