Model Evaluation
Know exactly how your AI performs before it touches production
Solnix designs and runs rigorous model evaluation programs, from custom benchmark construction and LLM performance testing to AI safety red-teaming and hallucination detection, giving you the evidence to deploy with confidence.
Overview
Evidence-based deployment, not hope-based deployment
Most teams deploy models when they feel ready. Solnix deploys models when the data says they're ready, with structured benchmark suites, adversarial red-teaming, and documented pass/fail thresholds against every capability that matters for your use case.
What's included
LLM Benchmarking
Custom benchmark suites built around your specific tasks, domains, and quality requirements, measuring what actually matters for your use case, not generic leaderboard performance.
AI Safety Testing
Structured safety evaluation against harmful content generation, bias amplification, jailbreak susceptibility, and alignment failure modes, with documented test coverage and thresholds.
Red Teaming
Systematic adversarial testing, probing models for prompt injection vulnerabilities, instruction override susceptibility, role-playing risks, and safety policy evasion.
Hallucination Detection
Factuality evaluation measuring grounding accuracy, citation quality, knowledge boundary awareness, and confabulation rates across your model's target domains.
Performance Evaluation
End-to-end profiling: inference latency, throughput under load, token efficiency, cost per query, and degradation behavior at production scale.
Regression & A/B Evaluation
Before every model update, regression evaluations compare new vs. old performance across your full task distribution, catching regressions before they reach users.
Developer experience
Simple API. Powerful results.
Integrate in minutes with our SDK. Full TypeScript support, comprehensive documentation, and live examples for every feature.
How it works
From setup to production
Evaluation Design
We define the evaluation framework, benchmark tasks, safety scenarios, performance baselines, and pass/fail thresholds aligned to your deployment requirements.
Benchmark Construction
Custom evaluation sets are built from domain-relevant data, adversarial examples, and edge cases that expose the specific failure modes relevant to your use case.
Red Team & Safety Testing
Our AI safety team runs systematic adversarial probing across your model's safety surface, documenting vulnerabilities and remediation paths.
Report & Deployment Decision
A structured evaluation report documents all results, with a clear deployment recommendation and the evidence behind it.
FAQ
Common questions
Get started
Get the evaluation evidence your deployment decision needs
Talk to an expert and get a tailored implementation plan within 48 hours.