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.
Free workflow review
Find the workflow worth automating next
Bring one process that is slow, repetitive, expensive, or leaking opportunities. We will map the current workflow, identify the bottleneck, and tell you whether automation is worth pursuing.
No obligation. If automation is not the right answer, we will say so.