Core Solution

LLM Evaluation
& Safety

Human-led evaluation for large language models — from RLHF preference ranking and safety red-teaming to adversarial prompting and output quality scoring. Expert reviewers, not anonymous crowd workers.

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Expert

Reviewers, not crowd workers

Multi-lingual

Red-teaming across 40+ languages

Structured

Rubrics & audit trail per prompt

Scalable

From PoC to ongoing evaluation program

Capabilities

End-to-end LLM evaluation support

Every capability delivered by a qualified, governed workforce — not anonymous click-workers. Rubrics designed with your team. QA authority built into every workflow.

RLHF Preference Ranking

Side-by-side response comparison and ranking. Reviewers trained on custom rubrics covering helpfulness, accuracy, tone, and safety. Consensus resolution protocols for ambiguous pairs.

Safety Red-Teaming

Adversarial prompting to surface harmful outputs, jailbreaks, and policy violations. Reviewers briefed on safety rubrics. Results structured by category, severity, and reproduction rate.

Output Quality Scoring

Multi-dimensional scoring on factual accuracy, coherence, instruction-following, tone, and format. Scorecards delivered per model, per prompt category, per release candidate.

Multilingual Safety Coverage

Safety gaps in non-English languages are common. Native-speaker evaluators in 40+ languages surface culturally specific harms that English-only evaluation misses.

Domain Expert Review

Medical, legal, financial, and scientific outputs reviewed by credentialed specialists — not generalist raters who lack the domain knowledge to catch subtle errors.

Ongoing Evaluation Programs

Not just one-off testing — continuous model monitoring, regression evaluation between releases, and benchmark maintenance over long-term model development cycles.

Our Approach

Rubric design is half the work

The quality of LLM evaluation is determined more by rubric quality than reviewer count. Fuzu Atlas's delivery process begins with structured rubric design — working with your team to define what “good” actually means for your model and use case.

Ambiguous rubrics produce noisy signal. Reviewers who don't understand edge cases produce biased rankings. We build for repeatability and interpretability from the outset.

Rubric co-design workshop

Joint session to define evaluation dimensions and edge case handling before annotation begins.

Calibration sample run

Small calibration batch reviewed by Fuzu Atlas QA team and client before full production begins.

IAA tracking throughout

Inter-annotator agreement monitored continuously. Reviewers retrained when consistency drops.

Sample Evaluation Dimensions

Factual Accuracy

RLHFQA

Instruction Following

RLHF

Harmlessness / Policy