Build eval datasets from production traffic, run experiments, and measure quality — without waiting for engineering to export data. Real-world evals, not synthetic benchmarks.
Query production data directly. Custom Python evaluators. Export for fine-tuning.
Production data access, evaluation pipelines, experiment tracking, and dataset management in one place.
Query production data directly
Filter all LLM inputs and outputs by model, user, time range, quality score, or custom metadata. No engineering dependency.
Build eval datasets from real traffic
Sample production conversations to create representative evaluation sets. Filter by quality, topic, or edge cases.
Run LLM-as-judge evals
Define evaluation criteria in natural language. A stronger model scores your production outputs. Results tracked and graphable over time.
Write custom Python evaluators
Arbitrary scoring logic: regex checks, embedding similarity, API calls, domain-specific rules. Run alongside built-in evaluators.
Compare models statistically
Same testset, multiple model configurations. Side-by-side score distributions with statistical significance testing.
Annotate and label outputs
Route production outputs to review queues. Collect human labels with custom schemas. Track inter-annotator agreement.
Export datasets for fine-tuning
Export labeled examples in JSON, JSONL, or CSV. Filter by quality score, label, or metadata to curate training data.
Track quality over time
Dashboards for eval score trends, quality regressions, and metric comparisons across experiments.
Evaluation
Data & labeling
Research & optimization
From production data to measured quality improvement — without building infrastructure.
Access production data
Query all LLM inputs and outputs with filters for user, model, feature, time range, and custom metadata.
→ Real production data, queryable
Build eval datasets
Sample production traffic to create representative evaluation sets. Filter by quality, topic, or edge cases.
→ Curated evaluation datasets
Run evaluations
Apply LLM judges, Python evaluators, or rule-based checks. Compare scores across model/prompt variants.
→ Quality scores and experiment results
Annotate and label
Route ambiguous cases to human review. Collect structured labels. Build gold-standard training data.
→ Labeled datasets for fine-tuning
Export and iterate
Export datasets for fine-tuning. Track quality trends. Feed improvements back into production.
→ Measurable quality improvement
100%
of production data queryable
Custom
Python evaluators supported
1-click
dataset export
Real-time
experiment tracking
Data formats
Evaluate any model
Import from