Solutions
Software Development Acceleration
For CEOs and engineering leaders who've paid for AI coding tools and can't see the difference on the roadmap. We help your team make agents dependable on the codebase you already have.
You're likely a fit if…
The tools are bought. The roadmap looks the same.
You've heard about gains elsewhere and can't find them in your own delivery.
Agent output on your real code needs heavy cleanup.
It works on a fresh demo project and falls apart on the system that pays the bills.
Senior engineers are drowning in review.
There's more code to check, and less confidence in it.
Everyone uses AI differently.
There's no shared practice, so AI adoption depends on a few enthusiasts and lessons stay with whoever learned them.
What we build
Specs that name the real code
Tickets turned into specs that point to the actual files, patterns and conventions before an agent starts.
Learn more: Specs that name the real codeReview the author didn't write
A separate reviewer with fresh context checks each change against its spec.
Evidence instead of assertions
"Tests pass" arrives with the test output, so reviewers check facts.
An engineer approves every merge
Agents add throughput. Your team keeps the judgment and the accountability.
Your team's knowledge, written for agents
Conventions, architecture and hard-won lessons captured as skills the agents use on every task.
A weekly improvement loop
A regular look at where agents went wrong, and a fix to the harness so it doesn't happen twice.
Scope
Included, and not
A pilot on one repository, harness setup, team skills, coaching and the weekly loop, then a plan to extend it. Not included: taking over your roadmap, or tool licensing.
What we need from you
An engineering leader as owner, access to the repository, CI and issue tracker, a pilot team, and weekly review time. Code stays in your repositories.
What drives cost
The number of teams and repositories, the size and age of the codebase, the state of your tests and CI, and the tools you use today. Builds are scoped and priced after the assessment.
How we'd start
Start with an Assessment, or a focused engineering review if your development team is the whole question.
Run a pilot: one real repository, one representative ticket, one team practice.
Baseline how the team works today, so the change is measured rather than anecdotal.
Extend to more teams once the pilot holds up.
Hand over: your team owns the harness, skills and loop. PRFlow, an open-source project I lead, is an option, not a requirement.
Common questions
Which AI coding tools do you work with?
Where we can, the ones your team already uses. The harness around the model matters more than the tool. We recommend a change only when the tool is the bottleneck.
Is it safe to send our code to an AI provider?
It can be, on business terms your security team reviews. We check the provider's data-use terms with you before any code is involved.
How do we measure whether it worked?
We agree the measures before the pilot, such as time from ticket to merge, rework found in review and defects after release, baseline them, and compare.
How long does it take?
A pilot is usually S (1–3 weeks). Rolling out across teams is M (1–2 months) to L (3–6 months), depending on how many teams and codebases.
Industries where this applies
Let's find where AI pays off in your business.
Thirty minutes with Daniel. No pitch deck: you talk about your business, I tell you honestly where AI fits and where it doesn't.