AI for ESG Reporting with LLM Evidence Extraction

Automate ESG evidence gathering, citations, and controls with LLMs while ensuring auditability and policy compliance.

ESG teams spend cycles collecting evidence and citations. LLMs can extract KPIs, controls, and narratives from documents and systems, with provenance and policy checks. Outcome: faster reports, fewer errors, and stronger audit posture.

Map controls to evidence and assurance workflows.

Draft sections with citations; sensitivity for claims.

Reviewer workflows; change logs; approvals.

live demo · related simulation● LIVE

Metadata extraction and lineage for evidence items.

Security: access controls, redaction, tenant isolation.

Extraction prompts with citations and units.

Frequently asked questions

What is the purpose of connecting document sources and systems to build an evidence registry?

Connect doc sources/systems; build evide?

How does the system incorporate policy checks and citation requirements into the reviewer workflow?

Question about Add policy checks and citation requireme?

Can AI automate report drafts, including localization efforts, and monitor for violations or gaps?

Question about Automate report drafts; localization; mo?

How does the AI integrate with existing controls and assurance processes, potentially adding external assurance packs?

Question about Integrate with controls/assurance; add e?

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation

AI for ESG Reporting with LLM Evidence Extraction | ML Knowledge Hub