AI-assisted development is the biggest change to how software gets built in a decade, and it's happening whether your team has a plan or not. Engineers are already pasting code into chatbots. The question is whether that becomes a disciplined, measured capability — or a quality and security problem you find out about later.
We help engineering organizations adopt AI properly: the right tools, agentic workflows for the boring work, standards that keep quality up, and numbers that prove it's working. We use these workflows to build our own products every day — we're not reselling a course.
Who This Is For
- CTOs and VPs of Engineering who see competitors shipping faster and want the same, minus the horror stories
- Teams that bought Copilot licenses a year ago and have no idea if anything changed
- Organizations whose security team currently answers every AI question with "no" — and need a policy that lets them say "yes, like this"
What We Set Up
Tool Evaluation & Rollout
Claude Code, GitHub Copilot, Cursor, and the rest — evaluated on your codebase, with your engineers, against your security constraints. The measured results pick the stack, not the marketing.
Agentic Workflows
Coding agents for delegated tasks, AI code review in CI, test generation, documentation upkeep — the repetitive work moved off your engineers' plates, with guardrails.
Standards & Guardrails
What to delegate, how to review AI-written code, where agents may act autonomously, and a security policy your compliance team can sign. Written with your seniors, not imposed on them.
Enablement & Measurement
Hands-on training for your engineers, and a before/after on your own numbers: cycle time, review turnaround, DORA-style delivery metrics, adoption depth.
AI Workflow Pilot 4 weeks · one pilot team
Four weeks with one of your teams:
- Week 1 — baseline your current delivery metrics and workflows
- Weeks 2–3 — roll out tools, agent workflows, and working standards; hands-on enablement
- Week 4 — measure, compare, and deliver the adoption playbook for the rest of the org
You end with real numbers from your own codebase and a playbook to scale what worked. If it didn't pay, the numbers will say that too.
Need the broader question answered first — where AI helps your business beyond engineering? Start with an AI Opportunity Scan. Want deeper skills training alongside the rollout? See AI Training.
FAQ
Does AI-assisted coding actually improve productivity, or does it wreck code quality?
Both outcomes are real — the difference is discipline. Teams that adopt AI coding tools with clear standards (what to delegate, how to review AI output, where agents may and may not act) see large, measurable speedups. Teams that just hand out licenses get inconsistent usage and quality regressions. Our whole service is installing the discipline along with the tools, and measuring the result.
Which AI coding tools do you recommend — Claude Code, GitHub Copilot, Cursor?
It depends on your stack, security constraints, and how your teams work; often the answer is a combination — an agentic tool like Claude Code for larger delegated tasks and an IDE assistant for in-flow completion. We're vendor-neutral: we run the evaluation on your codebase with your engineers and let the measured results pick the tools.
What about security, IP, and compliance when engineers use AI tools?
This is solvable and usually the first thing we sort out: enterprise agreements with zero-data-retention terms, self-hosted or VPC options where required, policies for what code and data may reach which tool, and audit trails for agent actions. You get a written policy your security team can actually sign off on.
How do you measure whether AI adoption is actually working?
We baseline before the pilot and measure during it: cycle time, review turnaround, deployment frequency and change-failure rate (DORA-style), plus adoption depth and engineer-reported friction. You see the before/after on your own numbers — if the pilot doesn't pay, you'll know that too.
Our senior engineers are skeptical of AI coding. How do you handle that?
Skepticism from seniors is healthy — they're usually right about naive usage. We don't evangelize; we demonstrate on your hardest real tickets, put the skeptics in the pilot group, and let the diffs argue for themselves. Standards written with your seniors, not imposed on them, is what makes adoption stick.
How long until we see results?
The AI Workflow Pilot runs 4 weeks: baseline in week one, tooling and standards in weeks two and three, measurement in week four. Meaningful signal on a pilot team inside a month; organization-wide rollout typically follows over the next quarter using the playbook the pilot produced.
Want your team shipping faster next month?
Book a free 30-minute call. We'll tell you what a pilot would look like for your team and stack.
No pitch deck, no obligation. If AI is the wrong answer for your problem, we'll tell you that too.