Intelligent Document Completion for Code Gaia — Accelerating ESG Disclosure with AI-Powered RAG
Built a RAG system that generates intelligent first drafts of ESG disclosures from clients' existing documentation.
Überblick
Code Gaia is a leading sustainability and EHS management platform for ESG and sustainability compliance. DataMax partnered with Code Gaia to build an intelligent disclosure completion system — transforming a traditionally labor-intensive process into a streamlined, guided experience powered by a state-of-the-art RAG system that analyses existing documentation and provides context-aware completion suggestions for regulatory disclosures.
Herausforderung
As ESG reporting requirements intensify globally, Code Gaia's clients — from mid-sized enterprises to large corporations — struggled with the manual effort required to complete complex disclosures. The process involved sifting through numerous internal and public documents, policies, and data sources to extract relevant information, often resulting in incomplete submissions or significant delays.
Vorgehen
We implemented a hybrid search approach combining traditional full-text search with vector-based semantic search, ensuring optimal document retrieval that captures both exact matches and conceptually related content.
Lösung
DataMax designed and implemented a RAG system that ingests clients' existing documentation and uses it to generate contextually relevant initial disclosure drafts and actionable improvement recommendations. The system uses hybrid full-text and vector semantic search via PostgreSQL with pgvector, powered by Amazon Bedrock's language models to align outputs with regulatory requirements and industry best practices. The AWS-based architecture (ECS, Aurora, Celery) handles growing document volumes and user demand at scale.
Ergebnisse
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