Products / Benchmark Lab
DNLA Benchmark Lab
Most systems get evaluated on random questions or gut feel. Benchmark Lab builds a benchmark custom to your system: the specific questions, edge cases, and scenarios that actually matter for what your AI is supposed to do.
Why generic benchmarks fail
Public leaderboards don't test your problem
A model that scores well on a public benchmark can still fail badly on your specific documents, your specific customers, and your specific edge cases, because public benchmarks were never built to test them. Most teams end up substituting a proxy for real measurement: a few favorite prompts, a gut feeling from reading transcripts, or "it seemed better this time." None of that scales, none of it is reproducible, and none of it tells you whether a change actually helped.
Deliverables
A benchmark, not a one-off report
- A Golden Dataset
- Real-world scenarios
- Edge cases
- An evaluation rubric
- Human-baseline comparison
- Comparison against your existing system
- Model-to-model comparison
- Cost per successful task
- Confidence intervals
- A regression-testing mechanism
How it runs
From discovery to a reusable suite
- Discovery: understand what the system is actually supposed to get right, and for whom
- Dataset construction: build the Golden Dataset from real scenarios and deliberate edge cases
- Rubric design: define exactly what counts as a correct, partial, or failed answer
- Baseline run: score the current system and, where useful, a human baseline
- Handoff: deliver the suite and the tooling to re-run it, not just a one-time score
Who it's for
- Teams evaluating quality by reading transcripts and forming an impression
- Teams choosing between models or vendors and need an apples-to-apples comparison
- Teams that need to prove a change improved quality, not just changed it
- Anyone who wants to know cost per successful task, not just cost per call
Still evaluating quality by eyeballing outputs?
Get a benchmark you can run again and again, not a one-time snapshot.