For companies doing $5M+ per year: we prove the system pays on your own numbers before you spend a penny building it.
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Five capabilities, each already carrying revenue inside a mid-sized company that answers for the number every month. None of them is a chatbot bolted onto a website.
Business consulting first: we find where AI actually earns its place in your operation, size what it is worth, and sequence the build against it.
The path from enquiry to booked revenue, engineered and instrumented - answered in seconds, qualified against your criteria, routed to the person who closes it.
Quotes priced off your own book, with the arithmetic kept away from the model - so it flags what it isn't sure of instead of inventing a number.
Built pipeline for companies where a single deal is worth six figures: the targeting, the sequences and the call layer, run as one system into your calendar.
The paperwork your team retypes between systems - read once, checked, and actioned automatically, with a person on anything the system flags.
Each one hands over a single thing and opens the next. The gate at stage two decides whether the build happens at all - scored on numbers agreed before it starts.
This is the part nobody else does. Most agencies sell you the build and find out afterwards.
We are not a lab. We operate the systems we build - carrying real revenue, inside businesses we answer to every month. That is why we can tell you which work AI pays for and which work it quietly makes worse.
Businesses putting AI to work inside what they already run.
One inspection report was costing the office two to three hours to price.
A Mr. Rooter plumbing franchise in Silicon Valley, where the owner sends job specs to the office as Spanish voice notes.
A voice note in Spanish becomes a priced quote, to the cent.
Twenty tools, one founder, and every process living in his head.
A blockchain ticketing platform in Galway growing entirely on warm introductions, with no outbound at all.
Getting the business out of the founder's head.
Nobody could say which spend was actually producing a customer.
A South African proprietary trading firm running paid social into a signup funnel, optimising against numbers it could not fully trust.
First we could measure it properly. Then we halved it.
The win here was measurement before creative. With tracking systemised across the whole funnel, the optimisation finally had something true to optimise against.
Full case study on requestEvery enquiry needed a conversation before it became a booking, and that conversation was eating his week.
A construction company in Kildare, growing on word of mouth and referral, with the owner personally answering every message that came in.
Monthly revenue more than doubled, and thirty hours a month back.
His job became checking the calendar, quoting and closing. The qualifying, the scheduling and the chasing happened without him.
Full case study on requestHe had the capacity to grow. Nobody could tell him which market to grow into.
A Perth carpentry company fitting doors and windows, choosing its next line on instinct like everyone else in the trade.
We found the demand first. Then we went and got it.
We built a system that reads every competitor advertising in his market, measures how contested each trade is, and weighs that against what a job in it is actually worth. Decking won on both counts. Then 73 enquiries were answered, qualified and booked without him coming off the tools, including the calls that landed mid-job.
Full case study on requestReads an inspection report or a Spanish voice note, prices every line off the client's own book, and flags what needs a human. Live and in use.
Answers, qualifies and books every enquiry within seconds of it arriving, day or night.
Targeting, sequences and the call layer run as one system, putting partnership conversations straight into a founder's calendar at a software company whose growth had been entirely warm introductions.
Conversion tracking rebuilt end to end across a proprietary trading firm's funnel, so every unit of spend could be traced to the customer it actually produced, and optimised against something true.
We map the operation, find where AI would actually pay, and put a number on it - what it is worth, what it costs to get there, and in what order.
We build the smallest working version and run it on your data. If it doesn't hit the numbers we agreed, you don't buy the build.
The production system, wired into the stack you already run, operated and measured against the baseline we set before a line of it was written.
Our systems run in construction, home services, financial services, solar and early-stage software. Every one of them sits inside a company doing $5M or more, where a wrong number costs a real job and there is no committee to absorb the mistake. That constraint set the standard we build to, and we have not relaxed it since.
What we are actually good at is keeping the model away from the arithmetic. It reads, retrieves and routes. Anything with a right answer gets computed. The reliability lives in the structure around the model, in the retrieval, the approval gates and the measurement, which is why our systems keep working when the models underneath them change.
We are not researchers and we are not a lab. We are operators who got tired of watching capable companies lose their week to work a machine should have done. That is the whole business: find that work, prove a machine can do it on your numbers, then build the thing. The first company we did it in was our own - Only Big Jobs still runs on this stack. We add people as the work demands it, and a founder stays on your account either way.
