Open Source AI Transforms ESG Data Research for Investors

With the data collected, agents can query it via natural language. In one demonstration, an agent converted plain-English queries to SQL to fetch numeric data (e.g. "Scope 2 emissions in 2024") from the emissions database.

Regardless of source, all these data points - from PDFs, APIs, and databases - feed into a unified knowledge base for the reporting pipeline. The compliance assurance process is next in line after the raw metrics have been gathered. The mixture of code logic and LLM support can help in this regard.

In real life, you would perhaps get rules from a knowledge base or configuration. Compliance checks are frequently divided into roles in agent-based systems. The Criteria/Mapping agents link the data that has been extracted to the specific disclosure fields or the criteria of the taxonomy while the Calculation agents carry out the numeric checks or conversions.

To cite an example, one of the agents could check if a particular activity conforms to the "Do No Significant Harm" criteria set by the Taxonomy or could derive total emissions by means of text-to-SQL queries. LangChain provides SQL tools to automate this step. For instance, one can create a SQL Agent that examines your database schema and generates queries.

(In practice, ensure your database permissions are locked down, as executing model-generated SQL has risks.) After validation, the final stage is to compose the narrative report. Here a synthesis agent takes the cleaned data and writes human-readable disclosures. We can use LLM chains for this, often with RAG to include specific figures and citations.

A notable compliance gap is identified in the **Energy Audit Summary - 2024**, where the renewable energy share is reported at **28%**, which is below the regulatory target of **30%**.