IT Analyst.
Granton AI
An attempt at figuring out what an LLM can do when you sit it down next to the books of a small business. Data flows, APIs and integrations between frontend, AI layer and the accounting core.
SHIPPEDBilly is an AI accounting assistant that takes an uploaded invoice and gets it into the accounting system without anyone doing it by hand. Simple thesis, complicated reality. We had four months to build the first usable version.
About the product
The problem Billy solves is boring but real. Accountants spend a big part of their day copying data from invoices and statements into systems. It is not hard work, but there is a lot of it and it can be automated, at least 60-70% of it.
Technically it was a combination of OCR for document recognition, an AI layer for data extraction and categorisation, accounting logic for correct bookkeeping entries and integration into existing accounting systems. Each of those layers had a different pace, a different owner and different problems.
What I actually did
I worked on several fronts at once. I designed the AI pipeline: how a scanned invoice runs through OCR, gets passed to an LLM, is checked against accounting rules and ends up as a record in the books. At the same time I was implementing accounting rules into the system, because an LLM on its own has no idea how Czech accounting legislation works.
On top of that I was responsible for the first version of the user UI, an overview of which documents are being processed, which are done and how they got recorded. And prompt optimisation for the AI layer was its own discipline. The difference between a badly and a well-written prompt was in practice the difference between 60% and 90% extraction accuracy.
What was different from banking
Results were visible within a sprint, not a quarter. Every week brought a new problem that needed a spec written and handed off to dev within that same week. No two-week release trains, no waiting for sign-off.
It was a different kind of pressure. Better in that you can see progress. Harder in that a mistake in the spec shows up immediately.
What I took from it
LLM is not magic, it is a component with edges. Small businesses have messier accounting than startups assume. And the most valuable analyst in an AI project is not the one who understands the models, but the one who can say where not to trust them blindly.
Billy now works at multiple accounting firms in Czechia. That is a result I am not embarrassed by.