The governed AI software factory
One firm that ships like a team
The volume on our Work page comes from a specific way of working, not from long hours. AI agents do the building. What makes their output dependable is the discipline wrapped around them: the same specs, contracts, reviews and audits a strong engineering team would run, except here they are written down as software and enforced automatically, on every project, every time.
The team is AI. The discipline is installed as software. A person merges every line.
Six disciplines, installed as software
These are not aspirations. Each one exists as a policy, a check, or a tool that runs inside every project repository.
Truth before code
Every engagement starts as a knowledge bundle in which every load-bearing claim is logged to its source. Nothing gets built on an unverified assumption, and when a source is missing we say so instead of papering over it.
Contracts before implementation
Interfaces, data shapes and document sets are ratified before code exists, and each document carries a machine-readable status. Agents build from versioned, approved truth, never from the drift of a chat conversation.
Independent review, clean context
Every substantive change is reviewed by an agent session that never saw the implementation conversation, so the reviewer cannot inherit the blind spots of the builder. Measured across the portfolio, first-round reviews have found real issues with zero false positives.
Oversight installed as software
A governance baseline (policies, checks, scheduled audits, exceptions that expire) is installed into every delivery repository, then audited on a schedule. One rule never bends: a human merges every change.
Multi-vendor agent teams
Agents from different AI vendors work the same codebase under lock protocols and file-ownership rules, and verify what the others produce. No single model is trusted as its own referee.
Expertise that compounds
Finished expertise is packaged as installable skills, so the second engagement of a kind starts from a tested capability rather than from scratch. This is why fixed scope keeps getting faster.
What the factory produces
A few anchors, each measured from its own project record:
8 days
from first line to production for the sales pipeline app that retired our own 15-year spreadsheet practice
days
to build and publish the National Stop Register of Bulgaria: 13,974 stops consolidated in EU standard formats
~6 weeks
to build a bilingual handwriting AI tutor, now in daily use, that marks up photographed handwritten work like a teacher
4 days
to build the LEED / EDGE / BREEAM knowledge bases, with every claim cited to a primary source
The same factory has shipped a public macOS product (Stampit Smart Card Suite for macOS) and serves legalize-bg, a queryable knowledge base of roughly 3,600 Bulgarian legislative acts, live to AI tools.
The engagement rhythm
No twelve-month program, no discovery phase that outlives its welcome. Three steps, and you can stop after any of them.
30 minutes
A free call
You describe how work gets done today. We name the first tool worth building, what it would replace, and how long it would take.
~2 weeks
First Tool Sprint
A fixed-scope sprint that ends with one working tool on your real data, in production use. Your people build alongside us, so the learning lands where it belongs.
Monthly
Tool cycles
Optional cycles, roughly monthly, each shipping the next tool. Continue while each cycle earns its keep, stop any time.
Whenever you stop, your team keeps everything: the tools, the code, the data, and the documentation to run them without us.
Source-grounded output, EU data residency
Two questions come up in every first call. Both have engineering answers, not reassurances.
"AI makes things up."
Unsupervised, it does. In this factory every claim in every deliverable traces to a logged source, and the independent review checks the trace before anything ships. Method, not magic.
"Where does our data live?"
In the EU, under EU jurisdiction, fixed at the moment the infrastructure is created. Your data is not used to train models.
How an engagement starts
It starts with the 30-minute call. If the fit is real, we scope the First Tool Sprint in writing: what the tool does, what data it runs on, what "done" means. About two weeks later the tool is in production use and the next target is usually obvious. Pricing is fixed per sprint and per cycle, quoted before work begins.
One side note the factory explains: websites, documents and presentations fall out of it as side artifacts of the work. The end goal is always a working tool.