ModelTrustIndex
A public, continuously updated scorecard that grades major AI assistants on transparency, data collection, and hidden system behaviors, so buyers can compare tools before rolling them out.
IT procurement managers and CTOs
- Vendor-submitted and independently verified transparency scores for top AI tools
- Side-by-side comparison of telemetry, training opt-outs, and hidden behaviors
- Public incident log linking each tool to documented disclosures like the Claude Code case
- Procurement-ready PDF reports with citations usable in vendor RFPs
Procurement teams are now being asked by legal and security to justify AI tool choices, and the Claude Code disclosure shows that even reputable vendors ship behavior they don't disclose until someone looks.
Multiple 2025-2026 guides and frameworks targeted at procurement/CTOs exist (dunnixer Enterprise AI Vendor Scorecard, aikaara Enterprise AI Partner Scorecard, axis-intelligence 100-Point Evaluation Matrix, seethru.ai transparency guide), confirming an active buyer conversation.Enterprise AI Partner Scorecard — How Procurement Teams Compare Vendors ↗Enterprise AI Vendor Selection: Complete 100-Point Evaluation Matrix ↗
Crowded adjacent space — ServiceNow VRM, Prevalent/Mitratech, BitSight, UpGuard and emerging AI-specific TPRM tools already serve continuous vendor risk; however, none act as a public, independent trust scorecard focused on AI assistant transparency and hidden behaviors — narrow gap, not blue ocean.8 AI Vendor Risk Management Tools for 2026 | Torii ↗Which AI Vendor Risk Management Tool Fits Your Compliance Program? 2026 ↗
Procurement/GRC SaaS budgets are established and an independent blueprint estimates $12K–$55K MRR potential for a similar AI vendor risk assessor; willingness to pay exists but scorecards are also heavily given away as content marketing, putting pressure on paid conversion.AI Vendor Risk Assessor — AI / ML SaaS Blueprint, Validation & 2026 GTM ↗Best AI Procurement Software & Tools in 2026 - Suplari ↗
Regulatory tailwinds (EU AI Act, NIST AI RMF, expanding SEC disclosure rules) plus board-level scrutiny of AI purchases make independent transparency scoring a durable, multi-year need rather than a passing trend.
Building the scoring methodology and maintaining continuous eval across frontier labs is non-trivial but technically straightforward; the harder part is editorial credibility, lab cooperation, and avoiding vendor pushback — moderate build effort.