Inspiration & Problem Statement
Organizations across Asia-Pacific face tightening ESG regulations (Bursa Malaysia Sustainability Reporting, K-ESG guidelines, and ISSB standards), yet 78% of mid-market companies still rely on fragmented spreadsheets, manual utility bill parsing, and static PDF reports. Worse, organizations stop at reporting ("Data") without executing verifiable emission reductions ("Action") or catching supply-chain greenwashing anomalies.
Our Solution: EcoPulse AI (Data to Action)
EcoPulse AI bridges the gap between passive ESG reporting and autonomous operational action. Built as a full-stack cloud web application, it transforms unstructured operational data into audit-ready disclosures and triggers real-time sustainability workflows.
Key Features
- Automated Multi-Modal ESG Data Ingestion: Parses utility bills, ERP invoices, IoT facility sensors, and logistics manifests using OCR and LLM extraction to compute real-time Scope 1, 2, and 3 emissions.
- AI Greenwashing & Anomaly Auditor: Uses RAG and anomaly detection to cross-verify supplier sustainability claims against empirical emission factors and flag compliance discrepancies before regulatory audits.
- Autonomous "Data-to-Action" Workflow Engine: Instead of static charts, EcoPulse AI generates ranked, ROI-backed remediation playbooks (e.g., automated cloud workload shifting to low-carbon grids, supplier contract routing, and HVAC schedule optimization) with one-click webhook execution into Jira, Slack, and ERP systems.
- One-Click Multi-Framework Compliance: Automatically maps metrics to GRI, TCFD, ISSB (IFRS S1/S2), and K-ESG frameworks with verifiable audit trails.
Technology Stack & Architecture
- Frontend: Next.js, React, Tailwind CSS, Recharts (Interactive ESG & Carbon Simulation Dashboard)
- Backend & AI Pipeline: Python (FastAPI), LangChain, OpenAI / Gemini API for document intelligence & RAG, Pandas, Scikit-Learn (time-series emission forecasting & anomaly detection)
- Database & Cloud: PostgreSQL, Redis, Docker, deployed on public cloud infrastructure
Target Users & Business Viability
- Target Market: SMEs, manufacturing exporters, and corporate sustainability teams in Malaysia and South Korea navigating cross-border supply chain mandates.
- Business Model: B2B SaaS tiered subscription based on tracked facilities and automated supplier audits, reducing ESG compliance consulting costs by over 65%.
Expected Impact
Cuts manual ESG data collection time by 80%, eliminates greenwashing liability via cryptographic audit trails, and drives measurable 12–18% Scope 2 & 3 emission reductions within the first two quarters of deployment.
Built With
- ai
- cloudant
- databases
- fastapi
- langchain
- machine-learning
- nextjs
- postgresql
- python
- react
- tailwindcss
