Services
Managed AI & adoption
Operate, improve and support enterprise AI systems with quality monitoring, model lifecycle management and practical team training.
Measure AI quality and cost together
Measure AI quality and cost together
A cheaper response is not necessarily a cheaper completed task.
Define the useful outcome
A single business task may trigger retrieval, several model calls, retries and human correction. Allocate cost to the accepted outcome so a low per-call price does not hide expensive failure. Compare approaches using the same representative tasks.
Keep an evaluation set under change control
Version examples, expected behavior and scoring rules. Include edge cases, permitted languages and situations requiring escalation. Review the set when the workflow changes so passing an old test does not become the only definition of quality.
Plan for a model or prompt change
Run regression checks before release, compare meaningful failure categories and preserve a fallback. Monitor changes in latency, unsupported answers and tool behavior after deployment. The operating team needs a way to pause or reverse a problematic update.
Assign ownership after launch
Decide who reviews feedback, approves new sources, updates evaluations and manages incidents. Production AI is a service with continuing responsibilities. A handover should include these people and processes alongside the technical implementation.
Further reading
Common questions
What is the practical starting point?
A single business task may trigger retrieval, several model calls, retries and human correction. Allocate cost to the accepted outcome so a low per-call price does not hide expensive failure. Compare approaches using the same representative tasks.