Know what an AI evaluation claim really proves
Share one benchmark, accuracy claim, or vendor evaluation. We’ll tell you whether it applies to your use case, and what you should test next.
Trusted by electronics leaders running AI agents in mission-critical environments
A clearer basis for your decision
We’ll assess the claim you share and explain what it supports, what it does not establish, and what to evaluate next.
What the score proves
What was tested, and which conclusions the result reasonably supports.
What it leaves unanswered
Whether the test cases, evaluator, and scoring method reflect your actual business use case.
What to test next
The additional questions or evaluation cases you should request before making a decision.
One claim. One focused review. A clearer basis for your decision.
A plain read on one claim
Your review separates what the evidence shows from what it only suggests, then turns the gaps into tests you can ask for.
Claim reviewed
“Our agent answers product questions with 94% accuracy.”
- Run 50 real questions from your own support queue
- Have a domain expert grade a sample of the judge’s verdicts
- Add questions whose answer lives in a datasheet table
Example shown for illustration. Your review will be based on the claim you share.
As our ecosystem grows, developers and customers need fast, reliable access to the knowledge required to build, optimize, and deploy on DEEPX. Rapidflare enables us to deliver that experience while maintaining the accuracy, traceability, and trust required in mission-critical technical environments.
Tim ParkStrategic Marketing Director, DEEPX 
Have a score or claim in front of you?
Send it over. A sentence or a report link is enough to get started.
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Keep reading: Your LLM judge is another model to test Designing evaluation cases that reflect real use