Model Evaluation
Model Evaluation: Test Before You Invest.
Choose models, runtimes, quantization, and hardware from measured workload evidence — not vendor leaderboards.
Four-time Microsoft Solutions Partner: Security, Data & AI, Azure Infrastructure, and Digital & App Innovation. Founder-led engineering experience since 1999.
What we test
- Your actual task: generation, editing, retrieval, document processing, or tool use.
- Candidate models and serving stacks that fit the data boundary and latency requirement.
- Throughput, quality, confidence intervals, failure modes, and what was not measured.
What you receive
- A recommendation tied to evidence.
- Hardware and serving guidance based on measured behavior.
- A plain-language explanation of statistical ties and limits.
- A written risk note that prevents over-reading the result.
Proof method
Bonelli publishes its own model-evaluation discipline at openllms.bonellisystems.com: eleven batteries, published coverage, confidence intervals, disclosed gaps, and withdrawn off-standard data rather than hidden exceptions.
What we will tell you that others may not
Most model rankings are statistical ties. We will tell you when the difference between two options is not significant, and we will tell you what we did not measure.