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
What we can scope together
- A focused product flow or AI feature with a usable interface
- APIs, data models, and integrations around the AI component
- An agreed evaluation and deployment path for the first release
Final deliverables depend on the agreed scope.
What the project needs from you
Bring the intended users, the task they need to complete, existing software constraints, and examples of acceptable outputs. For an MVP, we choose one complete user journey before expanding the feature list.
Keep people in control
Design authentication, permissions, timeouts, rate limits, and error states alongside the model interaction. The interface must distinguish a generated suggestion from a confirmed action.
Can we start with an AI MVP?
Yes. A useful first version tests one user need with a bounded workflow and explicit acceptance criteria. Integrations, data readiness, and review requirements determine scope; a model demo alone does not validate the product.
Define, build, evaluate, hand over.
We first agree on the workflow and acceptance criteria, implement a bounded version against representative inputs, review errors and edge cases, then decide what is ready for release. Deployment, documentation, ownership, and ongoing support are agreed explicitly.
Start with an architecture consultationWhere this shows up in the work
See it running
The projects and case studies show this work in production, with the architecture and the results.