Projects using Python in DelhiProjects using Python in DelhiDeveloped a production-grade Retrieval-Augmented Generation (RAG) system specifically designed to automate the analysis of complex Environmental, Social, and Governance (ESG) reports. This tool bridges the gap between static LLMs and the dynamic, data-heavy requirements of legal and sustainability compliance. [1 (https://www.youtube.com/watch?v=wkYPcMtwlN8)]
Key Features & Capabilities
Intelligent Document Processing: Automatically handles large, unstructured PDF/Word ESG reports, extracting critical clauses and metrics in seconds.
Fact-Grounded Q&A: Uses a RAG architecture to ensure all answers are strictly based on the uploaded documents, virtually eliminating AI hallucinations.
Compliance Mapping: Cross-references internal company data with global frameworks like CSRD, GRI, and TCFD to identify gaps or inconsistencies.
Audit-Ready Traceability: Every insight generated includes direct citations and excerpts from the source files, providing a clear "paper trail" for legal teams.
Automated Drafting: Capability to draft legal summaries, notices, or internal policy updates based on analyzed ESG risks
Note: The 'Slaughter and May' branding in the sidebar is for UI/UX demonstration purposes only, showcasing how the tool integrates into a top-tier law firm's environment.
#AI #RAG #LegalTech #ESG #Python #LangChain Problem:
Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.
Solution:
This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing. 
