Build AI capabilities into your product.

• It works on selected examples, but results vary with real user inputs.
• It needs access to product data or tools without bypassing existing permissions.
• The experience breaks down when an answer is wrong, incomplete, slow, or unavailable.

A useful starting point is one task with a clear definition of success. For a product assistant, that includes which records it can read, which actions it can take, and when it must ask a person to review the result.

• An integrated capability connected to agreed data sources and permitted actions.
• Repeatable evaluations covering useful responses, failure cases, latency, and cost.
• A release plan with human review points, fallback behavior, and an explicit operating handoff.

The scope is built around the user’s task and your product constraints. It can include a natural-language interface, an information-processing workflow, or an agent that uses defined tools.

We agree acceptance criteria and representative examples before implementation, then test both successful use and failure cases. Data access and tool permissions are defined before systems are connected. Code review, automated tests, and evaluation take place in a separate test environment. Before release, we agree approval, rollback, monitoring, and who will operate the capability.

This work fits product leaders with a concrete user need, access to the relevant people and systems, and someone who can judge whether the result is useful. It is not a fit for unrestricted autonomous access or a launch that skips evaluation and release approval. Where conventional software better serves the task, that tradeoff belongs in the scope discussion.

Do we need a finished prototype?

No. We can start with the user task, available data, and constraints, or assess an existing demo. The first step is defining what is worth building and how useful progress will be judged.