Agents fail in unpredictable ways
How do you know your AI feature works?
Evals test your AI with real data and score the results. You can determine whether changes improve or hurt performance.
Are bad responses reaching users?
Production monitoring tracks live model responses and alerts you when quality drops or incorrect outputs increase.
Can your team improve quality without guesswork?
Side-by-side diffs allow you to compare the scores of different prompts and models, and see exactly why one version performs better than another.
Intuitive mental model
All evals are composed of a dataset, task, and scorers. This framework gives teams a shared understanding for testing and improving AI applications systematically.
Cross-functional collaboration
Engineers write code-based tests. Product managers prototype in the UI. Everyone can review results and debug issues together in real time.
Built for scale
Reliable, fast infrastructure handles high-volume production traffic and complex testing workflows.
“I've never seen a workflow transformation like the one that incorporates evals into ‘mainstream engineering’ processes before. It's astonishing.”
Fast prompt engineering
Tune prompts, swap models, edit scorers, and run evaluations directly in the browser. Compare traces side-by-side to see exactly what changed.
Batch testing
Run your prompts against hundreds or thousands of real or synthetic examples to understand performance across scenarios.
AI-assisted workflows
Automate writing and optimizing prompts, scorers, and datasets with Loop, our built-in agent.
Quantifiable progress
Measure changes against your own benchmarks to make data-driven decisions.
Quality and safety gates
Prevent quality regressions and unsafe outputs from reaching users.
Automated and human scoring
Run automated tests on every change, then layer human feedback to capture the nuance machines miss.
Live performance monitoring
Track latency, cost, and custom quality metrics as real traffic flows through your application.
Automations and alerts
Configure alerts that trigger when quality thresholds are crossed or safety rails trip.
Scalable log ingestion
Ingest and store all application logs with Brainstore, purpose-built for searching and analyzing AI interactions at enterprise scale.
Loop
Prompt optimization
Loop analyzes your prompts and generates better-performing versions so you can hit your quality targets faster.
Synthetic data generation
Loop creates evaluation datasets tailored to your use case with the volume and variety needed for thorough testing.
Scorer building
Loop builds and refines scorers to measure the specific quality metrics that matter for your application.
Brainstore is built for AI data
Security and compliance at scale
Granular permissions
Role-based access control with org-level permissions and project isolation to meet your security and compliance requirements.
Hybrid deployment
Self-hosting options to maintain full control over your AI data and meet strict compliance requirements.
Outsized impact for the biggest brands in AI
“Every new AI project starts with evals in Braintrust—it's a game changer.”