The training data agent
for autonomous AI research.
Training data curation is becoming the bottleneck to self-improving AI. Provena changes that by generating standardized signals for dataset quality — then learns which interventions actually improve model performance.
Ready
Why Provena
The next generation of AI systems will increasingly improve themselves.
Yet training data workflows remain fragmented and reactive
— a 2018 stack powering a 2030 paradigm.
Today
- Manual curation
- One-off evaluations
- Disconnected tooling
- Reactive debugging
Future
- Continuous feedback loops
- Autonomous optimization
- Standardized signals
- Self-improving data systems
What Provena does
A three-layer architecture for autonomous data curation.
Over time, Provena learns which interventions improve downstream model performance — creating feedback loops for autonomous data curation.
Layer 01
Measure
- Quality
- Provenance
- Metadata completeness
- Safety & security
Layer 02
Diagnose
- Hidden risks
- Multilingual asymmetries
- Annotation inconsistencies
- Long-tail gaps
Layer 03
Improve
- Filtering
- Augmentation
- Relabeling
- Automated interventions
Product ecosystem
A coherent stack — from signal to agent.
Data Scorecards
Standardized evaluation for dataset quality, provenance, metadata, and safety.
Learn moreTraining Data Agent
Coming SoonAutonomous systems that learn how to improve training datasets over time.
Learn more