AI-augmented software development means using AI, and especially AI coding agents, in every stage of building software: planning, writing code, reviewing it, testing it and maintaining it. Done well, the same team ships more, with the same or better quality. Done badly, it produces more code that nobody fully understands.
We help engineering teams adopt AI across the software development lifecycle (SDLC) in a way that keeps quality, security and ownership where they belong.
Who it is for
This service fits teams that:
- Have developers using AI coding tools individually, with uneven results.
- Want to try coding agents that open pull requests, but worry about quality and security.
- Spend too much time on reviews, tests, upgrades or documentation.
- Are a small team that needs to deliver like a bigger one.
- Need a policy on which AI tools may see the company's code.
What you get
- An assessment of your current workflow. How work moves from idea to production today, and where AI would help most.
- Coding agents in the loop. Agents set up for the tasks they do well, such as drafting changes, writing tests, explaining unfamiliar code, upgrading dependencies and first-pass reviews.
- Repository and CI conventions. Instructions files, task templates, test and lint gates, and review rules that let agents work safely in your codebase and make their output easy to check.
- Guardrails and policy. Which tools are approved, what code and data they may access, and where a person must review.
- Team enablement. Hands-on coaching with your developers on real tickets, not slides.
How it works
1. Listen
We review your repositories, your delivery pipeline and a sample of recent work, and we talk with the developers about where time goes. We note what is already automated and what slows the team down.
2. Shape
We pick a few high-value uses, such as test generation or dependency upgrades, and define how to measure them, for example time from ticket to merge, review effort or escaped defects.
3. Build
We set up the tools and conventions in your repositories and pipeline, then work through real tickets with the team. Developers learn by shipping, with us alongside.
4. Sustain
We compare the results with the starting point, adjust the conventions, and leave a short playbook the team owns, so new members pick up the same habits.
What stays the same
Humans stay responsible for what ships. Agents propose changes; people review, approve and own them. Your existing standards for tests, security review and code quality still apply, and the conventions we add make them easier to enforce, not easier to skip.
How long it takes
A focused engagement for one team usually runs a few weeks: a short assessment, set-up, and then coaching on real work. Larger organizations roll it out team by team. We agree the plan once we have seen your codebase and pipeline.
How pricing works
We start with a free conversation about your team and how it builds software. We then quote a defined scope for the first team or repository, so the cost is clear before we begin. Licenses for AI coding tools are separate and paid to the vendors; we help you choose plans that fit.
Common questions
Will AI-written code be lower quality?
Not if it goes through the same tests and reviews as any other code. Much of our work is making those checks automatic, so AI output is held to the same bar.
Is our code safe with AI tools?
It depends on the tool and its settings. We help you choose tools and configurations that meet your requirements, and set clear rules on what may be shared.
Do we need to change our stack?
No. We work with the languages, repositories and pipeline you already have.
How is this different from giving everyone an AI assistant?
An assistant helps one person type faster. Augmenting the SDLC changes the team's process: what agents do, how their work is checked, and how it is measured.
This often sits alongside agent development and team playbooks.