01
Agents that do the work
Not a chatbot bolted onto a FAQ. Software that reads your real data, decides, acts, and escalates to a human only when it should — with the escalation path designed, not left to chance.
AI systems for business
Agents. CRMs. Platforms. Built in weeks, not quarters.
Most of it is not a model problem. It is a plumbing problem.
The interesting part is rarely the AI. It is the spreadsheet nobody trusts, the form that loses half the leads, the reconciliation done by hand every month. We build the system underneath, then put intelligence where it actually changes the outcome.
What we build
Six kinds of thing — 01 to 06
01
Not a chatbot bolted onto a FAQ. Software that reads your real data, decides, acts, and escalates to a human only when it should — with the escalation path designed, not left to chance.
02
The Google Sheet that quietly runs your business, rebuilt as something that survives three people opening it at once — and that tells you what is unpaid without you reading every row.
03
Ticketing, memberships, bookings, checkout. Products with real users and real money moving through them, which is a different engineering problem from a demo.
04
Banking, messaging, payments, calendars, storefronts. Unglamorous, and usually the difference between a system people use and one they abandon.
05
Getting data out of places that do not offer an API, then making it answer a question you can act on — including the parts where the official tool caps what it will give you.
06
Reconciliation, reporting, follow-up, month-end. The tasks too small to hire for and too regular to keep doing by hand.
Why it is fast
One operator, a team of agents.
There is no account manager, no discovery phase billed by the week, and no handoff between the person who understood your problem and the people who build. Diagnostic, architecture and build come from the same place. That is the whole trick — and also the honest limit: this is built for problems a small, senior team can own end to end, not for staffing a department.
How an engagement runs
Step 1
We read what you actually have — the spreadsheet, the intake form, the DMs, the numbers — and report what is there, including the uncomfortable parts. One real example: a studio whose order book read €95k while only 40% had been collected, with a lead pipeline built and never used. That finding changed what was worth building first.
Step 2
A written plan: what replaces what, in what order, with the tradeoffs stated and the things we are deliberately not doing named. You can take this document to someone else. That is the point of it.
Step 3
Shipped in slices you can use, not one delivery at the end. Every slice is verified against real data before it counts as done.
Step 4
Yours to own, with the code and the reasoning documented — or we keep operating it. Both are fine; being vague about which is not.
What it is made of
Production systems, not experiments. The interesting part is the AI layer; everything holding it up is chosen to be unremarkable and well understood.
Frontier models via API, orchestrated as agents with tool access — plus the retrieval and evaluation scaffolding that keeps them honest.
Before you write
What this isn't.
If the need is five engineers for a year, this is the wrong shape.
Prototypes that impress a board and then rot are cheaper elsewhere.
Systems that replace a manual process need someone internal who wants it replaced.
The first conversation is about what is actually broken. If it is not a fit, you will be told in that call.
nadir@astra22.com