Finds the work worth automating, then builds the thing that does it.
A technical founder and AI systems architect, Yazan decides where AI creates durable value, sets the architecture, and stays personally accountable for what ships. He owns the build end to end - opportunity mapping, engineering, delivery, and the number it gets measured against.
Track record
Works out whether it is worth your money before anyone writes code.
An enterprise sales executive and commercial operator, Seanie connects what the technology can actually do to what a business will value, buy and adopt, a judgment built selling agentic AI into multi-site operators. He owns the market-facing system: positioning, qualification, negotiation, onboarding and the operating rhythm after the sale.
Track recordA model that performs in a demo can still be wrong on your actual jobs. Five things settle whether an implementation holds, and every one of them is decided before anyone picks a model.
Request the write-up →We test it on your records before you pay to build it. We agree a number up front - accuracy, cost per job, hours returned, whatever the business actually cares about - then build the smallest working version and measure it against that number on your own data. If it misses, you don't buy the build. Most firms let you find this out after the invoice.
With whichever question you cannot answer yet. If you know something is expensive but not what to fix first, start with Find. If you already know the bottleneck and want to see whether a machine can actually handle it on your records, skip to Prove. Nobody starts with the build, including us, because until a proof exists neither side can price it honestly.
Three ways, in order. We keep the model out of the arithmetic - pricing, eligibility and anything else with a right answer is computed deterministically, and the model only reads and routes. We engineer the refusal: the system is built to stop and flag when it is outside what it was proven on, rather than produce a confident guess. And autonomy is scoped per action - anything a customer or a payment sees goes past a person first. That boundary is written into the spec before the build, not discovered in production.
We work on the smallest slice that proves the point, we don't train third-party models on your records, and everything we build runs inside accounts and infrastructure you own. Where a proof needs live data we'll agree the scope and handling in writing before anything moves.
Usually yes - and where it can't, it doesn't need to. Some of what we build sits alongside your stack and hands your team the output rather than writing into it, which means nothing you depend on can break and nobody has to approve an integration to get value. Where a true integration is worth it, that's scoped in Build & Run.
A founder owns your engagement, start to finish. Yazan builds and Seanie runs the commercial side, and neither of those gets handed to someone else. As the work grows we bring in specialists behind them, but you will not be introduced to a founder and then passed to an account manager, because the person who agreed the number is the person answerable for it.
Our work lands hardest from roughly $5M in revenue upwards - big enough that a manual bottleneck is costing real money every month, and where you can still decide to change it without a steering committee. Below that the numbers rarely justify the build.
It tells you. The system flags what it isn't confident about instead of guessing, and anything customer-facing passes a person first. A tool that quietly invents an answer is worse than no tool, and it is the single fastest way to lose a team's trust in the whole programme.
Find is fixed scope. Prove is priced to the proof. Build & Run is priced to the outcome, because by then both sides know what the outcome is worth. Nobody signs a build cost before the proof - including us.
You keep everything: the system, the source, the data and the documentation to run it without us. Nothing is hosted on our account, and there is no key we hold that turns it off. We would rather earn the next month than trap you in it.
Half an hour with a founder, not a sales rep. You bring the part of the week that keeps going wrong, we tell you straight whether a machine fixes it, what it would cost, and what it would return. Nothing to prepare.
We go through where the hours and the margin actually go, and name the one or two places a machine pays for itself. Specific to your operation, not a slide about industry trends.
How we would build it, roughly what it costs, and the number we would hold ourselves to. If it is a fit, that becomes the Find engagement. If it is not, you still leave with the map.
We take on a deliberate number of clients so a founder stays on every one. If your problem is outside what we are good at, we will tell you on the call and point you somewhere better rather than sell you a project.
Plenty of these calls end with us saying the problem is not worth automating yet. That is a useful answer too, and it costs you nothing to get it.
A founder on the call. No deck, no gatekeeper, nothing to prepare.
Free · 30 minutes · we answer within a working day
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A founder owns your engagement from the first call to the number it is measured against. We grow the team behind that, never in front of it.
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