How Elite Is On Track to Double Its 2025 Output—in Just Six Months

Elite is the operating system for the business of law. Its flagship platform, 3E, connects financial management, billing, and practice operations for law firms worldwide, serving about 2,000 firms including 75% of the AmLaw 100 and 72% of the Global 100. With products spanning financial management, e-billing, and compliance, Elite's engineering teams sit at the center of how the world's most prestigious law firms run their business.

Across multiple product teams spanning dozens of engineers, Elite's leadership wanted to move beyond individual AI tool experimentation and toward systematic, organization-wide adoption. GitHub Copilot was already deployed, but the tool's value wasn't compounding across teams. The question wasn't whether AI coding tools were worth using. The question was whether Elite could build the habits, workflows, and culture to get consistent returns at scale.

Why now? Development velocity had become a competitive edge in legal tech, and Elite's leadership knew ad-hoc adoption would produce uneven results. They needed a structured approach that worked across very different technical environments — from legacy XML-heavy codebases to modern agentic development pipelines.

DevClarity partnered with Elite on an AI Coding Jumpstart, beginning with foundational training for engineers across the organization. From there, DevClarity worked hands-on with four product teams to turn training concepts into production-ready workflows and team-owned artifacts.

The engagement included:

• • Foundational Training. Delivered hands-on AI coding sessions to engineers across nine teams, establishing shared vocabulary, best practices, and a consistent baseline for tool use.
• • New Development Workflow. Built a seven-prompt library with Elite's financials team for their XML-heavy environment — covering e-invoicing, refactoring, and bug-to-test automation — with all prompts committed to the team's repo for ongoing iteration.
• • QA Testing Framework. Built an AI-native testing framework with Elite's QA team from scratch. The team's real bottleneck turned out to be test data infrastructure rather than test scripting, so the work delivered a data manager library, a Playwright test generation workflow, and a skills package the team can extend independently.
• • Agentic SDLC Validation. Worked with Elite's most advanced product team on their autonomous coding pipeline (an agent-driven software development lifecycle), building four validation skills — code review, simplification, requirements refinement, and decision logging — that moved quality checks from post-push pipeline failures to pre-push local validation.
• • End-to-End Agent Pipeline. Partnered with an additional product team on full-stack agentic development, including instructions files, multi-repo workspace agents, and Azure DevOps integration that lets agents autonomously fetch work items, generate PRs, and update tickets.

Throughout the engagement, every session produced working artifacts. Prompts, skills, and workflows were committed directly to team repos and owned by the engineers who built them.

The impact became visible fast. Within weeks of the QA and New Development engagements, one team reported they had tripled their per-sprint output while cutting cycle time from 9 days to 4. Across the organization, Elite hit its third consecutive all-time high in stories and story points delivered.

But the real impact shows in what has changed around the work:

• • Teams now own their AI workflows. Prompt libraries, skill packages, and context files live in repos and are being iterated by the engineers who built them.
• • AI acceptance rates are running at 95%+ across active teams that have done the work to build great context & skills to increase code quality.
• • Leadership conversations have shifted from proving ROI to scaling access. Elite has since expanded the engagement to include their services team.

Elite now has a working AI coding toolkit: prompt libraries and skill packages committed to repos, validation workflows for autonomous development, a leadership dashboard in production, and teams trained to own and iterate their own AI coding patterns.

What Elite received. Foundational AI coding training for engineers across the entire engineering org, four hands-on team engagements producing owned artifacts, and repeatable AI-native development workflows across the organization. Elite is now on track to deliver in the first six months of 2026 what took all of 2025.