TenBeltzQuality without a shared baseline
Without representative cases and acceptance criteria, model and prompt changes are judged by feel.
AI evaluation for SaaS products
See how a system performs on real cases before choosing a model, expanding access or shipping a change.
The problem
A demo does not prove a system is ready
Regressions that are easy to miss
A better answer on one example can make other tasks worse. Compare versions on the same test set.
Cost and latency without context
Average usage does not show which tasks are expensive or slow. Measure per task, including demanding cases.
How we work
What we measure before expansion
- 01
Reference cases
Collect representative examples, known failures and an acceptable outcome for each task.
- 02
Reproducible comparison
Evaluate models, prompts and architectures on the same cases to find improvements and regressions.
- 03
Operating criteria
Document quality, latency and cost with thresholds your team can check again.
Project evidence
Document classification compared on held-out data
The final ensemble reached 72.35% Top-1 and 91.51% Top-5; the CSLS baseline on the same test reached 56.44% and 83.66%.
Evaluation on 1,414 held-out documents across 77 subcategories. These are classification metrics on that test set, not a guarantee on other data or a throughput measure.
Read the full caseA fitting way to start
See the service options Technical diagnosis or Agent MVP
We will confirm the scope and recommend the right level of support after reviewing your product and current stage.
Next step
What do you need to take into production?
Tell us about your product, the AI system and its current stage. Aritz will review the fit and outline a concrete next step with you.
Prefer a conversation?
Book a 15-minute phone callWe will cover your product, the AI project and its current stage, then agree on a useful next step.
Want to assess your project first?
Take the AI diagnosis · 2 minSee your score; get your priorities in the downloadable report.
AI diagnosis · 2 min