AI coding tools are everywhere. Turning them into faster shipping, better quality and a stronger enterprise value story is the hard part. DevClarity gives your organization a structured path from AI experimentation to AI adoption to measurable impact.
Everything we do drives one of two outcomes: Adoption (raising your whole team's baseline) or Impact (accelerating the work that matters most to the business).
Schedule a callAn AI-enabled software development lifecycle is one where AI works across every phase, from planning and design through build, test, review, deploy and maintain, not just as autocomplete in the editor. Our AI-SDLC Maturity Model defines five levels, from fully manual to autonomous, and we score every team against it phase by phase.
A concentrated first phase sets your baseline, raises the floor across the org, and drives impact in your highest-value work. An ongoing partnership then executes the roadmap, scaled to your size and maturity.
An org-wide survey and leadership discovery, scored into maturity ratings for all seven SDLC phases, plus a baseline of what you measure today. The output is your AI-SDLC Transformation Roadmap: specific milestones per phase that you can prioritize, measure and show your board.
We configure your AI tools to work in your codebase, then train the whole engineering org to a common standard, calibrated to where each team actually is. An advanced automation workshop and office hours follow.
Directed Efforts: paired working sessions on your highest-value projects, each aimed at a roadmap milestone. Your team keeps the skills, context and workflows they build. The phase closes with a re-benchmark and a closing report.
Embedded AI Transformation: ongoing Directed Efforts, team-wide workshops, office hours, a co-owned roadmap, and regular re-benchmarking against your original baseline.
The full arc above: Assess, Train and Apply, followed by Embedded AI Transformation.
The Assess, Train and Apply phases on their own, for teams that want to establish a standard, measurable approach to AI coding fast. Available for engineering, QA, product, and implementation and services teams.
An ongoing partnership for teams already using AI day to day, to keep climbing the maturity curve.
For teams ready for autonomy: an autonomous pipeline that moves qualifying work from ticket to merge without a developer driving each step. Your team governs policy and handles the exceptions.
Standardize AI-assisted coding across portfolio companies and benchmark maturity at the fund level. AI enablement for PE portfolios →
The same Assess, Train, Apply, Sustain model works in any function. Run it as its own engagement, or add it to an engineering engagement.
Automation coverage across unit, integration and end-to-end testing, so quality keeps pace as engineering accelerates.
Research, prioritization, specs and prototyping, for sharper calls on what to build.
Repeatable configuration, data mapping and go-live workflows, for faster time to revenue.
Every engagement is measured against the AI-SDLC Metrics Framework: adoption metrics as leading indicators, impact metrics as lagging ones. You see where you started, where you are, and what moved.