Fina: When Finance Expertise Meets AI Engineering

Every finance team has the same month-end story. The data is scattered across systems, the consolidation is done by hand, the journals take days to prepare, and somewhere in the middle of it all, a skilled (sweating) accountant is doing work that feels more like data entry than actual accounting.

We're AI educators and practitioners, and we don't just teach, we practice what we know by building ourselves. Finance turned out to be one of the clearest cases of where AI can act as an extra pair of hands: not replacing people, but taking the repetitive, stressful work off their plates so they can focus on what actually requires human judgment. One of our team members, who had 15 years of finance experience before joining us, initiated and led the project. This is her account of how it came together and what she learned.
‍

Why existing tools weren't the answer

ERP systems exist, accounting software exists, and some of it even has AI features built in. But all of these tools are designed to work for any company, which means they're never quite right for yours. There's an analogy that keeps coming up: a university accounting degree gives you a solid foundation, but when you join a real company, you still need months to learn the internal processes, the specific tools, the unwritten rules — the tacit knowledge that makes you actually useful. ERP systems are the degree. What we built is closer to a trained employee who already knows your company, configured around your workflows, not the other way around.
‍

How we built it

The core team was three people: our finance lead, a full-stack developer, and a CEO as an advisor. The MVP took about 2 months. One decision made everything faster: we skipped the web interface entirely. The AI accountant works via email: you send a document, it processes it, you get structured output back. That mirrors how finance teams already work with outsourced accountants, so there's no learning curve and no time lost on UI that wouldn't validate anything.

The architecture is custom-built for each client — a combination of deterministic agents that guarantee accuracy on calculations and structured data, and LLM-based agents that handle flexibility: unstructured documents, new formats, edge cases. AI can hallucinate, and in finance that's not acceptable, so the design is deliberate about where you let the model be creative and where you don't. Getting that balance right is one of the core things we at Hyperskill teach as well, because it comes up in almost every serious AI product.
‍

What changed for the teams using it

One client was manually consolidating financial data from multiple systems: a process that took 2-4 days every reporting cycle. With the AI agent, the same report is ready in about an hour, with minor manual review. Another client was spending a full working week preparing journals from bank statements and sales data. That same task now takes a few hours. These are the workflows that finance teams repeat every single month, done by people who are too experienced and too valuable to be spending their time this way.
‍

What it taught us about building AI solutions in general

Accuracy matters more than creativity. Finance has no tolerance for ambiguity: there's one correct answer, and the system either produces it or it doesn't. One mistake is enough to lose trust, and once trust is gone, the tool gets abandoned. So the real challenge is designing architectures that are reliable where reliability is non-negotiable, and flexible where flexibility is needed. That balance is exactly what we teach, and exactly what we had to apply here.

Building is also no longer the hard part. A functional iteration takes less than a week once the problem is well-defined. What's hard is understanding the user deeply enough to define the problem correctly, and that's where most of the time should go. That insight shapes how we structure our learning as well: more time on thinking before touching the code.
‍

If you want to build something like this

Start with customers, not with product development. Talk to potential users, run interviews, understand their workflows, identify real pain points. We validated demand through professional communities, finance meetups, LinkedIn posts, and direct outreach, which gave us real evidence.

And if you want to learn how to build AI systems that work in production — things that handle real data, edge cases and stakes — that's what our AI Engineering Bootcamp is for. We built this the same way we teach: by doing, by getting it wrong and by figuring out what holds up.

‍