Enterprise AI Strategy & Implementation for Australian Organisations

Challenge:

A global financial institution with operations across multiple jurisdictions was struggling with a knowledge access problem that was quietly draining productivity across every line of business. Policy, compliance, product, and operational documents were spread across legacy intranet portals, SharePoint sites, shared drives, and team-specific wikis — each with its own taxonomy, permissions model, and update cadence. Front-line bankers, relationship managers, and operations staff routinely needed to find authoritative answers to questions about lending policy, AML/KYC procedures, regulatory interpretation, or product eligibility. In practice they were forced to either rely on memory, ping a senior colleague, or navigate four or five different systems to piece together an answer. Search inside those systems was almost universally keyword-based, returning long lists of partially relevant documents with no synthesis, no context, and no source ranking. The consequences were measurable. Employees were losing 5–7 hours per week to information retrieval. Customer-facing turnaround times for non-standard enquiries stretched to days. Worse, the same question asked twice often produced two different answers, creating real compliance and conduct risk. The bank's internal audit team had flagged this inconsistency as a material control gap that needed remediation before the next regulatory review cycle. Leadership had already evaluated several off-the-shelf enterprise search tools and at least one early-generation chatbot pilot. Both had failed: the search tools could not reason across documents, and the chatbot hallucinated answers in a regulated environment where being plausibly wrong is worse than being honestly unsure.

Solution:

Get AI Ready designed and implemented a multi-agent Retrieval-Augmented Generation (RAG) platform on Databricks to unify knowledge access across the enterprise — purpose-built for regulated environments where every answer must be traceable to a source. The architecture used LangGraph-based orchestration to manage the entire query lifecycle. Each incoming question is first classified by intent (policy lookup, compliance interpretation, product enquiry, advisory summary) and routed to a specialised retrieval agent tuned for that document type. The retrieval layer combined dense vector search over fine-tuned financial embeddings with metadata filters drawn from Databricks Unity Catalog, ensuring that every retrieved chunk carried full lineage back to its source document, version, and owning team. A modular Vector Search adapter sat between the orchestration layer and the embedding store, allowing the bank to swap embedding models or storage backends without rewriting downstream logic. Semantic embeddings were fine-tuned on a curated corpus of the bank's own policy and regulatory language, materially improving recall for domain-specific terminology that generic foundation models routinely miss. The synthesis layer dynamically adjusted prompting strategies depending on query intent. Policy lookups used a strict extractive style that pulled verbatim passages with citations. Advisory summaries used a more flexible abstractive style with explicit confidence statements. Every generated response included inline source links, document versions, and a confidence score derived from the underlying retrieval scores. Critically, automated evaluation pipelines using DeepEval Faithfulness metrics were integrated with MLflow Evaluate. Every model output was scored for faithfulness (does the answer actually reflect the source?), relevance, and completeness against a golden dataset curated by the bank's compliance team. Outputs falling below threshold were flagged for human review before being shown to end users. This evaluation harness ran continuously in production, providing the audit trail the internal audit team had been asking for.

Challenge:

A leading energy operator running a fleet of gas compression assets across multiple production sites was facing recurring unplanned shutdowns that were eroding both production uptime and maintenance budgets. Each unscheduled compressor outage cost six figures in lost throughput, plus the secondary cost of expedited parts, overtime crew dispatch, and re-certification of safety systems before restart. The root cause was not a lack of sensor data — the compressors were heavily instrumented with PI historian feeds capturing turbine pressure, lube oil pressure, discharge temperature, seal gas differentials, vibration, and dozens of other signals at sub-minute intervals. The root cause was that nothing was systematically reading that data. Field engineers relied on manual inspection of PI dashboards and spreadsheet-based performance tracking, which made it almost impossible to identify the slow, multi-day drift patterns that precede most compressor failures. Operational parameters were also being logged inconsistently across sites, with different naming conventions and sample rates, so a deviation that was obvious on one asset would be invisible on a sister unit. Engineers knew the warning signs were in the data but couldn't see them until after the failure had happened. The operator had previously trialled a generic anomaly detection product from a major vendor, but it produced too many false positives to be useful and could not be tuned to the specific operating envelopes of each compressor stage. They needed a solution that combined engineering domain knowledge with machine learning, and that ran on the data platform they had already standardised on.

