From frozen legacy to AI-powered velocity: transforming a global investment and insurance firm

A firm that manages billions running on software built for a different era

Our client is a globally operating investment and insurance conglomerate managing assets across multiple continents. With thousands of employees, a complex regulatory footprint, and mission-critical systems underpinning every trade, policy, and customer interaction the stakes for their technology infrastructure could not be higher.

Yet beneath their modern-facing operations ran a patchwork of legacy applications some written over two decades ago, in languages and frameworks that had long fallen out of mainstream use. New hires couldn't read the codebase. Senior engineers who understood it were retiring. Every release cycle was a calculated gamble, and every sprint ended with a growing list of technical compromises carried forward into the next one.

Four compounding problems that had no simple fix

Through deep discovery workshops with technology, operations, and business leadership, Devopstrio mapped the root causes behind the modernisation stall. The problems were structural not just technical.

Inconsistent development standards

Thirty-plus developers across three geographies writing code in different styles, patterns, and conventions making every merge a negotiation and every review a source of delay.

Opaque legacy codebase

Millions of lines of undocumented, untested code with no living architecture map. Understanding what a module did often required reverse engineering it from scratch a task that could take weeks.

Slow, high-risk releases

Release cycles stretching across months. Each deployment carried the anxiety of unknown dependencies, manual testing bottlenecks, and rollback procedures that rarely worked cleanly.

Eroding developer confidence

Top engineers were spending the majority of their time on maintenance and firefighting rather than building new capabilities leading to burnout, attrition, and growing cost-to-serve.

Critically, these were not problems that could be solved by hiring more developers or throwing a longer migration timeline at the board. The organisation needed a fundamentally different approach to how software was built and governed.

GenAI not as a tool but as the foundation of an entirely new way of building software

Devopstrio's conviction from the outset was clear: incremental modernisation was insufficient. Grafting new tools onto broken processes produces better-tooled broken processes. Instead, the team designed a GenAI-native development operating model one where AI was embedded into every workflow, from initial code understanding through to deployment.

Legacy codebase comprehension at scale

Before a single line could be rewritten, the team needed to understand what existed. Devopstrio deployed AI-assisted code analysis tools that systematically parsed the existing application estate generating structured documentation, dependency maps, and risk-ranked modernisation backlogs in a fraction of the time manual analysis would have taken

AI-assisted code generation and refactoring

With a clear picture of the codebase, teams used GenAI generation workflows to accelerate the rewriting of legacy modules. Rather than hand-coding replacements, developers worked in a human-in-the-loop model reviewing, steering, and validating AI-generated output

Standardised AI-enforced development workflows

Devopstrio introduced structured prompt-driven workflows and AI-powered code review pipelines that enforced consistent standards automatically. Every pull request was evaluated not just for correctness, but for adherence to architectural patterns, security baselines, and compliance requirements relevant to the financial services context

Regulated-environment continuous delivery

Modernising the application layer meant nothing if releases remained slow and risky. Devopstrio rebuilt the delivery pipeline from the ground up incorporating automated testing, compliance-aware deployment gates, and AI-assisted rollback decision logic.

Developer capability uplift and change management

Technology transformation at this scale succeeds or fails on human adoption. Devopstrio ran embedded enablement programmes alongsiclose up of notebook used by employees developing ai systems in tech startupde the technical work upskilling development teams in AI-assisted workflows, shifting cultural mindsets around what "good engineering" looks like in a GenAI-augmented environment, and establishing internal champions who could sustain the model independently post-engagement.

Transformation that shows up in the numbers and in how the organisation operates

Twelve months into the engagement, the results were measurable across speed, quality, cost, and culture. But perhaps more significantly, the client had built internal capability to sustain and extend the model reducing ongoing dependency on external partners.

Accelerated modernisation velocity

Release cycles that previously stretched across quarters were compressed to weeks. Teams were shipping modernised modules at three times the rate of the pre-engagement baseline without increasing headcount or compromising quality.

Reduction in post-release defects

AI-enforced coding standards and automated review pipelines dramatically reduced the inconsistencies that had been generating the majority of production incidents. The QA team shifted from firefighting to forward-looking quality engineering.

Developer productivity improvement

Engineers reclaimed the majority of their capacity from rework and repetitive tasks. Time previously lost to manual code translation and legacy archaeology was reinvested into building new capabilities that drove direct business value.

Compliance audit pass rate maintained

Despite the significant increase in release frequency, the firm maintained a clean compliance record throughout the transformation a critical proof point for regulators and internal governance that speed and control are not mutually exclusive.

Building a self-sustaining engine for continuous modernisation

The initial engagement delivered transformation. The second phase is about institutionalization. Devopstrio is now working with the client to embed the GenAI-native development model as a permanent operating capability not a project, but a practice. This includes expanding AI-assisted workflows to adjacent business units, building internal AI engineering guilds, and evolving the compliance-aware delivery pipeline to accommodate the firm's growing product portfolio. The modernisation journey is not complete. But the firm is no longer standing still and more importantly, it no longer needs to rely on an external partner to keep moving forward.