Know whether the intelligence does useful work.
An impressive demonstration does not establish how an AI system behaves across real work. We help teams define representative scenarios, construct evaluation data, and compare results against explicit acceptance criteria.
Define the result before the benchmark
We start with the job the system performs: extracting a record, answering a question, writing a draft, or completing a tool action. The evaluation distinguishes correctness, evidence, omissions, exceptions, and the amount of review needed. A single average score should not hide an unacceptable failure mode.
Create useful scenario coverage
Test cases should cover ordinary work, ambiguous inputs, missing information, and actions outside the workflow’s authority. We can help construct synthetic records or examples where real data is scarce or inappropriate to share. Generated data is labelled and checked; it is not assumed to represent every production condition.
Compare models and workflows fairly
The same cases and acceptance rules can compare model choices, prompts, retrieval settings, or tool sequences. Results should retain the input, source version, configuration, cost, and outcome so a later change can be assessed. This makes evaluation a repeatable engineering activity rather than a one-time presentation.
Translate evaluation into a release decision
The output identifies accepted behavior, remaining gaps, and cases that need review or a fallback. Canadian buyers can include their own language, access, information handling, and sector requirements in the evaluation brief. We agree the criteria and data permissions before building the test set.
Working deliverables.
A clear next step.
Scope the work around your existing systems, information boundaries, and acceptance criteria. Agree timing, ownership, and commercial terms before delivery begins.
- 01An evaluation plan tied to the business task
- 02A labelled scenario dataset and repeatable checks
- 03An evidence report with gaps and release recommendations
Questions worth asking.
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, latency, and operating cost.
Explore connected capabilities.
Bring the next challenge into focus.
Share the task, current systems, and operating constraints. We’ll help shape a scoped engagement.