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AI Benchmarks

Evaluate AI Models with Custom Benchmarks

Turn model evaluation into clear, repeatable metrics that map to your unique business outcomes.

why custom ai benchmarks matter

Measure business impact, not leaderboard rankings.

LLM leaderboards and off-the-shelf benchmarks  provide useful reference points, but they rarely reflect the unique challenges of your business. HumanSignal makes it easy to design custom benchmarks that align with your specific domain, data, and success metrics.

How do AI benchmarks work?

AI benchmarks are standardized, repeatable tests for AI systems.

Two key components:

Two key components:

1. Task Set

Test suite tailored to your use case

  • Happy-path, common cases
  • Challenging, poor-user feedback, or adversarials
  • Diversity of scenarios the system encounters

2. Scoring Method

How the benchmark evaluates tasks

  • Statistical scoring (eg. accuracy)
  • Judgement-Based Scoring (eg. factuality, rubric)
  • Composite methods (eg. token overlap score with relevancy rating)

HumanSignal provides tools to turn your proprietary data into repeatable, trustworthy benchmarks:

  • Define your benchmark

    Build ground truth datasets or rubric criteria with custom interfaces designed for SMEs.

  • Automate benchmark evaluation with HITL

    Configure automated metrics and/or human evaluations for accuracy, reliability, and alignment.

  • Analyze and compare results

    Dig into benchmark results to identify failure modes or regressions.

  • Iterate and version

    Annotate new tasks and expand benchmarks as new requirements or edge cases emerge.

Case Studies

Resources to Explore:

Resources to Explore:

Legal Benchmark with Legalbenchmarks.ai

A global community of legal professionals ran the first independent benchmark for real-world contract drafting.

Interesting insights:

Learn how Legalbenchmarks.ai built and scaled a benchmark for practical contract drafting tasks using LLM-as-a-judge and human review in Label Studio Enterprise.

GPT-5 on Custom Benchmarks

In our recent blog tutorial, we evaluated GPT-5 against a custom benchmark built from realistic use cases.

Key insights:

This demonstrates why custom benchmarks are essential for organizations that want model performance tied to business outcomes. Read more here.

Benchmarks evolve as models evolve.

Get started today.

Whether you’re deploying LLMs into production or exploring model fit,
HumanSignal gives you the framework to measure what matters.