AI Governance
AI governance should be proportional to the impact of the workflow, the data involved, and the consequence of a wrong output.
What we run by default
Use-case definition
Define the decision or task, acceptable output, owner, data inputs, and failure consequence before selecting a model.
Provider selection
Choose models and providers based on quality, data terms, latency, cost, and the client's risk requirements.
Human review
Keep a human approval step where an AI output can materially affect a person, payment, contract, or regulated process.
Evaluation
Agree on representative test cases and acceptance criteria before treating a model workflow as production-ready.
Change control
Material changes to prompts, models, tools, or decision logic should be tested against the same important cases.
Ownership
Every production AI workflow should have a named business owner who can pause or change it.
30-minute working session
Find the highest-ROI automation in your business
Bring one workflow that is slow, repetitive, or leaking opportunities. We will map the bottleneck, the systems involved, and whether automation is actually worth implementing.
No obligation. If automation is not the right answer, we will say so.