Private Capability Benchmarks
Human-authored question sets designed for your specific model tasks — not off-the-shelf benchmarks. Private, non-contaminated, directly relevant to your evaluation needs.
Use Case
Model
Benchmarking
Human-constructed benchmarks that measure what matters — real-world task performance, nuanced instruction following, and capability gaps that standard automated metrics obscure or miss entirely.
The Problem with Automated Benchmarks
Benchmark saturation is a real problem
Models that score well on MMLU, HumanEval, and HellaSwag are still shipped with significant real-world capability gaps. Public benchmarks become contaminated with training data, and teams increasingly need private, human-constructed evaluations that reflect their specific use case and user population.
Fuzu Atlas builds custom benchmarks from scratch: human-authored questions, scenarios, and evaluation criteria designed to measure what your model actually needs to do well — not what public benchmarks were designed to test.
Contamination risk
Public benchmarks frequently appear in model training data. Performance on contaminated benchmarks is not a reliable measure of generalisation.
Task mismatch
Standard benchmarks test generic capabilities. Custom benchmarks built around your actual use case measure whether the model will work for your users.
English-only coverage
Most public benchmarks are English-centric. Private multilingual benchmarks are required to evaluate capability across language markets.
No longitudinal tracking
One-off benchmarks don't track capability regression as models are fine-tuned. Ongoing benchmarking programs catch the degradation before it reaches users.
Benchmark programs Fuzu Atlas can build
Multilingual Capability Tests
Parallel benchmark sets across your target language markets — authored by native speakers with equivalent difficulty calibration across languages.
Domain-Specific Expert Benchmarks
Medical, legal, financial, and scientific benchmarks authored by credentialed domain experts. Tests that a generalist cannot pass by memorisation alone.
Regression Testing Suites
Stable benchmark sets run after each model update to detect capability regression. Human evaluation maintained over long model development cycles.
Instruction Following Evaluation
Human-constructed instruction sets across complexity levels and constraint types. Scored by trained evaluators against rubrics designed for your deployment context.
Human Preference Studies
Side-by-side model comparison studies with target-population evaluators. Measures user-facing quality, not just automated metric alignment.
Related solutions & industries
Build a benchmark that actually measures what matters
Define your evaluation goals — we'll construct the benchmark, run the evaluation, and deliver actionable findings.