AI SolutionsService
Evaluating an AI system before you deploy it
The test dataset, the scoring method and what the score has to mean.
Kaelo Global is a Dubai company licensed in Meydan Free Zone, with over 100 clients served so far.
At a glance
What it is
A demonstration is a chosen sample shown by someone who wants a yes.
A principal, Kaelo Global AI solutions
Most AI projects are judged by demonstration, which is the one method almost guaranteed to produce a favourable result. A demonstration is a chosen sample shown by someone who wants a yes. An evaluation is a fixed dataset, a scoring method agreed in advance and a number that can go down as well as up. The second is less exciting, and it is the one that predicts what will happen in production.
A useful AI evaluation harness has three parts: a dataset built from real inputs including the difficult ones, a scoring method chosen before anyone sees the results, and a way to run the same test again whenever the model, the prompt or the retrieval changes. Without the third part, you learn about problems from your users.
We build evaluations for systems that feed real decisions, such as document processing, customer replies and internal search, and we are happy to test a system that someone else has built.
What's included
Six areas of scopeA test dataset that reflects reality
Built from real inputs, including the difficult and ambiguous ones, labelled by people who know the subject and then frozen. A dataset chosen to make a system look good is worse than none at all, because it creates false confidence.
Scoring chosen before results are seen
Exact match, similarity of meaning, human rating or scoring by another model. Each is right somewhere and misleading elsewhere, and choosing after seeing the output is how evaluations turn into justifications.
Errors measured instead of asserted
For systems that answer from retrieved documents, the question is whether each answer is supported by the material it retrieved. That can be measured, and it should be reported as a number instead of as reassurance in a proposal.
Repeat testing as the system changes
Models are updated, prompts are edited and retrieval is tuned. Without a test you can run again, a decline in quality reaches you through your users.
Failure analysis
A close look at the cases the system gets wrong, grouped by type, which usually reveals a few specific problems to fix instead of a general need for a better model.
Human review where it matters
Clear rules for which outputs a person checks before they are used, based on the cost of a mistake instead of on convenience.
How the work runs
Scope the task
What the system must do, and what would count as failure.
Build the dataset
Real inputs, labelled by people who know the subject, with difficult cases included.
Agree the scoring
The method and the threshold, written down before any results exist.
Run, report and run again
Including after every later change to the system.
When to come to us
- 01You are being asked to approve an AI deployment on the strength of a demonstration.
- 02A system is already live and nobody can say whether it is getting better or worse.
- 03The output feeds a decision where being wrong has a real cost.
- 04Two providers are competing for the same work and you want them compared on the same test.
What we do not do
- Evaluating on a dataset the builder assembled.
- Reporting a single headline accuracy figure without showing how the difficult cases performed.
- Publishing benchmark figures from someone else's system as though they were yours.
Common questions
