Proof
What a senior lead and AI actually ship.
Three products in production and 28 production AI capabilities across them. What each one does, what it took, and the evidence underneath: scope, AI features and working days counted directly from each git history, never estimated. Some are named, others withheld until their owner is ready.
PropTech · Client engagement
A market-intelligence platform for letting agents
Letting agents had no way to see their own market. The information existed, scattered across thousands of public sources, and assembling it by hand was never worth anyone's time. This platform collects it continuously, reconciles it into a single dataset, grades it with AI, and turns it into something an agent can act on the same morning.
Delivered end to end in one funded stage: a public site, a multi-source data engine, and an AI proof of concept.
8 production AI capabilities, delivered in 68 working days by a senior lead directing AI.
Counted from git
65,395 lines of production code
9,532 lines of tests
19 application pages
828 commits
68 working days
Trunk commit cadence: median 13 minutes between commits, with deploy-on-merge CI. For scale, a traditional team is estimated at £241k-£270k for the counted scope.
Two more in production
Different domains, same delivery model, measured the same way.
Cyber security · SaaS
CyberSee
Getting certified has meant hiring a consultancy and losing most of a year to it. CyberSee runs that path in software instead: posture assessment, the evidence a certifier asks for, and guidance on what to fix next, kept current rather than assembled in a panic the week before an audit.
Live. Sign up free and look around it yourself. cybersee.ai
10 production AI capabilities, delivered in 82 working days by a senior lead directing AI.
Counted from git
53 pages
205 endpoints
107,257 production LOC
11,820 test LOC
10 integrations
For scale, a traditional team is estimated at £509k-£566k for the counted scope.
Legal AI · Platform
An AI legal-workflow platform
Legal work is expensive because it is slow, and slow because somebody has to read everything. This platform breaks a matter into the issues that will decide it, finds the law governing each one, and shows its working, so a lawyer can check it rather than take it on trust.
Pre-beta, access by invitation.
10 production AI capabilities, delivered in 33 working days by a senior lead directing AI.
Counted from git
17 pages
51 endpoints
23,531 production LOC
8 integrations
For scale, a traditional team is estimated at £247k-£275k for the counted scope.
How this is possible
A senior lead directing AI, with specialists pulled in where scope needs them, produces what used to take a coordinated department.
- AI writes the code, tests and documentation. The lead directs and reviews, applying 20+ years of engineering judgment to every decision.
- Specialists join for the work that needs them, design, security, data, under the same single accountability.
- Builds start on infrastructure patterns already proven in production, so work moves on day one.
- Fixed price against a signed done-test, with the delivery risk on us.
How we measure
The scale figures (code, tests, pages, AI capabilities, commits, cadence, working days) are counted directly from each project's git history and infrastructure by our measure tool. Nothing is estimated.
The traditional-team benchmark prices the counted scope component by component against published market rates, each row sourced and graded for confidence. Set against our fixed price for the same scope, that is typically 4-6 times. For ventures we believe in, we can share in the upside instead of charging full price.
It excludes recruitment, management overhead and the 52% of projects the industry reports as challenged or failed, all of which a traditional team pays.
Working days are git-counted; days spent across multiple projects are counted in full against each, so correcting the overlap only widens the gap.
AI API spend, third-party vendor spend, and documentation effort are tracked separately and are not included in these figures.
Want these numbers for your own codebase?
The Codebase X-Ray runs this tool over your code and reports what it would cost a traditional team to build, how it is maintained, and where it is exposed. Free, and yours whatever you do next.
Want this level of evidence on your own system?
Start with a fixed-price assessment, or tell us what you're building and we'll come back with a scope and a fixed price against a signed done-test.