Enterprise AI Strategy & Implementation for Australian Organisations

Challenge:

A global financial institution struggled with inefficiencies in retrieving critical information across policy, compliance, and operational documents. Employees relied on outdated portals and manual searches, leading to long turnaround times, inconsistent answers, and reduced productivity.

Solution:

Rhino Partners designed and implemented a multi-agent Retrieval-Augmented Generation (RAG) platform to unify knowledge access across the enterprise. Built with LangGraph-based orchestration, the platform manages the entire query lifecycle, from classification and contextual retrieval to multi-round reasoning and evaluation. A modular Vector Search adapter was integrated with Databricks Unity Catalog, ensuring data lineage, auditability, and strict adherence to security compliance. To ensure domain precision, semantic embeddings were fine-tuned for financial terminology, while the synthesis layer dynamically adjusted prompting strategies based on query intent (e.g. policy lookup, compliance interpretation, or advisory summaries). Finally, automated evaluation pipelines using DeepEval Faithfulness metrics were integrated with MLflow Evaluate, verifying factual accuracy and consistency across generated responses.

Challenge:

A leading energy operator faced recurring unplanned shutdowns and inefficiencies in gas compression operations. Their monitoring system relied on manual inspection of PI data and spreadsheet-based performance tracking, which made it difficult to identify early warning signs of component degradation. Operational parameters such as turbine pressure, lube oil pressure, discharge temperature, and seal gas differentials were logged inconsistently, leading to undetected anomalies and high maintenance costs.

Solution:

Designed an automated predictive maintenance pipeline architecture on Databricks, integrating real-time PI system data with historical compressor telemetry. The system applied predefined engineering rules for each operational stage (covering Enclosure, Pre-lube, Yard Valve, Ignition, and On-load conditions) to flag deviations from normal operating ranges. Anomaly detection models tracked sensor trends like voltage stability, seal gas pressure, and turbine differential pressure to identify early signs of failure. A machine learning layer powered by MLflow-tracked XGBoost and time-series anomaly detection models predicted potential component failures up to 7 days in advance. The workflow used Delta Live Tables for data standardisation, and LangGraph orchestration to trigger automated alerts and generate daily performance summaries for engineers.

Challenge:

A major energy operator needed an efficient way to process and evaluate thousands of daily operational and compliance documents. Manual verification led to inconsistent results and delayed decision-making in field operations.

Solution:

We designed a Databricks-based Retrieval-Augmented Generation (RAG) workflow to automate document interpretation and validation. The planning phase focused on establishing a LangGraph-powered multi-agent architecture capable of parsing structured and unstructured data across Excel, PDF, and operational reports. A Delta Lake-backed ingestion layer standardised extracted data, while an Agent Evaluation Framework was introduced to ensure every model-generated output met factual accuracy and operational compliance benchmarks. The pipeline leveraged DeepEval to continuously assess agent reliability and MLflow Evaluate for precision tracking and performance benchmarking. A modular orchestration pattern allowed the RAG system to dynamically scale across multiple use cases, from report summarisation to anomaly flagging, while maintaining audit traceability and explainability within Databricks.