Gold-Standard Benchmarking

Benchmark your AI
with humans

Upload your AI outputs, define evaluation criteria, and get a human-verified scorecard. Accuracy, safety, comparison — any benchmark, gold-standard quality.

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Human-verified
Multi-rater
Scorecard
Any model
How It Works

From AI outputs to gold-standard scores

Four steps to benchmark any AI model with human evaluators.

STEP 01

Upload AI outputs

Import model responses, generated text, or AI predictions via file upload, CSV, or API. Include reference answers if available.

STEP 02

Define evaluation criteria

Choose a template — accuracy benchmark, safety evaluation, model comparison — or build custom evaluation dimensions and rubrics.

STEP 03

Human evaluators score

Verified raters evaluate each AI output against your criteria. Multiple raters per item ensure reliable, unbiased scoring.

STEP 04

Get model scorecard

Download dimension-level scores, pass/fail rates, and aggregate benchmarks as CSV — or view results in the dashboard.

Benchmarking Types

Four ways to benchmark your AI

Each benchmarking type comes with pre-built templates, evaluation dimensions, and quality controls.

Accuracy Benchmark

Evaluate AI model outputs against ground truth. Get a human-verified accuracy scorecard.

Use Cases

  • LLM evaluation
  • QA system benchmarking
  • RAG pipeline accuracy
  • Chatbot quality scoring

Rating Scale

Fully CorrectMostly CorrectPartially CorrectMostly IncorrectCompletely Wrong

Template Preview

Template Name
LLM Output Accuracy
Description
Evaluate large language model outputs for factual accuracy, relevance, and completeness against reference answers.
Evaluation Dimensions
Factual accuracyRelevanceCompletenessCoherence
Pass Threshold
80%
Raters per Item
3
Rater Guidelines Preview
Compare each AI-generated response against the reference answer. Rate factual accuracy (are claims verifiable and true?), relevance (does it answer the question?), completeness (are key points covered?), and coherence (is it well-structured?). Use the reference as ground truth — if the AI output contradicts it, mark as incorrect even if the AI reasoning seems plausible.

See it in action

A real example of how benchmarking works — evaluating an LLM output for accuracy.

loevech.com/manage — LLM Output Accuracy
Item #12 of 50
Prompt
What is the capital of Australia and when was it established?
AI Response
The capital of Australia is Canberra. It was established as the capital in 1913, chosen as a compromise between Sydney and Melbourne.
Reference Answer
Canberra is the capital of Australia. It was officially established as the capital on 12 March 1913 as a compromise between rivals Sydney and Melbourne.
Guidelines: Compare the AI response against the reference answer. Rate factual accuracy, relevance, and completeness.
Your Evaluation

Built for AI evaluation teams

Model Scorecard

Get a structured quality report with per-dimension scores, pass/fail rates, and aggregate benchmarks across your entire evaluation set.

Multi-Dimension Eval

Rate AI outputs across factual accuracy, safety, coherence, relevance, and more — simultaneously. Granular scores, not just a single number.

Gold-Standard Validation

Compare AI outputs against verified ground truth and reference answers. Human raters confirm what automated metrics cannot.

Regulatory Ready

EU AI Act, SOC2-ready audit trails. Every evaluation is timestamped, traceable, and exportable for compliance documentation.

Side-by-Side Comparison

A/B test two models on identical inputs. Human raters pick the winner across multiple quality dimensions — no position bias.

Rich Export

CSV with per-dimension scores, rater agreement, individual evaluations, and aggregate statistics. Ready for your analysis pipeline.

Who uses AI benchmarking?

Any team that builds, deploys, or procures AI systems.

Model Eval

AI/ML Teams

Evaluate model accuracy before deployment. Benchmark fine-tuned models against baselines and track quality across releases.

Compliance

Compliance Teams

Run safety and bias audits on AI systems. Generate human-verified evaluation reports for regulatory submissions.

Product Quality

Product Teams

Measure chatbot and copilot quality from the user perspective. Benchmark response accuracy, helpfulness, and safety.

Research

Research Teams

Create gold-standard benchmark datasets with human annotations. Publish reproducible evaluation results with inter-rater agreement.

Vendor Eval

Enterprise Teams

Evaluate AI vendor outputs before signing contracts. Compare multiple vendors on the same test set with unbiased human raters.

Trust Building

Startups

Demonstrate AI accuracy to investors and customers with third-party human evaluation. Build trust with gold-standard benchmarks.

Trust but verify your AI

Upload your first batch of AI outputs and get a human-verified scorecard in hours. No credit card required.

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