Define the useful action
We replace “add AI” with a specific job: draft a reply from approved business knowledge, turn an idea into an editable artifact, summarize a bounded record, or help a user complete a task.
AI with a job, context, and a way to fail safely.
We build AI around a clear responsibility inside a product. The model gets the right context, the interface makes uncertainty understandable, and a person remains in control where mistakes matter.
Discuss your productAI is one component of a product. The surrounding data, workflow, evaluation, permissions, and recovery path usually determine whether it becomes useful.
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What the work requires
Ground the response
We design how the system retrieves approved context, cites or exposes its basis, separates customer data, and handles missing information. Good grounding reduces confident invention.
Give people control
Review, edit, approve, pause, retry, and escalate are product features. We place them where the cost of an incorrect answer or action justifies human judgment.
Evaluate in the workflow
Model quality is tested against representative tasks and failure cases, then observed after release. Accuracy alone is insufficient if the system is slow, confusing, expensive, or hard to correct.
A useful starting scope
Make the responsibility visible.
- AI use-case and risk definition
- Prompt, retrieval, and tool workflow
- Product interface and controls
- Evaluation set and release checks
- Monitoring and iteration plan
Questions before the workClear answers
Clear answers
make better briefs.
Do you train custom models?
Most products get more value first from strong context, tools, evaluation, and interface design around an existing model. We consider fine-tuning when evidence supports it.
Can AI use private company data?
Yes, with deliberate data boundaries, provider choices, access rules, and retention settings. We define those constraints before connecting sensitive sources.
How do you control hallucinations?
We narrow the task, ground it in approved information, require structured outputs where useful, test known failure cases, and provide a safe fallback or human review.