Your AI prototype works on selected examples, but the team is unsure whether it is ready for a broader release. inAi can evaluate the current system and implement an agreed production-readiness programme.
This is a scoped engineering service, not a certification or a promise that all errors can be eliminated.
Begin with the intended use
Describe the users, the tasks, the data, the consequences of an incorrect output and the workload the application must support. Review the current implementation, existing tests and known incidents.
We separate product uncertainty, data problems, model behaviour and conventional software defects. Each may require a different intervention.
Build an evaluation that reflects the work
Create a versioned test set covering common tasks and important exceptions. Define what passes, who judges it and what evidence supports the judgement. Include unsupported questions, malformed inputs, permissions, unavailable providers and changed source data where relevant.
Assess task success, source support, structured validity, access boundaries, latency and operating cost separately. A single headline score can hide a serious weakness in one of these areas.
Turn findings into engineering work
A review can lead to improvements in retrieval, validation, prompting, model selection, user review, retry logic, timeouts, deployment, monitoring or support procedures. Not every failure requires a different model.
Deliverables can include a findings report, a prioritised backlog, evaluation datasets and scripts, regression tests, implemented fixes and a release recommendation against the agreed acceptance criteria.
Control the next change
Define which tests must run before a prompt, model, source collection or integration changes. Store the relevant version information so a team can investigate a regression rather than compare two undocumented demos.
Include a rollback or disable path and assign operational ownership. A production system needs someone to handle failures after the launch meeting ends.
Evidence without overstating it
Report the tested population, configuration, date and limitations. An observed pass rate on one dataset is evidence about that dataset and setup; it is not a universal reliability guarantee.
Where a key requirement has not passed, the recommendation can be to narrow the scope, retain human review or delay the affected feature while the rest of the system proceeds.
What to bring
An architecture overview, a working test environment, representative inputs, known failure examples, existing logs where sharing is appropriate, and the decision you need to make about release.