AI-Powered Document Intelligence & Energy Compliance Platform

The Challenge

A mid-sized Australian energy company was facing mounting pressure to meet regulatory compliance and evolving regulatory reporting requirements. Their existing workflows were heavily manual — relying on spreadsheets, disconnected data sources, and time-consuming document preparation. Energy data from IoT meters, weather systems, and solar generation logs sat in silos. Building managers lacked the technical skills to interpret complex consumption patterns, resulting in delayed decisions and growing compliance risk. The company needed an intelligent platform that could unify their data, automate compliance workflows, and make energy insights accessible to non-technical stakeholders.

Our Approach

Corlence designed and delivered an end-to-end AI-powered energy analytics and document intelligence platform combining four integrated capabilities.

We built predictive intelligence models for energy demand forecasting and solar generation prediction using LSTM neural networks, Prophet, and XGBoost ensemble methods. These models ingested historical consumption data, real-time IoT feeds, and weather API data to deliver accurate short-term and medium-term energy forecasts — enabling proactive resource planning rather than reactive responses.

An anomaly detection engine using unsupervised machine learning algorithms was deployed to continuously monitor energy consumption and generation patterns. The system automatically flagged irregularities such as equipment faults, unusual load spikes, and data quality issues — often days before they would have been identified through manual processes.

The centrepiece was a document intelligence layer specifically engineered for regulatory compliance. This component automatically extracted, classified, and validated information from building performance reports, regulatory submissions, and compliance documents. It dramatically reduced the manual effort required for compliance reporting and ongoing regulatory obligations — transforming a weeks-long process into a largely automated workflow.

To make all of this accessible, we layered a generative AI chatbot powered by LLM and RAG architecture on top. Non-technical building managers could now query energy data naturally — asking questions like "What is tomorrow's solar output prediction?" or "Show me this quarter's compliance status" — and receive instant, contextualised answers grounded in real data.

Technology Stack

Python, TensorFlow, Prophet, XGBoost, LangChain, OpenAI GPT, Azure Machine Learning, Power BI, FastAPI, Docker

Business Value Delivered

• Regulatory reporting time reduced by over 60% through automated compliance document generation and validation
• Equipment anomalies detected an average of 3 days earlier than manual monitoring, preventing costly failures and downtime
• Non-technical building managers independently accessed and interpreted energy insights for the first time, reducing dependency on specialist analysts
• Established a scalable AI foundation for multi-building portfolio management, positioning the client for growth without proportional headcount increases