How EverTrue turned five-month migrations into two-week sprints with DevClarity

EverTrue is a comprehensive advancement platform that helps fundraising teams across higher education institutions, nonprofit organizations, and healthcare systems engage supporters, discover new prospects, and manage donor relationships with greater personalization and impact. By combining modern data infrastructure with intuitive, mobile-first tools, EverTrue enables development teams to coordinate prospect research, portfolio management, volunteer engagement, and personalized outreach in one unified platform.

Today, many of the most sophisticated fundraising organizations—including leading universities, major nonprofit institutions, and healthcare foundations—rely on EverTrue to power their advancement operations. These teams use the platform to unlock deeper insight into their constituents, prioritize the highest-impact relationships, and create more meaningful engagement with donors, alumni, patients, and community supporters.

EverTrue wanted to establish reliable AI coding practices across their engineering team to increase product velocity — and they had a concrete proving ground: consolidating seven distinct tech stacks across nine products into a unified platform. As software systems evolve, they naturally accumulate complexity over time. Different architectural decisions, framework versions, and design patterns can work well on their own, but create friction when combined.

Traditional migration approaches would take years to reconcile these differences. Initial AI-assisted attempts often stalled in the last stretch (around ~80% complete) as subtle feature interactions and edge cases emerged.

The urgency wasn't just about one project. The team needed to increase engineering capacity without adding headcount, and quality could not be sacrificed for speed.

DevClarity partnered with EverTrue on an AI Jumpstart engagement—a structured program combining foundational training, workflow education, and hands-on directed efforts to accelerate AI coding adoption. The goal: transform AI tools from experimental curiosities into dependable teammates embedded in daily practice.

DevClarity began with foundational AI coding training for the full engineering team to establish baseline skills. Then, over a few months, DevClarity engineers worked laptop-open alongside EverTrue's developers on two focused workstreams: platform consolidation and test automation.

The engagement included:

• • Foundational training. The entire engineering team participated in training sessions on AI coding best practices, environmental setup with context files, and first principles of prompt engineering—establishing the foundation for what would follow.
• • Structured Migration Process. Developed a systematic approach for migrating legacy code to modern frameworks with built-in quality checkpoints—generating detailed specifications upfront to capture all existing functionality, then implementing changes in phases with review stages to catch issues early.
• • Automated Test Generation. Created a streamlined process for building comprehensive test coverage using AI-assisted tools, significantly reducing the time required to ensure quality across the platform.
• • Knowledge Management Framework. Established organized documentation systems across codebases to maintain consistency and enable efficient collaboration as the team scales.
• • Specification-Driven Development. Introduced a lightweight framework for planning new features collaboratively, replacing informal processes with structured proposals that the whole team can review and refine.

Throughout the engagement, every challenge became a documented solution. The checkpoint-driven workflows prevented the "80% problem" where AI work stalled in final implementation, while human-in-the-loop review at each step maintained quality standards.

After the engagement, EverTrue reported measurable gains in output and AI adoption with 100% of respondents reported using AI >50% of their coding time and 80% reported daily use.

Beyond the headline metrics, the team described meaningful changes in engineering execution:

• • Faster migration cycles. Work previously estimated at 4-5 months was completed in two-week sprints, and a major frontend rewrite completed between November and February.
• • Testing became the default. Automated tests became a standard expectation on pull requests, with QA using AI-assisted generation while expanding higher-value coverage.
• • Modernization became more feasible. The team progressed multiple product rewrites that had previously been considered too expensive to take on.
• • Repeatable workflows in the repo. Context files, documented patterns, and reusable workflows were committed to codebases so teams could continue improving without relying on ad-hoc prompting.

The business impact was significant: velocity improvements contributed materially to EverTrue achieving strong portfolio performance, with AI-accelerated development helping the team deliver on aggressive integration timelines.

What EverTrue received. EverTrue left with durable, repeatable ways of working: shared training across the engineering organization, documented workflows for migration and testing, and practical patterns embedded in repositories. The result was not just a short-term speedup, but a foundation the team could extend as consolidation work continued.