— AI-assisted engineering
Make AI coding work for your organization.
Find what works, manage the risks, and turn individual experiments into shared engineering practice.
— The problem
Your teams are experimenting. The organization isn't learning.
The tools are already in use. What's missing is a way to turn scattered practices into how your engineering team works.
Adoption is uneven.
A few engineers get real leverage. Others tried it once and stopped. Most are somewhere in between, and it's hard to tell who is where.
Good practices stay local.
Useful prompts, workflows, and setups live in one person's head or one team's repo. The next team starts from zero.
Risk arrives quietly.
More code reaches review, faster. Questions about quality, security, and ownership surface after the code ships.
Nobody can say what's working.
Spend goes up and anecdotes pile up, but there's no clear picture of where AI helps and where it gets in the way.
— Approach
Improve the system around the tools.
Making AI coding work across an organization is engineering work on the workflows, context, and guardrails the tools run inside.
Find what works
Look at how your teams really use AI today. Separate the practices that pay off from the ones that only feel fast.
Share it
Turn what works for one engineer into something the whole team can use: shared workflows, configuration, and examples.
Build the supporting system
Give the tools what they need to do good work: project context, clear workflows, review paths, and sensible permissions.
Manage the risks
Keep code quality, security, and review standards intact as more of your code is written with AI.
The goal is an engineering capability your organization owns, not a collection of individual experiments.
— How it works
Start with an AI coding audit.
A quick look at how your organization uses AI in software development today. It shows what is working, what is getting in the way, and what to change. It stands on its own: you get a complete piece of work, not a pitch for the next one.
We talk to the team
Conversations with your engineers and leads about how they use AI coding tools today.
We look at the real work
Workflows, repositories, and tooling, as far as the access you choose to give allows.
We write it up
A report with clear findings and specific recommendations you can act on.
What you get
- The current state of AI-assisted development in your organization.
- Key observations and opportunities.
- Specific recommendations for improvement.
# AI coding audit ## 1. Current state - Who uses which tools, and how - Where AI-written code enters review ## 2. Observations and opportunities - What already works, and where it could spread - What gets in the way ## 3. Recommendations - What to change first, and why - Risks to address, and how
— Experience
Experience you can check.
We build with these tools every day and talk openly about what we learn.
R&D velocity was our biggest pain. A simple feature in our training framework could take two weeks of implementation and review. Matt built us an AI development setup that changed that. The same change is now usually a twenty-minute session with Claude, and it still goes through tests, a pull request and review. PRs close in days instead of sitting open for months, and our engineers extend the setup with their own skills without being asked. It doesn't replace judgment, and we still verify what matters. But we ended up with a tooling system that just works. As we grow the team, we don't need to rebuild anything or hire infrastructure people to support it.
Stanisław Raczyński, PhD
Founder & CEO, Revoize
Our AI coding setup was already productive: nine out of ten pull requests were fine as delivered. But I was getting nervous, because the agents were running with more autonomy and access than I was comfortable with. Matt showed me how to run them confidently and securely, with clear boundaries around what they can touch and a written process that gets better over time. What I valued most was how concrete it was. The report tells you the exact setting to change, how to scope permissions in the tools we're using, and what order to tackle it all in, and I started implementing it the same day.
Jarich Braeckevelt
Founder & Principal Consultant, JBAnalytica · Bruges, Belgium
The whole harness works really well with all our internal tools. Everything is so well wired together and automated — it's fantastic. And it doesn't force anyone into a way of working they don't want. Big kudos to Matt.
Software Engineer, Revoize
From chat to harness
AGIV Builders Talk
From AI helping you write code, to agents writing it, to the environment you build around the agent. A 30-minute talk, then a 30-minute live demo of a development harness working a real task.
— About
We work with you directly.
Gravital Forge is founder-led. When you work with us, you work directly with our engineering leaders.
We use AI-assisted development every day, and we build the systems around it: the shared skills, review paths, permissions, and retrospectives that let coding agents do reliable work on real codebases.
That's what we bring to your team: practical changes, grounded in how your engineers actually work.
— Questions
Practical questions.
What happens during the AI coding audit?
Over 1–2 weeks, we talk with your engineers and leads about how they use AI coding tools, and we look at real work wherever access allows. The goal is to understand what is working, what is getting in the way, and why. The details adapt to your organization.
What do we get at the end of the audit?
A written report on the current state of AI-assisted development in your organization, the key observations and opportunities, and specific recommendations for improvement.
Do you need access to our code?
Not to get started. Conversations with your engineers are enough. Access to real workflows, repositories, and tooling lets us go deeper and give more specific, actionable recommendations. The more we can see of how your team actually works, the more useful the audit is.
Which AI coding tools do you work with?
The work isn't tied to one vendor or tool. We start from what your teams already use. When tool choice is part of the problem, the recommendations say so.
Can you work with our security and confidentiality requirements?
Yes. We work within the policies you already have. If you need an NDA, limited access, or work only on your own systems, tell us at the start and we'll set up the audit to fit. Because the audit can start from conversations alone, access never has to block it.
What happens after the audit?
After the audit, you'll have a clear view of your current state and concrete recommendations. From there, you can run with it internally. If useful, we can also continue working together to implement changes, from light-touch coaching to hands-on support.
— Contact
Let's talk about an audit.
Tell us a little about your team and where you are with AI coding.
Message received.
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