AI-generated code,held to production standards.
We are not LLM-powered. We are AI-native. Every part of how we deliver is built around how the model actually works.
- 01Specs in
- 02Context curated
- 03Verified, not vibed
- 04Architected by humans
- 05Hardened to ship
A signature stack, regulatory-grade, in a week.
A category-defining eSignature workflow rebuilt from scratch: qualified signatures, audit trails, the EU regulatory surface. Five days from kickoff to signed-document-in-hand, on Luxembourg's national trust infrastructure. Stripe billing and TPP eSeal landed three days after that. 15k lines of production code.
- 5 days
- Kickoff → first signed doc
- eIDAS QES
- Qualified e-signature
- 15k LOC
- Production code shipped
A custom operating system for one operator.
Bookings, intake forms, route planning, invoicing, recurring charges: a non-technical operator running his entire business on software built around his actual workflow, not a SaaS template. Pet-specific memory on every reservation. Post-stay follow-ups triggered by real events, not by human discipline.
- 1 operator
- Built around him, by name
- 7 modules
- Bookings, billing, comms...
- $0 / mo
- No SaaS bill
We run ITzWorking on software we wrote.
Project specs, context bundles, verification dashboards, time-of-flight metrics: the framework lives inside a tool we ship to ourselves first. Notion, Linear, Slack, HubSpot, Drive collapsed into one interface tailored to how we actually deliver.
- 10 → 1
- Tools collapsed
- Dogfood
- Used daily by the team
- Five pillars
- One dashboard each
Three kinds of agency.
Only one of them ships.
A team of humans.
Talented, expensive, slow. Quality scales with seniority. Output scales with headcount. The unit economics break the moment you need to move fast.
A team of humans plus a chat window.
Copy-paste workflow. The model is a tool, not a teammate. Speed-up is real but capped. The bottleneck is now the human typing into the box.
A delivery system the model is part of.
Specs, context, verification, architecture, hardening: every step designed around how the model actually performs. Senior humans make the bets the model can't.
Three ways we engage.
From idea to production.
Full products shipped end-to-end in weeks, not quarters. You bring the brief and the conviction; we run the spec sprint, ship the build, and hand you something live.
Embed the workflow.
We drop into your team, ship features alongside your engineers, and leave you with the practices, tools and templates to keep the leverage after we go.
Take over the mess.
A stalled or tangled codebase. We find the load-bearing failures, write the spec the previous team didn't, and ship what they couldn't.
Five pillars.
One bar.
The discipline of shipping AI-written code at production quality. Skip any one and the model writes you a demo, not a product.
Read the framework“The right question isn't can AI write this.
It's can the team around it ship it.”
Got something gnarly to ship?
Tight specs, hard deadline, real users. That's where this gets interesting.
or just