Use cases / AI quality & cost monitoring
Track AI quality, response time, and cost as your system changes
Evaluate one AI workflow against representative tasks. Compare results over time and review failures, response time, and cost per successful task.
Illustrative example; not a completed customer project.
- 01Define useful results
- 02Record a baseline
- 03Compare changes
- 04Review failures
Use cases
Workflow steps
Representative evaluation cases
Agree permitted inputs, expected results, field checks, source requirements, and examples that require human judgment.
Quality, speed, and total task cost
Measure successful and failed runs, response time, usage, retries, and review effort. Compare configurations against the same tasks.
Changes that need attention
Repeat agreed tests after model, prompt, retrieval, or data changes. Inspect regressions and record the limits of each recommendation.
When an AI feature changes
A working AI feature can produce different results after a configuration or data change. Establish what a successful task means before comparing quality, response time, or spend.
Illustrative workflow
Example: reviewing a financial document assistant
A proposed evaluation compares two configurations on approved reports. Checks cover extracted amounts, reporting periods, currencies, and source references. A reviewer inspects failures before recommending whether the new configuration should proceed.
Define access, test coverage, and review frequency
Start with a paid assessment of the workflow, available logs, data rights, and test cases. Agree which checks can run automatically and which need a domain reviewer. A passing test set establishes results for those cases and conditions.
Agree the review schedule, usage budget, failure thresholds, and operating owner. Model changes, production deployment, and continuous incident response require explicit responsibilities and scope.
What you receive
What we can deliver
A versioned evaluation set, baseline results, and acceptance criteria.
A comparison report with failures, response times, task costs, and known limitations.
Scheduled reviews and recommendations for separately agreed tuning or integration work.
Related services and examples
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Scope your AI evaluation
Describe one AI workflow, its users, available evaluation examples, and the quality or cost problem. We can define a baseline assessment and scheduled reviews.
Discuss AI quality and cost