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An enterprise-grade, interactive industrial decarbonization platform developed by Srinivasa. Powered by Streamlit and Scikit-Learn, this application implements a physics-enforced machine learning pipeline to track, optimize, and reduce manufacturing carbon footprints. Features real-time SCADA telemetry simulation, automated closed-loop anomaly dete
first-person walkthrough of building a sustainability-reporting pipeline where provenance is a feature, not a nice-to-have and why I stopped letting the model do the math. Intro I got into ESG tooling the way a lot of engineers do: someone needed carbon numbers for a report, the data was messy, and an LLM seemed like a fast way to wrangle it.
Local-first multi-agent RAG for ESG compliance auditing on SEC 10-K filings. LangGraph Supervisor, Retriever, Auditor and Critic agents with hybrid BM25 + vector search (RRF), a ReAct loop and self-consistency scoring that flags coverage gaps instead of refusing. Runs on Llama 3.1/3.2 via Ollama and LanceDB. Refusals cut from 48% to 20%.