.png)
Research report
AI Does Not Remove the Bottleneck
Find your real workflow constraints before buying AI tools.
The riskiest build is the one that impresses investors.
MVPs scoped hard and shipped to real users, on foundations that hold if the idea works.
Where we help
The work we are asked to do in startups.
01
MVP scoping
Deciding what not to build, which is most of the value we add early.
02
Build to first users
A working product in front of people who can say no.
03
Technical diligence support
The architecture and security questions an investor's engineer will ask.
04
Fractional senior engineering
Seniority before you can hire it permanently.
05
Scaling after traction
The rebuild you avoid by making three decisions properly at the start.
The numbers behind our startups work
The riskiest thing an early team can build is the version that impresses investors. It is bigger than the evidence requires, it takes a quarter longer, and it answers a question nobody in the market asked. We would rather build the smallest thing that proves someone will pay, and leave the architecture that scales for the point where scale exists.
14 wks
how fast we get to market
25+
our track record
600+
products shipped
15
years of first versions
Selected work
Engagements in startups, described rather than named.
A creator-tools startupValidating the product before funding the buildA rapid concept workshop that checked every platform assumption against the real API docs — and corrected several of them.10 daysfrom first workshop to a fundable, build-ready packageRead the case
A Los Angeles private car serviceTurning every call into a booking.Phone and web bookings answered by AI, written straight into the dispatch system, then rebuilt as a product other operators can buy.24/7booking cover on phone and chat, with nobody on the lineRead the case
A US healthcare practiceMaking surgery clearance easier to track.Referrals, pre-authorisations and clearances tracked in one place, with a rules engine that decides when a patient is ready.4 wksfrom kickoff to a tool the practice runs on dailyRead the caseQuestions we get asked
What should our MVP include?
The smallest thing that tests the assumption you are least sure about. Most first versions are built to impress rather than to learn, which takes a quarter longer and answers a question nobody was asking. We push hard on cutting scope, and that conversation is where the value is.
How fast can you get us to users?
14 wks to something real users are paying for, on a scope agreed in discovery. Faster is possible with less; we will tell you what is being traded away rather than quietly leaving it out.
Will this hold up if it works?
The parts that would be expensive to change will. Core data structures, tenancy and authentication get proper attention because retrofitting them is painful; everything else stays deliberately simple until the product earns the complexity. Pretending an MVP needs enterprise architecture is as wasteful as pretending it needs none.
Can you support technical due diligence?
Yes, and it is easier when the work was built to be looked at. Investors ask about architecture, security, dependencies, key-person risk and whether the codebase matches the story. We prepare for that during the build, which is far cheaper than assembling it under a deadline.
What happens when we hire our own team?
That is the intended outcome and we build for it. Code in your repositories, infrastructure in your accounts, documentation written for someone who was not there. We often stay alongside a first hire for a period, then step back as the internal team takes ownership.
We have no technical co-founder. Is that a problem?
It is workable, and it changes what you need from us. Someone has to hold the technical decisions on your side, which is a role we can fill during the build and hand over later. What does not work is nobody owning them, because then every trade-off defaults to whatever we prefer.
.png)
.png)
