We developed an AI-driven energy optimisation system that leverages Python, LangChain, LangFlow, and Retrieval-Augmented Generation (RAG) to analyse consumption data and provide actionable energy-saving insights. The solution combined multi-agent orchestration with intelligent reasoning to deliver value for both users and enterprises.

Traditional energy monitoring systems collect data but lack the intelligence to provide personalised recommendations. The client needed a system that not only identified inefficiencies but also guided users with practical, real-time tips for reducing energy costs.

We designed a three-agent AI system to handle the full data-to-advice pipeline:

Data Retrieval Agent – Automatically extracts user energy consumption data from the database in real time.

Anomaly Detection Agent – Processes and analyzes patterns to detect spikes, irregularities, and wastage trends.

Energy-Saving Advisor Agent – Uses the processed insights to generate personalized, actionable saving tips, adapting advice dynamically through RAG-powered knowledge enrichment.

⚡ Smarter Insights – Automated detection of unusual consumption patterns.

💡 Personalised Recommendations – Users receive relevant, real-world saving tips, improving engagement.

⏱ Real-Time Analysis – Eliminated manual monitoring with an autonomous agent system.

🌍 Sustainability Impact – Helped reduce unnecessary energy usage and supported greener operations.

By combining LangChain’s multi-agent framework with RAG pipelines, the solution transformed raw data into intelligent, actionable guidance—empowering users to lower costs and organisations to promote sustainability.