Close the Feedback Loop
Between Production and Training
Systematic evaluation across models. Structured corrections from experts. Training data exported as SFT, DPO, and ranking datasets. Every production failure becomes a training signal.
The ML Feedback Loop
Evaluate → Correct → Export → Fine-tune → Deploy → Evaluate again.
Systematic Evaluation
Evaluate model outputs against structured rubrics. Compare models side by side. Track quality across versions.
Training Data Export
Every correction becomes training data. Export as SFT (instruction/response), DPO (preference pairs), or ranking datasets.
Continuous Improvement
Fine-tune on corrections, redeploy, evaluate again. Each cycle reduces failure rate. Measure improvement quantitatively.
Multi-Stage Pipeline
Per-stage model selection. Use cheap models for initial triage, expensive models for edge cases. Optimize cost vs quality.
Quality Analytics
Failure pattern analysis, quality trends over time, per-dimension scores. Know exactly what's improving and what isn't.
Custom Taxonomies
Define evaluation dimensions specific to your domain. Medical accuracy, code correctness, creative quality — whatever matters for your use case.
Export Formats
SFT
Supervised Fine-Tuning
Instruction + gold-standard response pairs. Use corrections directly as training examples for fine-tuning.
DPO
Direct Preference Optimization
Chosen vs rejected response pairs. Train your model to prefer correct outputs over problematic ones.
Ranking
Multi-Candidate Ranking
Scored response sets for reward model training. Multiple candidates ranked by quality and correctness.
Frequently Asked Questions
Better Models Start with Better Evaluation
Join the teams shipping safer AI with real-time evaluation, audit trails, and continuous improvement.