LLM Quality, Safety & Evaluation
Systematic frameworks to ensure your LLM systems produce accurate, safe, and reliable outputs.
The 5 Dimensions of LLM Quality
We measure all five — because a model that scores well on accuracy but fails on safety is not production-ready.
What we build
LLM outputs are probabilistic — which means quality and safety cannot be assumed. We build evaluation frameworks that systematically measure output quality, detect failure modes, and establish the confidence thresholds your business needs before deploying AI in production.
Our evaluation systems combine automated scoring, regression testing, and human review workflows so you have continuous visibility into model performance and can catch degradation before it reaches your users.
Key deliverables
- Custom evaluation dataset construction for your domain and tasks
- Automated scoring pipelines (RAGAS, custom metrics, LLM-as-judge)
- Hallucination detection and factual accuracy measurement
- Safety guardrails and content policy enforcement
- Regression test suites for prompt and model changes
- Human review workflows and feedback loop integration
Real-Life Use Cases
Quality and safety evaluation preventing costly failures in production.
Medical AI Safety Guardrails
A health information platform deployed an AI Q&A system. Before launch, red-teaming revealed the model would provide specific medication dosage advice — a serious safety risk. We implemented content guardrails and a medical disclaimer system. Zero safety incidents in 18 months of operation.
Hallucination Detection for Legal AI
A legal research tool had a 22% hallucination rate on case citations — citing cases that don't exist. We built a citation verification pipeline that cross-checks every cited case against a legal database. Hallucination rate dropped to 1.4% and user trust scores improved significantly.
Product Recommendation Quality
A retailer's AI recommendation system was suggesting out-of-stock and discontinued products. We built an automated quality evaluation pipeline that checks recommendations against live inventory. Irrelevant recommendation rate dropped from 18% to 0.3%.
Multilingual Output Quality
A global company deployed an AI assistant in 12 languages. Quality varied dramatically across languages. We built a multilingual evaluation framework with native-speaker review panels. Low-quality language deployments were identified and improved before user rollout.
Ship AI you can trust
We'll build the evaluation framework that gives you confidence in your LLM system's quality and safety before it reaches users.
Build Your Evaluation Framework