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AI Training Data Contribution Intelligence
PrismMerit

Your Model Improved. Which Data Deserves The Credit?

Attribute gains to the shards that earned them. Expand what works, cut noise, and spend the next data dollar with evidence — not another quality score.

Analyze A Dataset Open Data Merit
Training sources
Capability contribution
Tool Selection +5.8 81.2

Production Corrections

Reasoning +2.1 78.4

Synthetic Reasoning

Instruction +1.4 84.1

Human Expert

Domain Recall 0.0 71.0

No measurable lift

Safety +0.9 88.2

Long-tail Failures

Attribute · Marginal lift with ablation evidence · click a source to focus

After review
Expand/ Keep/ Reduce/ Investigate
Answers

Know Which Data Actually Makes Your Model Better.

  • Which subset caused the improvement?
  • Which data is redundant?
  • Which data helps one capability while hurting another?
  • Where should the next data dollar go?
Not a catalog

Contribution Intelligence.

Not quality scoring, not synthetic generation, not benchmarks. PrismMerit asks whether a batch is worth the cost of training.

Decompose Model Improvement Into The Data That Earned It.