Fast because it is code. Precise because it is checked.
Every number here is measured from our own work or taken from a published source, and each one says which.
MEASURED
1,451 jobs in 24 hours
finished by the machine, 1.1% failed or stuck
About 196 worker-hours of finished jobs in a day, a median job taking 3 minutes.
Where this number comes from
Method: counted from the log our own system writes every time it finishes a job, over the last 24 hours. Before you ask: this is our own build machine working on our own code. It touches nothing of yours, it replaces nobody, and your IT provider stays in charge of anything we build for you.
One finished job every six minutes sounds quick. But is quick any use if it is wrong?
RECORDED
3.5 hours
from idea to a working, tested app with 56 commits (7 October 2026)
MODELLED
60.6
engineer-years of output since 4 May 2026, priced by the COCOMO model on 230,797 measured lines (9 October 2026), not a timesheet
MEASURED
6,314
test cases in the main engine, run before any change is accepted
RECORDED
2
layout bugs caught by the phone check before release, after the tests had passed
Why it is fast
Think of a kitchen where the prep is already chopped. Nobody works faster, there is just less to do at the last minute.
Most of what we build is plain code, and code runs in seconds, every time, at any hour. The AI handles only the judgement work, and many builds run side by side instead of one after another. That morning's app was 14 features built in parallel and merged into one product, about one commit every three minutes. See the case study.
How that compares
| Job | Typical published timeline | Our measured speed |
|---|---|---|
| Software change, idea to live | Elite teams: under a day. Low performers: 1 to 6 months (DORA 2024) | A full app built and tested in 3.5 hours |
| Penetration test report | 3 to 10 days of testing, plus 2 to 3 days to write the report | Public-record security checks run in seconds, reported in plain English |
Sources: DORA 2024 performance clusters (Octopus), CyberForte, penetration testing. A public-record check is not a full penetration test; the row compares turnaround, not scope.
Why it is precise
- Tests before claims. The main engine carries 6,314 test cases and the control layer 188 safety suites, and a change is accepted only when every one passes.
- Checked the way you will use it. Apps are tapped through on real phone screen sizes before release. In the trading-app build, that check caught two layout bugs the tests had missed.
- Every figure sourced or labelled. In our reports a number is either measured, taken from a named public source, or clearly marked as a model you can re-run. See the methodology.
- A person approves what matters. Anything that sends, spends or commits waits for a one-tap approval, and every change is backed up before it goes live.
Every angle of value, in one place
Want to see how fast this could move for your business? Would it be okay if we looked at one of your slow jobs on a free consultation? A no is welcome.