Case Study
How Line4 AI Pods bridged existing Power Platform systems with modern AI native engineering inside a global financial regulator, delivering $1.5m+ in annual savings.
$1.5m+
annual savings
4
systems in production
6
months total engagement
The Challenge
A global financial regulator faced a critical technology gap. Core supervisory functions including AML oversight, regulatory reporting, enforcement case management, and prudential submissions were running on disconnected legacy systems, manual Excel workflows, and expensive vendor platforms with slow change cycles.
The regulator had invested heavily in Microsoft Power Platform but lacked the engineering capability to extend it with modern AI native approaches. They needed a partner who could bridge their existing platform investment with agentic engineering, all while operating inside their governance framework.
Traditional consultancies had quoted 12 to 18 month timelines. The regulator needed a fundamentally different delivery model.
The Solution
Line4 deployed dedicated AI Pods inside the regulator's security perimeter. Each pod combined senior engineers with agentic AI workflows, bridging the existing Power Platform estate with modern engineering practices. All four workstreams ran in parallel with independent delivery cadences.
Impact
$1.5m+
annual savings across a 6 month engagement
4
production systems
0
vendor lock-in
100%
code ownership
4
AI Pods deployed
The AI Pod Model
Each AI Pod operated inside the regulator's environment from day one. Same tools, same governance, same security perimeter. Pod engineers worked alongside supervisory staff to understand workflows before writing a single line of code.
The regulator had a significant investment in Microsoft Power Platform. Rather than rip and replace, our AI Pods extended and modernised these systems by layering AI native capabilities on top of the existing estate. The result was faster delivery with less disruption.
Every AI Pod used agentic AI workflows to accelerate delivery. From parsing unstructured supervision data to generating validation logic, AI was not a product we sold. It was how our pods worked faster and delivered more with smaller teams.
Four AI Pods ran four workstreams concurrently with independent delivery cadences. Each pod had its own backlog, its own release cycle, and its own path to production. No dependencies between pods, no bottlenecks.
Four AI Pods. Four production systems. $1.5m+ in savings.
All delivered within a single six month engagement.
Technology
Microsoft Power Platform
Extended and modernised
Azure
Cloud infrastructure
Claude AI
Agentic engineering workflows
Python
Data pipelines and AI
DocuSign
Digital signatures
Microsoft 365
Outlook, Teams integration
TypeScript
Web portal development
Power BI
Analytics and reporting
Book a conversation with our team to explore how Line4 AI Pods can deliver the same impact for your organisation.