Verify your evidence reviews caught everything.

Your team already did the work: months of screening, thousands of abstracts, careful inclusion/exclusion decisions. But publication bias hides critical signals in gray literature—clinical trial registries, conference abstracts, failed trials with revealing secondary outcomes.

One missed study isn't just an oversight. It's a competitor's unpublished Phase II data you didn't know existed, or a mechanism-of-action insight buried in a lower-tier journal.

The error rate reality: Manual screening has a 10.76% classification inconsistency rate. That's 1 in 9 abstracts potentially misclassified. When you're building a $15-40M program on the completeness of your mechanistic rationale, verification isn't optional—it's insurance.

The offering

Screening verification

• Independent AI-powered re-screening of your title/abstract decisions
• Identification of potentially missed relevant studies
• Classification consistency check against your criteria
• Report highlighting discrepancies and flagged items for human review

Full verification

Everything in screening verification, plus:

• Data extraction verification against source documents
• Cross-check of extracted values, endpoints, and outcomes
• Identification of extraction errors or omissions
• Full verification report suitable for regulatory documentation

Why verification matters

Catch missed repurposing signals. Your screening found 47 relevant studies. We found 3 more you missed—including the one with your competitor's failed Phase II data and the conference abstract with secondary endpoint signals.

Publication bias protection. Gray literature contains the studies that didn't make top-tier journals but still matter. Verification ensures your evidence base isn't systematically skewed toward positive findings.

Faster than re-doing, cheaper than missing. Full manual second-reader verification takes weeks. Our verification takes days. And it's orders of magnitude cheaper than building a clinical program on incomplete evidence.

Start with your next indication pitch

1. Send us a completed screening or data extraction from your current indication evaluation.
2. We run independent verification and deliver a discrepancy report.
3. Your team reviews flagged items and decides what to incorporate.
4. Measure the value: studies caught, extraction errors identified, confidence gained.

One verification proves the model.