What do we build?
Systems that retrieve relevant knowledge, interpret documents and coordinate controlled workflows across existing applications. The deliverable is an engineered system: data pipelines, model integration, application logic, evaluation and operational controls. We choose between retrieval, agents and conventional software according to the task; building an AI system does not necessarily mean training a new foundation model.
How do we establish that it works?
We define representative inputs, expected outcomes and unacceptable failures before selecting the architecture. Evaluation covers answer quality, source traceability, permissions, latency and operating cost. When evidence is missing or an action needs approval, the workflow must have an explicit route back to a person.
What happens after the prototype?
We agree integration boundaries, deployment requirements, monitoring and responsibility for updates. A useful prototype becomes a production candidate only after testing against the agreed acceptance criteria. Scope and price follow discovery; data residency and model providers are selected for the requirements of the project.
- Architecture and integration design
- Evaluation against agreed business criteria
- Documented controls, deployment and handover