Why we built Gatlas

Every portfolio company keeps its books somewhere, sells somewhere else, and measures itself in a third place. Across a group of companies, the picture a CFO actually needs is spread over every one of those tools, and none of them talk to each other.

The usual answer is a quarter-end scramble: export spreadsheets from each system, paste them into a master file, reconcile by hand, and hope nobody changed a chart of accounts. It works, barely, and it tells you what happened two weeks ago.

We wanted data you can ask: at any moment, in plain language, with an answer you can trust.

Three things had to be true

Operators connect their own tools. A holding company shouldn't be collecting and storing the passwords of every company it owns. So onboarding is a setup link: the operator connects their Shopify, their accounting system and their CRM themselves, and Gatlas uses each stored credential only to reach the tool it came from.

Every company stays its own company. Each company's tools sync into a space of its own in Gatlas, on their own schedule and with their own freshness. Group figures are built on top, on purpose.

The data is documented. Raw tables are tribal knowledge. We turn them into datasets with names, definitions and the context a good analyst would give you, so the same trusted numbers serve a person and a machine.

The part we didn't expect

When we put documented datasets in front of a portfolio, an agent turned out to be their most valuable reader.

Give an LLM raw tables and it guesses. Give it governed datasets with context, scoped to exactly what it may see, and it cites its sources. That's the Gatlas we ended up building: a layer your whole portfolio, and its agents, can trust.

This is an early draft of our story. We'll keep writing as we learn.