Solution:

We designed an automated predictive maintenance pipeline architecture on Databricks that integrated real-time PI system data with historical compressor telemetry into a single governed lakehouse. The first stage was data standardisation: a Delta Live Tables pipeline ingested PI streams from every site, normalised tag names and units against a canonical schema, and produced a clean Bronze→Silver→Gold flow that engineers across sites could trust. On top of the standardised data we layered predefined engineering rules for each operational stage of the compression cycle — covering Enclosure, Pre-lube, Yard Valve, Ignition, and On-load conditions. These rules encoded the operator's own SME knowledge about safe operating ranges and were the first line of defence: any deviation outside normal envelopes generated an immediate alert with full context, eliminating an entire class of failures that didn't require ML to catch. For the more subtle degradation patterns we built a machine learning layer powered by MLflow-tracked XGBoost classifiers and time-series anomaly detection models including autoencoders. These models tracked sensor trends like voltage stability, seal gas pressure, and turbine differential pressure to identify early signs of failure up to seven days in advance of an actual fault. Each model was versioned in MLflow with its training data, hyperparameters, and evaluation metrics, so that engineers could trace any prediction back to a specific model version and dataset. LangGraph orchestration tied the rules engine and ML models together, triggering automated alerts when either layer flagged an issue and generating daily performance summaries for engineers across every monitored asset. The summaries included plain-language explanations of why a particular alert was raised and what historical pattern it most resembled, dramatically reducing the time engineers spent triaging false alarms. The entire pipeline was built with Unity Catalog governance, so every model, dataset, and alert had clear lineage and access controls — important for an organisation where safety-critical decisions need to be auditable.

Challenge:

A major energy operator was generating thousands of operational and compliance documents every single day across exploration, production, HSE, and asset management functions. These ranged from daily drilling reports and well integrity assessments to environmental compliance attestations, contractor safety records, and regulatory filings. Every one of those documents had to be reviewed, validated, and reconciled against operational data before it could be relied on for downstream decisions. The review process was almost entirely manual. A team of analysts and SMEs read each document, cross-referenced figures against PI historian data and SAP records, flagged discrepancies, and routed items for follow-up. The volume meant that backlogs were chronic, turnaround on critical safety reports could stretch beyond regulator-mandated SLAs, and the same document was often reviewed inconsistently depending on which analyst happened to pick it up. The operator needed a way to automate the routine 80% of document interpretation while still giving humans clear control over the high-risk 20%. Crucially, in a heavily regulated environment, any AI-driven interpretation had to be auditable, reproducible, and continuously evaluated for accuracy — a chatbot that occasionally hallucinated was simply not deployable.

Solution:

We designed a Databricks-based Retrieval-Augmented Generation workflow purpose-built for document interpretation and validation in regulated operational environments. The planning phase established a LangGraph-powered multi-agent architecture capable of parsing structured and unstructured data across Excel spreadsheets, scanned PDFs, operational reports, and email-embedded attachments. Each document type was handled by a specialised parsing agent that understood its expected schema, key fields, and typical anomaly patterns. A Delta Lake-backed ingestion layer standardised every extracted record into a governed canonical format, so downstream consumers (dashboards, alerts, regulatory filings) saw a consistent shape regardless of the source format. The core differentiator was the Agent Evaluation Framework. Every model-generated output — whether a summary, an extracted field, or a flagged anomaly — was scored against factual accuracy and operational compliance benchmarks before being trusted by downstream processes. The evaluation layer used DeepEval to continuously assess agent reliability and MLflow Evaluate for precision tracking and performance benchmarking. Outputs that failed evaluation thresholds were automatically routed to the human review queue with full context about why they failed. A modular orchestration pattern allowed the RAG system to dynamically scale across multiple use cases without rebuilding the foundation. The same core platform was used for daily report summarisation, anomaly flagging in operational logs, and synthesis of multi-document briefings for shift handovers. Audit traceability and explainability were built in from day one through Unity Catalog lineage tracking, so every AI-generated artefact could be traced back to its source documents, the model version that produced it, and the evaluation scores that justified its release.