Custom AI development, built into the systems you use.

What we build

Knowledge assistants with source references, document classification and extraction, drafting tools with review, and search or AI features embedded in existing applications. An AI project includes the surrounding software: permissions, data preparation, interfaces, deployment and support ownership.

We define what the system may answer or do, which actions require approval and what happens when a source is missing. A fluent response alone is not an acceptance criterion.

A delivery process with measurable checkpoints

1. Define behavior: identify users, representative tasks and unacceptable errors.
2. Test the uncertain part: use a bounded prototype to assess retrieval, extraction or tool use.
3. Build the application: implement access controls, integration, review and failure handling.
4. Validate and deploy: evaluate agreed cases, stage the rollout and document known limits.
5. Hand over: record ownership of hosting, data refreshes, evaluation and future changes.

Evaluation and operational controls

Keep a fixed evaluation set covering normal questions, missing information, conflicting sources and unauthorized requests. Inspect evidence quality as well as answer quality. Test retrieval and access filtering separately so a good-looking answer cannot hide a permission error.

Version prompts and configuration, record useful operational signals and define a fallback when dependencies fail. Agree on what may be logged, how long it is retained and who can inspect it.

What the engagement delivers

The agreed application and integrations, deployment configuration, evaluation cases and results, technical documentation and an operating handover. Model fees, hosting costs, maintenance and ongoing development capacity are defined in the scope.

Projects are quoted after scoping. If the business decision is still unclear, begin with AI consulting. If AI adds little value, custom business software may be the better route.

See the approach in detail

Read the knowledge assistant engineering walkthrough and the guide to evaluating internal AI assistants. The walkthrough is an illustrative design, not a customer performance claim.

Updated 2026-10-28.