How Evinova accelerates regulated software delivery with Devin

Overview

Evinova is a separate health tech company within the AstraZeneca group that delivers intelligently designed digital and AI-native solutions to biopharma companies and contract research organizations to optimize the entire clinical development lifecycle from end to end.

"Evinova is helping life sciences customers deliver groundbreaking, industry-first outcomes for patients at record speed by harnessing the latest advances in AI. But we can't credibly transform how our customers design and run clinical trials if we haven't transformed how we build the software that runs them. Our promise to customers is simple: the software shaping the future of clinical development is built by a company already living in that AI-native future."

Sean Connolly, Vice President, Chief Product and Technology Officer of Evinova

The Challenge

As an AI-native clinical trial technology company operating in a GxP-regulated environment, Evinova's engineering organization holds itself to the highest standards: every line of code must be traceable, every change auditable, and every release defensible to regulators. Within that operating model, the team identified a clear opportunity to deploy AI agents against the structured, well-bounded work that surrounds product development — backlog triage, regulatory documentation such as User Requirement Specifications and Disaster Recovery Plans, and the coordinated migrations that come with running a modern platform at scale.

The opportunity was meaningful. Regulatory documents typically required 35 to 40 hours of cross-functional coordination across engineering, product, and QA to assemble the necessary context. Evinova saw a chance to compress that cycle significantly while raising consistency and traceability — freeing senior engineers to focus on the differentiated platform work that sets Evinova apart in the market.

The team had evaluated the leading AI coding tools and set a deliberately high bar: any solution would need to own a task end-to-end — read a ticket, explore the codebase, write the code, run the tests, and open a pull request — while preserving the full audit trail that GxP and 21 CFR Part 11 demand. That standard is what led Evinova to Cognition.

Why Devin

Pete Nellius, who leads Future Product Discovery and AI initiatives at Evinova, championed bringing Devin into the organization. During the evaluation, the team tested every major AI coding tool against real engineering challenges. One test involved a complex task with dependencies on third-party libraries where none of the underlying logic existed. Devin was the only tool that completed it.

"The way Devin approached the problem and was able to iterate on its solution until it resolved the problem was ultimately why we decided to go with this tool."

Shaun Phillips, Director of Engineering for the Study Designer team

Other large regulated companies were already using Devin, which gave Evinova's leadership confidence that the tool could meet their compliance and security requirements.

Use Case: GxP Documentation

User Requirement Specifications are foundational regulatory artifacts. Producing one means pulling requirements from Jira, tracing them to the underlying code, organizing them into testable acceptance criteria, demonstrating test coverage and evidence, and formatting everything to a standard that makes regulatory audits routine. For a major software module, that work has historically taken 35 to 40 hours of senior engineering, product, and QA time — with much of that effort spent assembling context that lives across multiple systems and teams.

Accuracy under regulatory scrutiny was the central evaluation criteria across AI development tools. Evinova pointed Devin at the codebase and Jira and tasked it with generating URS documents directly from source. Devin produced structured first drafts at roughly 90% accuracy, shifting senior engineers from primary authors to reviewers — the role where their regulatory judgment is most valuable.

They used the same approach for Disaster Recovery Plans.

"It was done in five to ten minutes. The cursory overview was 90% accurate."

Sudha Panuganti, Senior Director of Product Engineering

The team was able to refocus its time on edge cases, more complex DR scenarios, and more effective DR test cases and end to end simulations.

The outcome was a deeper, more meaningful alignment with key life sciences software regulations and a stronger set of plans and evidence for Evinova customers to leverage for clinical use qualification.

Use Case: Automated Bug Triage and Resolution

Bug triage is exactly the kind of recurring, well-bounded engineering workflow where autonomous agents can compound value over time. Rather than treating it as a manual process to optimize, Phillips's team designed it as a system: AutoFixer, a Devin playbook that runs four times a day and works through open Jira tickets end to end — reading the ticket, exploring the codebase, implementing a fix, running tests, and opening a pull request for human review.

The results validated the approach quickly. Within 22 days, AutoFixer had attempted 79 bugs — merging clean, review-ready fixes for half and getting engineers most of the way to resolution on another quarter. Phillips estimated a 100% return on his time investment: under two days of setup yielded three to four days of saved engineering effort, with compounding returns as the playbook continues to run. The pattern is portable by design — other squads can replicate the workflow against their own repositories with minimal configuration, turning a single team's investment into a platform capability across Evinova engineering.

Use Case: Tech Stack Migration

Evinova's engineering organization continuously evolves its technology stack to stay at the frontier of AI-native software development. As part of that ongoing investment, the team migrated existing product components to a modern TypeScript/Next.js architecture — a stack better suited to the agent-driven development workflows, rapid iteration cycles, and composable UI patterns that define how leading AI-native products are built today. The work spans backend logic translation, API updates, UI reconstruction, and full validation, and a single component has historically required about five days of focused engineering time.

The team set out to migrate one component of a legacy product to test the feasibility of an agent-led migration. Devin completed three components in roughly a day and a half — touching 58 files across three repositories and surfacing only nine points where it needed engineer input. The pattern is now repeatable across the remaining components, allowing Evinova to advance its modernization roadmap at a pace that matches the speed of innovation in the broader AI-native ecosystem.

Use Case: Test Automation

Comprehensive test automation is foundational to Evinova's quality posture — the more coverage the team can build and maintain, the more confidently it can ship at the cadence regulated software demands. The team's automation framework pairs Playwright with Cucumber-based BDD scenarios — a pattern that doubles as living regulatory documentation by mapping test coverage directly to user requirements — with test specifications managed in X-Ray and traceability anchored in Jira.

Evinova reframed test script development as another well-bounded workflow ready for agent ownership. Before Devin, the team projected two to three quarters to reach near-complete test automation coverage. Now, Panuganti said they're looking at weeks.

What Comes Next

Evinova is scaling the agent-led playbook across the organization. AutoFixer is expanding from bug resolution into feature development, and the workflow patterns are being rolled out to engineering teams across Evinova — turning early wins into a shared platform capability.

The impact extends beyond throughput. For Shaun Phillips, Devin has changed what's possible from the engineering leadership seat. Senior leaders at Evinova spend much of their day in strategy, architecture, and people work — by design. Devin gives them a way to stay close to the code without competing for the same hours. "I would previously ping one of my lead engineers with questions," Phillips said. "That question now goes straight into Devin and I get an immediate answer, without requiring a human engineer to be online, available, and context switch."

It has also let him ship again and keep his engineering skills sharp. "Devin has given me, as an engineering manager, the ability to balance my leadership role and directly contribute to our products," Phillips said. "I just ping Devin and provide human oversight as Devin handles the ticket end to end, and spits out a pull request on the other side."

Most software organizations are bolting AI onto how they already work. In partnership with Cognition, Evinova is rebuilding the operating model itself — leaders shipping code alongside their teams, engineers freed for the work only humans can do, and capabilities that compound across squads rather than getting trapped inside them. In an industry where the pace of AI progress is outrunning most enterprise software organizations, that operating model - coupled with tools like Devin - is what lets Evinova ship clinical trial technology at the speed the moment demands — and what makes it the kind of engineering organization the best builders want to join.

Evinova at a Glance