AI ESG Reporting
AI that assembles auditable ESG disclosures from your operational data instead of a quarterly spreadsheet scramble.
ESG reporting has become a second closing process: emissions figures scattered across meters, invoices, and contractor reports, disclosure frameworks that overlap but never quite align, and auditors who now check the numbers. We build AI ESG reporting automation that treats disclosure like financial data. Extraction models pull activity data from utility bills, fuel invoices, and supplier documents with citations. Calculation engines apply the right emission factors with versioned methodologies. Mapping layers align one governed dataset to CSRD, GRI, SASB, and investor questionnaires so you stop re-answering the same question in ten formats. Every figure traces to its source, and your sustainability team reviews and signs off before anything is disclosed.
Extract activity data from utility bills, fuel invoices, travel records, and supplier reports, each value cited to its source document.
Apply versioned emission factors and calculation methodologies so every figure is reproducible and defensible under audit.
Map the governed dataset to CSRD, GRI, SASB, and customer questionnaires, drafting narrative sections grounded in the numbers.
Route every disclosure through sustainability team review and sign-off, with the full lineage retained for assurance providers.
What it does
Activity data extraction
Reads bills, invoices, and contractor reports into structured activity data with citations, replacing the quarterly spreadsheet chase.
Versioned calculations
Emission factors and methodologies are versioned and logged, so restated figures are explainable and audits do not start from zero.
Multi-framework mapping
One governed dataset feeds CSRD, GRI, SASB, and investor questionnaires, ending the copy-paste divergence between reports.
Assurance-ready lineage
Every disclosed number traces back through calculation to source document, which is exactly what limited and reasonable assurance require.
A mid-cap energy company cut ESG reporting preparation from 14 weeks to 4 and passed limited assurance with no restated figures.
Questions, answered
The mechanical layers: extracting activity data from documents, applying emission factors, and mapping figures across frameworks. Judgment calls, materiality decisions, and final sign-off stay with your sustainability team.
Yes, that is the point. Every figure traces from the disclosure back through a versioned calculation to a cited source document, so assurance work gets faster instead of harder.
No. One governed dataset maps to each framework's requirements, so CSRD, GRI, and customer questionnaires draw from the same verified numbers.
The extraction pipeline handles supplier documents and spend data, and the calculation engine supports both spend-based and activity-based Scope 3 methods, with the method recorded per category.
Bring ai esg reporting to your team
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