Intelligent Document Completion for Code Gaia
Accelerating ESG Disclosure with AI-Powered RAG
Code Gaia, a leading sustainability & EHS management ESG and sustainability complianceplatform, partnered with DataMax to develop an intelligent disclosure completion system. This solution empowers companies to efficiently complete complex ESG disclosures by leveraging advanced AI technology, transforming a traditionally labor-intensive process into a streamlined, guided experience.
DataMax built a state-of-the-art Retrieval-Augmented Generation (RAG) system that analyzes existing documentation and provides intelligent, context-aware completion suggestions for regulatory disclosures.
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Technologies: AWS, Amazon Bedrock, PostgreSQL with pgvector, Celery, Python, ECS, Aurora
Challenge
As ESG reporting requirements intensify globally, companies face mounting pressure to produce comprehensive, accurate sustainability disclosures. Code Gaia's clients—ranging from mid-sized enterprises to large corporations—struggled with the manual effort required to complete these 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. The founding team at Code Gaia, having experienced these pain points firsthand, recognized that their clients needed more than just a platform—they needed intelligent assistance to navigate the complexity of ESG compliance efficiently.
Solution
DataMax designed and implemented a cutting-edge RAG system that fundamentally changed how Code Gaia's clients approach disclosure completion. The solution intelligently analyzes information provided by clients' existing documentation, using this context to provide initial disclosure completions and actionable improvement recommendations.
The system employs a hybrid search approach, combining traditional full-text search with vector-based semantic search. This dual methodology ensures optimal document retrieval, capturing both exact matches and conceptually related content. By leveraging Amazon Bedrock's advanced language models, the system generates contextually relevant completions that align with regulatory requirements and industry best practices.
Result
The intelligent RAG system transformed Code Gaia's platform capabilities and delivered measurable value to their clients:
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Accelerated Disclosure Completion: Clients now receive intelligent first drafts of their disclosures, significantly reducing the time spent on initial document creation.
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Enhanced Quality and Completeness: The system's contextual analysis identifies relevant information across diverse document sources, reducing the risk of incomplete disclosures.
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Guided Improvement Process: Beyond initial completion, the system provides targeted suggestions for enhancement, helping clients elevate the quality of their ESG reporting.
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Scalable Architecture: The AWS-based infrastructure seamlessly handles growing document volumes and user demand, positioning Code Gaia for continued growth in the expanding sustainability and EHS management market.