Check an AI workflow
before your team relies on it.
We prepare task-specific test cases, repeatable checks, and a report of failures and remaining gaps. Synthetic examples can cover missing or difficult cases. Your team agrees the release criteria before testing begins.
Define the result before the benchmark
Define the task: extracting a record, answering a question, drafting text, or using a tool. Check correctness, supporting evidence, missing information, failures, and review effort. Record serious failures separately from average scores.
Create useful scenario coverage
Cover routine work, unclear inputs, missing information, and unauthorized actions. Where real data is scarce or unsuitable to share, create synthetic examples. Label generated data, check it, and document what conditions it represents.
Compare models and workflows fairly
Use the same cases and criteria to compare models, prompts, search settings, or tool sequences. Retain inputs, source versions, settings, costs, and results. Your team can repeat the checks when the system changes.
Translate evaluation into a release decision
Report what passed, remaining gaps, and cases needing review or a fallback. Include your language, access, information handling, and sector requirements. Agree criteria and data permissions before preparing the test set.
What your team receives.
Agree what we will build, how it will be checked, and what your team receives. We confirm timing, access, and commercial terms together.
- 01An evaluation plan tied to the business task
- 02A labelled scenario dataset and repeatable checks
- 03An evidence report with gaps and release recommendations
Build tests from the task
Start with representative examples selected by people who know the work. Record what an acceptable output contains and which errors matter most. Generated examples can exercise unusual formats or misleading inputs, but should be labelled and kept separate from real-world validation examples.
Inspect important failures individually
An average score can hide an expensive error. We review missed exceptions, unsupported statements, permission failures, and excessive correction effort. The report connects each failure to its input, observed output, expected behaviour, and source or workflow version.
Use results for a release decision
The scope can deliver an evaluation set, repeatable checks, a failure report, and a recommendation against acceptance criteria. Improvements are tested on held-out examples where practical. The team can narrow the scope, increase review, or delay release when evidence does not support wider use.
Common questions
Does synthetic data remove privacy concerns?
No. The generation process, source material, similarity to real records, and permitted uses still need review. We scope those questions with the data owner.
Can evaluation compare model providers?
Yes. We can compare providers or configurations using agreed scenarios, task quality, review effort, response time, and operating cost.
Related services
Tell us what you need to improve.
Describe the task, the tools you use, and the result you need. We’ll help plan the first step.