Eagl

After Ghent in May, we’re bringing the AI Finance Hackathon to Amsterdam. 15 October, together with Payhawk. The format hasn’t changed: you bring one real problem from your finance operation and your own data. You build all day with AI experts at your table. At 5 PM, you demo whatever works, in front of everyone. RSVP closes on 24 September. 10 teams only. Link in the comments. Looking forward to seeing some of you there! 🇳🇱

A few weeks ago, we put finance professionals in a room with today's AI tools and watched them build. Some of it was prototype-grade, some was genuinely production-worthy. But that's not the point. Finance teams can build now, and I'd push anyone to just start! The ones who do will understand this shift faster than anyone waiting on the sidelines.

Second video in the series: Knowledge. Every finance team has rules about how their books should look. Most of them live in someone's head. "Knowledge" is where they go instead, and how our agents learn them from the way you work.

Pick your clothes up off the floor before you set the robot vacuum loose. Same goes for your finances. Fix the data before you automate it. Eagl product series - video N°3: Data Quality.

I've spent enough time around finance teams to know what financial controlling actually looks like. It's a lot of back and forth with colleagues just to get the data right. What if it was right from the start, not corrected at the very end of the close? We built Eagl's controlling agent to take that off their plate. It runs the review proactively and surfaces what needs attention. The controller decides what to do with it. Posted a short video today on how it works. Worth a watch if you close across more than one entity with high transaction volume.

A lot of you asked to see the real product, so here it is. Starting a short series on how Eagl actually works. First up: the Financial Controlling Agents. This one's a quick overview of what the agents do and how finance teams work alongside them day-to-day. Next in the series: Knowledge, Accruals, and the Data Quality agents that catch and correct misclassified bookings, such as the wrong cost center, cost unit, GL account or project. As any self-respecting vlogger would say: like and follow for the rest.