ClaimCheck
An independent AI-vendor evaluation service that re-tests a vendor's marketing claims (accuracy, lift, ROI) across multiple implementations and real customer data before a buyer signs the contract.
Procurement and operations leaders at mid-sized companies evaluating AI tools for sales, support, or forecasting
- Submit a vendor's claim (e.g. '95% accuracy on invoice parsing') and receive an independent re-test across at least three implementation variants
- Red-flag report flagging cherry-picked scenarios, narrow benchmarks, or single-run demos
- Side-by-side comparison of competing AI vendors tested on the same anonymized customer data
- Board-ready 'reliability scorecard' with confidence intervals rather than point estimates
The 'implementation lottery' paper shows winner outcomes flip 25-43% of the time between implementation draws, meaning procurement teams routinely over-trust a single vendor demo.
Multiple sources confirm AI procurement is now mainstream ("most enterprise AI is now procured, not built") and buyers need help, but no evidence yet that mid-sized companies pay specifically to re-test vendor accuracy/ROI claims.AI Vendor Assessment: The Pre-Procurement Evaluation Framework ↗Third-Party AI Vendors: Due Diligence Requirements ↗
Existing third-party AI due-diligence vendors (GLACIS, Prediction Guard, Airiskaware, GrowthlyAI) focus on governance, compliance, bias, and security checklists — none re-test vendor performance/accuracy/ROI claims across real implementations.Third-party AI due diligence tools - AI Governance Vendors ↗AI Vendor Due Diligence Checklist 2026 — GLACIS ↗Third-party AI vendor risk assessment: audit-ready vendor due diligence checklist ↗
Enterprise AI due-diligence is a recognized spend category with vendor checklists being sold, but bespoke re-testing of performance claims requires expensive expert labor and customer data access; willingness-to-pay for outcome-specific testing is unproven.Third-party AI due diligence tools - AI Governance Vendors ↗Third-Party AI Vendors: Due Diligence Requirements ↗
AI vendor proliferation is structural and accelerating, and the paper's framing of implementation-level vs idea-level reliability maps onto demo vs production reality — the underlying problem is durable as long as AI tools are sold on demos.One Run Is Not an Idea: The Implementation Lottery in Automated Research ↗
Re-testing a vendor's accuracy/lift/ROI requires accessing real customer datasets across multiple deployments and re-running vendor models — a major data-access and engineering barrier for a startup; the cited 'Idea Reliability Audit' is a research methodology, not a turnkey product.