EnviroWash is an automated intelligence platform that detects corporate greenwashing by pairing companies' public sustainability claims against their actual environmental data from government sources.
With over $35 trillion in ESG-labeled funds globally, investors, regulators, and the public need objective tools to distinguish genuine environmental commitment from performative messaging. EnviroWash provides that transparency through automated, reproducible analysis grounded in government data that companies cannot edit or delete.
Every 4 hours, articles are pulled from 11 sources: 3 PR wire RSS feeds, 2 news APIs, and 6 government databases (EPA ECHO, GHGRP, TRI, Newsroom, SEC EDGAR, Violation Tracker).
AI clusters articles by company, then dual-scores each event: the G-Score measures actual environmental harm across 5 weighted drivers, while the C-Score measures how loudly the sustainability claim is being made.
Same-company claims are matched against reality events using ESG category matching and contradiction detection. The Wash Index = (C x G) / 100 x gap_confidence.
Every Sunday at 5am UTC, the week's data is frozen into an immutable snapshot. Companies cannot retroactively alter their record.
EnviroWash ingests from 11 independent sources. Government databases serve as ground truth — EPA filings cannot be edited or deleted by companies.
AI provides component scores, but final G-Score and C-Score are always recomputed locally using deterministic formulas. We never trust Claude's final_score directly.
Weekly snapshots are frozen permanently. Every score change is logged in an audit trail with who, when, what changed, and why.
EPA enforcement actions and emissions reports serve as the reality baseline. These are official government filings that companies cannot modify.
For the full scoring methodology, see the Methodology page.
EnviroWash is open source. View the code, contribute, or fork it for your own domain.
View on GitHub