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An AI-operated software factory

Software built and kept working, with the evidence for every change.

We deliver software the way a factory does — planned, built, checked and reported, owned by a small accountable team — and we look after the infrastructure it runs on. Two different jobs for two different readers, so pick the one that is yours.

What NamiQ is

We build and maintain your software, and hand you the evidence for every change.

A delivery service, not a tool

Nothing to install and no product to learn. We take a defined scope — a new build, a change to a system you already run, or one nobody understands any more — and deliver it.

Run by AI, owned by people

Twelve specialist AI roles do the work. A small team owns the outcome: a Technical Architect for what ships, and a Forward Deployed Engineer who works with you and watches your running product. Six checks decide what reaches your product, so speed does not come out of quality.

You keep everything, including the reasoning

Working software, every check that ran, the decisions and why they were made, and the board we ran it on. An ordinary repository, no proprietary runtime.
Four situations we take on

If one of these is yours, we have done it before.

You have something to build

The scope is clear and delivery capacity is the constraint.

You inherited something nobody understands

The author has gone, the documentation describes a version that no longer exists, and every change carries a risk nobody can size.

Nobody owns your infrastructure

No IT staff, a server someone else set up, and backups nobody has restored. Reviewed, set up once, then watched.

An AI tool built it, and now it breaks

You got to a working first version fast. Now each fix breaks something else, and nobody can say why.

This is not our claim. It is what 2026 published.

1.7×

more issues in pull requests containing AI-assisted code than in human-written code

arXiv preprint 2603.28592, March 2026
30–41%

rise in technical debt within six months of widespread AI tool adoption

Innovative Group, 2026
45%

of AI-generated code introduced a security vulnerability

Innovative Group, 2026
4×

maintenance costs by year two, for teams that do not actively manage the debt

Innovative Group, 2026

Published figures, directional rather than audited. Three of the four come from the same 2026 analysis; the first is a separate preprint. Every one is linked so you can weigh it yourself.

If you are building software · why it is hard to do well

Two ceilings nobody warns you about.

We wrote these in July 2026, from running a factory past 165,000 lines. The measurements since have pointed the same way.

Your reviewer is your throughput

AI can 10× how much code gets written. It cannot 10× how much a human can safely review. If every line still passes through one person's eyes, that person's reading speed is your real ceiling — no matter how fast the model is.

It degrades exactly where you are going

AI is excellent on day one: a blank file and an afternoon get you a week of senior work. Real products cross into six figures of lines, where parts depend on each other in ways nobody wrote down. The model does not get worse — the blast radius of confidently wrong grows with the codebase, and nothing tells you unless something is built to.
Generation capacity rises steeply while review capacity stays flat; the widening gap becomes unreviewed code.AI tooling adoptedmonths latervolumecode generatedcode safely reviewedthe gap shipsanyway
The gap is not idle work. It is code that ships having been read less carefully than the code before it. We think that is what the 1.7× figure is measuring, though the study reports the association and not the cause.
Why the usual answers fail

More generation makes it worse, not better.

Buy more AI tooling

It raises output and hands the review bottleneck straight back to the same people. The constraint was never how fast code gets written.

Hire senior engineers

Correct — and it costs you a quarter you do not have. The market for them is the reason you reached for AI in the first place.

Bring in an agency

A retainer team, and usually AI bolted onto the same process rather than changing it. Ask any supplier — us included — to show you the evidence for the last thing they shipped. Where each option wins →
What we do instead

Gates decide what ships. Not prompts, and not optimism.

Agents do the work; 6 checks decide whether it reaches your product. The last one asks whether a real person can finish the job on the running system — the failure tests cannot catch.

01

Understand

We read your problem — or your existing system — into a map of what it is, what depends on what, and where the gaps are. You get a plan you can challenge before anything is built.

02

Build

The work is split into small, tracked items with agreed acceptance criteria and assigned to the right specialist. Every change is isolated and reviewed.

03

Prove

Six checks run before anything reaches your product — ending with whether a real person can finish the job on the running system.

04

Report

Progress, quality and cost are recorded as the work happens. One live board, always current. Blocked work shows the day it blocks.

What that produced

Our own product, not a pilot — and not a client engagement.

98%
Work completed
1,355 of 1,381 items closed
182/184
Features delivered
99% of the planned programme
2.64
Defects per 1,000 lines
every line shipped — within the 1–5 published for a disciplined team
17 days
To reach this scope
against a 9–18 month norm — see /results for the caveat

These come from the live cybersecurity platform we build on this factory — 165,000 lines. That is the friendliest case there is: we set the scope and we chose the stack. Position as at 2 August 2026, recomputed from the work record rather than typed into a slide. The same board reports that 43% of work was later redone and that queueing time rose as parallelism increased. All of it, including those →

Insights

What we have learned running it.

All insights →

Start with one piece of work.

Give us one piece of work and read access to one system. We map it and come back with what we found, what we would build and what it would take — before any commitment. About a week from read access to write-up. One service or repository up to roughly 50,000 lines; a larger estate we scope first rather than promise a week for.

Talk to us