6 Million Lines of Legacy Code. £10M/Year Savings Opportunity for Markerstudy.
Allyship Snapshot
Use Cases
- Application portfolio assessment
- Modernization roadmap
- AI and agentic readiness
Solutions
- Strategic Modernization Assessment
- AI code assistants
Outcomes Achieved
The Challenge
The Weight of Two Decades of Growth
Since 2001, Markerstudy has grown into one of the UK’s largest insurance service providers. Today, it protects millions of customers with products spanning motor, home, commercial and pet insurance, and employs over 6,000 people.
At the heart of their operations sit the DISC and Orion platforms, running on IBM Power systems using RPG, a language purpose-built for high-volume transaction processing and trusted across insurance and banking, but not very easy to hire for.
These platforms process around 30 million requests daily, powering policy renewals, quotes, underwriting and policy adjustments at scale. For Markerstudy’s customers, brokers, and colleagues, they’re the engine behind every interaction.
But success at this scale came with a challenge. These platforms had evolved over 25 years through acquisitions and organic growth. Critical knowledge sat with a small number of long-tenured engineers. Documentation was sparse. Maintainability was low. And the tightly coupled architecture made even routine changes slow and risky. These applications had become black boxes: essential but increasingly difficult to evolve.
The Journey
Becoming the Insurer of the Future
Markerstudy didn’t just need to understand their applications, they also needed to work out how best to modernize them to achieve their goals. They needed a path to:
Ensono delivered:
- Reduce cost and risk of change by 25–50% through decoupling and modular services
- Accelerate onboarding of new distribution channels and pricing strategies by 50%
- Accelerate delivery cycles by 30–50% via AI-enabled, spec-driven engineering
- Reduce key-person dependency through AI knowledge capture and shared domain models
- Decommission a legacy document engine, reducing run cost and operational complexity, removing over 10% (567k LoC) of the current DISC codebase through replacement with a third-party platform, removing key-person dependencies and reducing operational risk
- Build API and data foundations enabling AI-driven, agentic pricing and underwriting decisions
- Create a sustainable internal capability through the embedded Ensono + Markerstudy delivery model
To achieve these goals, Markerstudy sought a partner who could deliver an honest, evidence-based answer quickly. They needed deep expertise in legacy platforms, modernization approaches, and the ability to translate technical complexity into boardroom-ready insight. Ensono brought all three.
A Four-week Assessment, Accelerated by AI Code Assistants
The Outcomes
From Black Boxes to Future-ready Assets
This assessment gave Markerstudy’s leadership team the clarity and evidence-based application modernization strategy they needed to move forward with confidence. Now they know where to focus for maximum ROI and to achieve their goals.
“Thanks to this assessment from Ensono, we now have a clear, evidence-based modernization path, one that transforms our core applications running on the IBM Power platform into even greater, AI-ready strategic assets.“
– Andrew Beal, Director of Architecture and Innovation
Frequently Asked Questions:
How can AI code assistants accelerate legacy application modernization assessments?
AI code assistants like IBM Bob Code Assistant and GitHub Copilot can parse, interpret, and validate millions of lines of legacy code in weeks rather than months, dramatically reducing assessment timelines while uncovering technical debt, key-person risks, and modularization opportunities.
What is the best approach to modernizing legacy insurance platforms running on IBM Power systems?
A strategic modernization assessment combines AI-accelerated code analysis, architecture mapping, and risk assessment to create an evidence-based roadmap. This approach identifies quick wins, defines domain boundaries for modularization, and translates technical findings into board-ready insights.
How can IT leaders reduce key-person dependency and technical debt in legacy applications?
By using AI tools to capture institutional knowledge, document domain logic, and create shared models, IT leaders can reduce reliance on long-tenured engineers. Decoupling tightly integrated systems into modular services also lowers the risk and cost of change over time.
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