Better code quality is a process problem.
AI has made writing code cheap. The hard part is everything around it: knowing the work is correct, knowing who decided what, and knowing the process holds when people are tired. We work with engineering teams on exactly that.
Predictable outcomes are a habit, not a tool, and habits come from understanding the process. We teach that as much as we build it — every engagement should leave a team more capable than we found it.
Teams rarely have a tooling problem
In most engagements the tools are already fine. What is missing is a shared, written answer to a handful of questions that every team ends up answering differently, badly, or not at all.
What “done” means here
Without a written bar, quality becomes a matter of who reviews it and how much time they have. Two engineers, two standards, and an argument every time.
Who decided, and why
Design decisions live in meetings and disappear. Six months on, nobody can reconstruct the reasoning, so the same debate runs again.
What the tests actually cover
Coverage numbers look reassuring while the tests assert nothing meaningful. Green builds become a signal with no information in it.
Where work is actually stuck
Without a written process there is no way to see where time goes. Bottlenecks are felt rather than measured, and fixes go to the loudest complaint.
Who is on call and why
Operational knowledge accumulates in individuals. That is a business risk wearing a technical costume, and it gets worse with attrition.
What happens when AI writes it
Generated code arrives faster than the process can check it. Review capacity becomes the real constraint, and quality becomes whatever the reviewer had time to look at.
Every one of these is solved by writing the process down and then improving it deliberately. That is unglamorous, and it is the actual work.
Evidence first, tooling second, always reversible
We are not attached to a particular product, and we would rather tell you that a problem does not need us than sell you a remedy. The sequence below is deliberately conservative, because process changes fail when they are introduced faster than a team can absorb them.
Observe before concluding
We watch the work happen rather than inferring it from the org chart. The cause of a recurring problem is usually not where the team thinks it is.
Write it down before changing it
Documenting the current process exposes the parts nobody agrees on. You cannot improve what you have not first made explicit.
Change one thing at a time
A process change introduced alongside five others cannot be evaluated. We make one alteration, watch the effect, and adjust.
Leave it owned locally
Anything we build lives in your repositories and is understood by your team without us. A process that depends on a consultant is not a process.
Teach, don't just hand over
A team that understands why the process is shaped a certain way will adapt it when the situation changes. One that is merely following steps will have no idea what to do the first time something does not fit.
What an engagement looks like
Most work begins with an assessment and then narrows to one or two of these. They are listed in the order teams usually need them, not as a menu of things to buy.
Engineering process assessment
Typically the starting point
Interviews with the people doing the work, a review of the codebase and the tooling, and a written findings document that names the actual constraints. You get a clear picture of where quality is lost and what it would cost to fix, whether or not you engage us further.
Deliverable: a prioritised assessment with specific recommendations.
Development process design
For teams whose process is undocumented
We write down how your team actually works — review expectations, release cadence, escalation paths, quality gates — and turn it into a standard people follow rather than interpret. Where a step is missing entirely, we design it and test it against real work before asking anyone to adopt it.
Deliverable: a documented, agreed development process.
AI-assisted development workflows
For teams already using AI, inconsistently
Structured workflows written against your codebase and your review standards, so the same task produces a comparable result regardless of who runs it. Validation runs as ordinary code, and approval points sit where judgment genuinely belongs.
Deliverable: repeatable workflows your team owns and can edit.
Legacy codebase remediation
For systems that resist change
Characterisation tests that pin down current behaviour, then upgrades and refactors applied one verified slice at a time. Also where knowledge capture belongs, for code that only a few people fully understand.
Deliverable: incremental, verified improvement with no big-bang rewrite.
Team enablement
For teams that want to run it themselves
Working sessions with developers on the practices themselves — how to review AI output, how to write acceptance criteria that hold up, how to keep the process current as the team changes. We would generally rather your team not need us afterwards.
Deliverable: a team that maintains the process without external help.
How it starts, and what it costs
The first conversation is free and usually settles whether there is anything worth doing. If there is, the next step is a paid assessment with a fixed scope and a written deliverable — so you know exactly what you are buying before anything else begins.
Conversation
Thirty minutes on what you are seeing and what it is costing you. No charge, and a straight answer if we are not the right help.
Assessment
Fixed scope and fee, ending in a written findings document you keep regardless of what happens next.
Focused work
One clearly scoped piece of work at a time, with a defined end. No open-ended retainers by default.
Handover
Documentation and training so the practice belongs to the team. Ongoing support only if you want it.
Hegeman LLC
An independent consultancy working with engineering teams on process, code quality and the practical adoption of AI-assisted development. We work with a small number of clients at a time, because the work is close enough that attention gets divided if it does not.
The goal is simple: your team understands its own process well enough to keep improving it after we have gone. Everything else is in service of that.
If this sounds like the problem you have, let's talk.
Send a message and we can set up a short conversation about what you are seeing.