AI-native software studio · Wake Forest, North Carolina
We design, build and run software with senior engineering judgment and AI agents doing the typing. Products, platforms and SaaS, from first sketch to production, with the evidence to show it works.
And one way of working that runs through all of them: a written spec, machine-run checks, and a person who signs.
Agents that ship, not demos.
Agent harnesses wired to real repositories. Spec formats that agents and people both follow. Reviewer roles, machine-run checks, and a second model that tries to break the work. LLM features inside your product, with evaluation and guardrails around them.
One pair of hands, the whole stack.
Interface design and design systems. Web front ends, mobile apps, APIs, cloud infrastructure as code, sign-in, analytics and release engineering. Every engagement is led by someone who has run each layer in production.
From first sketch to paying customers.
Tenancy, billing, sign-in, usage metering, admin tools, monitoring, and the release process that keeps it running. New builds, and rescues of apps that were built fast and now carry real customers.
Five steps. Each one ends in something you can open: a signed spec, a priced plan, a passing check, an evidence file.
We write down what the system must do, from the running product and from you. You sign it.
The spec becomes milestones. Each milestone has checks a machine can run and a price.
AI agents write, test and review the code. A principal directs them and makes every judgment call.
A model from a different vendor tries to break the work. The checks run. The principal reads and signs.
You get the code, the evidence file and the decision log. Nothing depends on us staying.
This is what “AI-native” means here. Not a chatbot in the sidebar: a delivery system where every change is specified, written, attacked, checked and signed, and the record of all of it comes to you.
Exit checks a machine can run. Signed by you.
Implementer and test author work one ticket at a time, in a branch.
Architecture, security, accessibility, performance, types, contracts. One narrow brief each.
A model from a different vendor is told to refute the pull request’s claims.
Lint, types, tests, budgets, parity. Pass or fail in CI, not in a status report.
A person reads the diff, the checks and the log, and puts their name on it.
Every engagement is fixed price, quoted in writing after one call. Part is due at the start. The rest is invoiced when the checks pass.
Teams now produce more code than they can check. These figures come from a published study, not from us.
more bugs per pull request once teams leaned on AI coding tools.
Faros, AI Engineering Report 2026 (22,000 developers)as many production incidents for each merged pull request, in the same study.
Faros: incidents-to-PR ratio up 242.7%Things we shipped and can show. Client work is shown on request.
Product · iOS
A two-player strategy board game for iPhone, iPad and iPhone Duo. Rules engine, simulator, app, store pipeline, trailer and site, all shipped by one person directing agents.
Read the caseWebsite · this one
Every build measures page weight, JavaScript, links, headings, alt text and color contrast, and publishes the result. Fail a check, fail the build.
Open the build recordEvery engagement is led by an engineer who can take a product from design to production alone: interface, back end, infrastructure, analytics, release.
No juniors learning on your budget. No account managers between you and the person doing the work. The person who quotes the job is the person who signs the result.
Founder and principal
27 years building and running software, from interface design to cloud infrastructure. Directs the agents and answers for the result.
One call. Then a fixed quote in writing, with the checks that will close the work.