Not a project that ends. I read where the work stalls, build the fix, ship it into production, then run and improve it month after month. AI where it pays off, and not where it doesn't.
Your best people lose hours every week to work a system should handle — hunting for the right photo or measurement, re-keying the same order, answering the same question for the tenth time, chasing a status nobody updated.
None of it lands on an invoice, so nobody counts it. But it's real money and your team's time. I build systems that take that work off them — every product spec in one place instead of five people's heads; a configurator your customers drive themselves instead of interrupting your staff. Then I run it, so the time stays saved and the busywork doesn't creep back.
See it in your own numbers. Pick one job your team still does by hand every week:
Fully-loaded = salary plus employer taxes, tools, and overhead — what an hour of that person really costs you, not their gross pay.
Illustrative only, from the figures you enter — 46 working weeks a year, a hire loaded at 1.35× to reflect employer taxes and overhead. Not a quote and not specific to any real dataset. Your real system cost depends on the work; I'll give you the honest number for your case, free. AI where it pays off — and where it doesn't, I'll tell you.
I read the operation first, find where the work actually gets stuck, then make the right next step easier than ignoring it. The software is the lever, not the deliverable.
Process-mine the live system to see exactly where work stalls, with numbers, not opinions.
Rebuild the flow and automate the mechanical parts. Surface the one next action instead of a backlog.
Built into your production system, not a slide deck or a parallel tool nobody opens.
The new way holds because it is easier than the old. Then I keep it running and improving, month after month.
Real systems, shipped into production and still running. Each one built so the work it does can be measured and audited. Read the full cases →
I spent my first career in finance and credit risk, running decision systems for a multinational group: scoring, approval chains, controls, across a portfolio near €500M and five continents.
That taught me to read a business process, where decisions happen, where friction hides, and what actually matters versus what just looks busy. Now I build the software myself.
I read the process first. Then I write the code.
Map your operation on a single page, send it over, and I will give you my read for free. If it is worth going further, the path is short.
Fill in the one-page operation map, send it, and get my honest read on where AI pays off for you, and where it doesn't.
I build the fix into your production, own the outcome, and stay on the hook until the operation actually moves.
I keep it running and improving, month after month. Scaled to the size and criticality of your systems.
A few lines on where the work gets stuck. I read every one myself and reply personally, usually within a day.