AI Clear is the independent rating layer for AI transparency. Structured the way credit rating agencies structure credit.
Ratings are derived entirely from public disclosure, scored against a published 26-criteria rubric, and mapped to the MIT AI Risk Repository, the EU AI Act, and the Colorado ADMT framework.
The registry is updated quarterly. Scores can be tracked over time as a leading indicator of regulatory and reputational risk.
What you get
Portfolio scoring
Score every company in your portfolio against the AI Clear methodology. Identify disclosure gaps before regulators do and benchmark holdings against industry peers.
Pre-investment diligence
A structured AI transparency assessment for any target company, mapped to Colorado ADMT, EU AI Act, and GDPR Article 22. A defensible artifact for the investment memo.
Custom sector reports
Sector-specific analyses showing how transparency disclosures vary across industries, which categories face the most enforcement risk, and where the leaders and laggards sit.
Data API access
The full registry via API for firms that want to build AI transparency signals into internal scoring models, deal sourcing platforms, or LP reporting.
Portfolio risk preview
Enter the number of companies in your portfolio to see a simulated grade distribution based on current registry averages.
How investors use AI Clear
Detailed scenarios drawn from actual conversations with growth equity and venture firms. Names are anonymized to protect commercial relationships.
The covenant
A growth equity firm was considering a $30M Series B in an AI agent startup serving regulated industries. The firm pulled the target's AI Clear score, which came back as a B at 82 out of 100, and compared it against 15 other AI agent startups already in the registry. The B score placed the target in the bottom quartile of its peer set. The firm used this delta to negotiate deal terms, including a covenant requiring an A score within twelve months of closing as a condition of the second tranche.
AI Clear scoring is now a standing input in the firm's investment committee memos.
The portfolio sweep
An early-stage venture fund preparing its quarterly LP update commissioned AI Clear to score all 38 active portfolio companies. The audit returned three findings the GP did not previously know: two portfolio companies had no public AI policy at all, four had outdated disclosures from before the EU AI Act took effect, and one had a documented data incident with no published incident response process. The fund used the audit to drive a portfolio-wide remediation memo and added AI transparency reporting to its standard board package.
AI Clear became the firm's quarterly portfolio risk dashboard.
The sector report
A thematic fund developing a healthcare AI investment thesis needed independent data on how clinical AI vendors disclose their model practices, training data sources, and bias audits. AI Clear produced a custom sector report covering 22 healthcare AI companies, showing that fewer than a third disclosed any form of bias audit, only four published model cards, and none had a public appeals process for patients affected by algorithmic decisions. The report was attached to the fund's thesis memo and shared with the investment committee.
The fund repositioned its thesis around the four companies with the strongest disclosures and avoided two bottom-quartile targets.
The LP request
A multi-stage fund received a request from one of its largest institutional LPs to demonstrate how the fund was assessing AI risk across its portfolio in light of new state-level AI laws. The fund did not have an internal framework. It engaged AI Clear to score 24 portfolio companies, produce a written risk summary mapped to the Colorado ADMT framework, and deliver a one-page artifact suitable for inclusion in the LP report. The LP accepted the artifact as evidence of an AI risk process.
AI Clear is now referenced in the fund's annual ESG and risk disclosures.
Every AI Clear score is derived from public disclosure, scored against a published rubric, and backed by source citations.
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