VANTAGE
Human evaluation for better AI systems.
Maurvi AI helps teams design LLM evaluation, preference, QA, and safety review workflows that make model quality visible and testable before scale-up.
What we support
Evaluation workflows that help teams judge model quality with structure.
Model response evaluation
Preference and pairwise comparison
Safety and policy review
Factuality and relevance checks
Instruction following review
Helpfulness and utility scoring
Comparative model review
Classification and rubric-based QA
Evaluation approach
A clear process for judging model quality and improving it over time.
01
Define the rubric
We align on the quality criteria, dimensions, and failure modes that matter most for the task.
02
Calibrate on examples
Representative examples establish the standard before larger evaluation work begins.
03
Review and compare
Human review supports quality assessment, disagreement handling, and model comparison.
Typical use cases
Built for the points where product quality and human judgment meet.
Model quality review
Assess response quality against business goals, policy expectations, and edge-case performance.
Prompt and system design validation
Compare candidate instructions, generation strategies, and output choices across dimensions of quality.
Preference collection
Capture pairwise judgments and ranking data to support model tuning, evaluation, and feedback loops.
Safety and policy review
Support human review of harmful, sensitive, or policy-violating output patterns.
Operational model
Human review without creating a fragile or unscalable process.
Rubric-based scoring
Reference examples and counterexamples
Disagreement capture and adjudication
Traceable evaluation notes
Start a conversation
Need a more structured evaluation workflow?
Bring the task, model, rubric, or quality questions. We can help shape the evaluation process before it becomes a noisy, hard-to-trust workflow.
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