Case Studies

Challenge

Legacy on-premises infrastructure across 1,300+ workloads with strict regulatory requirements (APRA CPS 234). The existing environment was reaching end-of-life with growing operational costs and limited scalability.

Approach

• Migration readiness assessment across all workloads with risk scoring
• Multi-cloud architecture design (AWS primary, Azure for Microsoft workloads)
• Phased migration strategy: non-critical systems first, production last
• Compliance mapping to APRA CPS 234 and ISO 27001 controls
• FinOps framework to track and optimise cloud spend from day one

Results

Challenge

Manual deployment processes taking 2-3 weeks per release. No automated testing, security scanning, or infrastructure-as-code. Development and operations teams working in silos with high incident rates.

Approach

• Current-state assessment of delivery pipeline and team structure
• CI/CD pipeline design with automated security scanning at every stage
• Infrastructure-as-code migration using Terraform and Azure DevOps
• Team coaching: embedded with developers for hands-on upskilling
• Incident response framework with automated alerting and runbooks

Results

Challenge

High-volume manual processes across operations, compliance, and customer service. Teams spent 60%+ of their time on repetitive tasks: data entry, document review, approval routing, and status reporting. Previous automation attempts with traditional RPA had stalled due to brittle integrations and unstructured data.

Approach

• Process mapping across 12 business units to identify automation candidates ranked by effort, volume, and error rate
• Designed agentic AI architecture using LLM-powered agents for document understanding, decision support, and multi-step task orchestration
• Built autonomous workflow agents that handle intake, classification, routing, and escalation with human-in-the-loop checkpoints
• Integrated with existing systems (ServiceNow, SAP, SharePoint) via API orchestration layer
• Implemented responsible AI guardrails: audit logging, confidence scoring, and fallback to human review below threshold

Results

Challenge

Urban parking infrastructure generates massive operational inefficiency: drivers spend an average of 17 minutes per trip searching for parking, councils lack real-time occupancy data for enforcement and planning, and parking operators run on manual processes with no predictive capability.

Approach

• Designed cloud-native platform architecture for real-time parking occupancy detection using IoT sensors and computer vision
• Built predictive availability engine using historical occupancy patterns, event calendars, and weather data
• Developed driver-facing mobile app with turn-by-turn navigation to available spots and pre-booking capability
• Created operator dashboard with real-time occupancy heatmaps, enforcement alerts, and revenue analytics
• Integrated with council parking infrastructure and payment systems via open API layer

Results

Challenge

Government departments processing thousands of citizen submissions, policy documents, and regulatory filings manually. Policy analysts spent 70% of their time on document triage and summarisation rather than analysis. Cross-departmental knowledge sharing was non-existent, leading to duplicated research and inconsistent policy recommendations.

Approach

• Built an AI-powered document intelligence platform for automated ingestion, classification, and summarisation of citizen submissions and policy documents
• Designed multi-agent architecture: intake agent for classification, analysis agent for sentiment and theme extraction, synthesis agent for cross-submission pattern identification
• Implemented knowledge graph connecting policy areas, stakeholder positions, and historical decisions to surface relevant precedents
• Created analyst workbench with AI-assisted drafting, fact-checking against source documents, and confidence scoring
• Deployed with full audit trail, explainability layer, and human-in-the-loop approval gates for all AI-generated outputs

Results