How It Works
How the Proof Expert Data Pipeline Works
Proof is a 5-stage pipeline that transforms domain expert knowledge into verified, auditable AI training data with full provenance.
Stage 1: Intake and Specification
Every engagement starts with understanding the data need: what domain, what task complexity, what quality standards. The specification defines expert qualifications required, annotation schema, verification criteria, and delivery format. This ensures alignment before any annotation begins.
Stage 2: Expert Annotation with Provenance
Verified domain experts (PhDs, senior engineers, licensed practitioners) create annotations including not just answers but reasoning processes. Every annotation records who created it, their qualifications, time spent, and confidence level. This provenance metadata enables downstream quality analysis.
Stage 3: Multi-Stage Verification
Independent expert reviewers verify each annotation against the specification. Disagreements go through structured arbitration with documented rationale. This multi-stage process catches errors that single-pass review misses and produces verified consensus labels.
Stage 4: Quality Metrics and Audit
Statistical quality assurance tracks inter-annotator agreement, error rates by category, expert performance, and annotation difficulty distributions. Auditable data cards accompany every delivery, giving teams full transparency into data provenance and quality.
Stage 5: Delivery and Iteration
Structured delivery with feedback loops enables continuous improvement. Teams can flag issues, request adjustments, and refine specifications based on how the data performs in training. This iterative approach ensures data quality improves over time.
FAQ
Frequently Asked Questions
How does the Proof expert data pipeline work?
Proof uses a 5-stage pipeline: intake and specification, expert annotation with provenance, multi-stage independent verification, statistical quality metrics with audit trails, and iterative delivery with feedback loops.
What makes Proof different from other data labeling services?
Proof uses verified domain experts (not crowd workers), captures reasoning provenance (not just labels), employs multi-stage independent verification, and provides auditable data cards with full quality metrics. It's data infrastructure, not a labeling marketplace.
Build reliable AI agents.
From diagnosis to expert data to regression testing — we help frontier AI teams ship agents that work in production.
Talk to Us