I built my first chatbot at 16. Aiqentra is what came next.
I'm Stephen. I'm 18, based in England, and I've been building automation systems since I was 16. My first was a chatbot for funeral homes — not the obvious starting point, but it taught me how to take a messy, human-heavy process and make it run without constant manual input. That's still what I do.
Before this I worked as a logistics coordinator processing 100–300 aircraft engine part receipts a week — raising sales orders, handling shipping, tracking parts across systems that didn't talk to each other. I know what it looks like when manual work is the only thing holding an operation together. I also know exactly where automation breaks it open.
Aiqentra is that pattern applied to US fintech — accounting firms, customs brokerages, fractional CFO shops, compliance teams. One workflow at a time, scoped tight, built around how the team actually files, reconciles, or clears. Scalpel, not chainsaw.
Built across more than one field.
The pattern I keep seeing across every discipline — logistics, code, everything in between — is the same: systems beat willpower. The ones that win aren't the most talented. They're the ones who built a process and stuck to it.
Most AI vendors sell chainsaws. We build scalpels.
Most AI tooling does everything, badly — wide, generic, template-shaped. For a 10-person accounting firm or a customs brokerage under deadline, that's the opposite of useful. It adds surface area to regulated work that already has too much.
We go the other way. One workflow, specifically scoped, built around how your team actually files month-end, reconciles accounts, classifies entries, or runs compliance review. Audit trail, validation layer, and human review are baked in from the first line of code — because in regulated fintech there is no other way to ship.
The methodology is the same scalpel approach proven inside regulated audit and Fortune 500 environments, translated down to the scale, budget, and pace of a small US fintech team. Once your team has the groundwork, we free their time for the work that actually needed them in the first place: judgement calls, edge cases, client conversations.
I don't take on work I can't make pay for itself.
Every system I build has a measurable output — hours saved, follow-ups sent, data processed. If I can't show you the number before we start, I don't pitch it. The bigger question is what your team starts doing with the hours you get back.