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.
🌲 AI & Satellite-Powered Wildfire Early Warning System. Real-time NASA FIRMS hotspot monitoring with Telegram & WhatsApp alerts + Leaflet GIS Dashboard.
ESG-Assure: An automated Supply Chain Audit Engine. Ingests raw supplier data, detects anomalies using Statistical Logic (Z-Score), and uses GenAI (Llama 3) to draft regulatory-grade Non-Compliance Memos.
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
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