AI-ready data, implemented
Ask your data anything. Get the same answer every time.
We connect your platforms, model the data and build a semantic layer on top — so Claude, ChatGPT or your dashboards all read from one definition of the truth.
The problem
AI on raw sources guesses. Ask twice, get two numbers.
“Revenue” lives in five tables and three tools. An assistant querying them directly has to guess which one you mean — and it guesses differently every time.
The fix isn’t a better prompt. It’s one shared definition underneath every tool you use.
How it works
From scattered sources to trusted answers.
- 01 → ingest
Sources
Every tool you use, synced into one warehouse.
hubspot · stripexero · quickbookspostgres+ 350 connectors - 02 → model
Models
Raw tables cleaned and tested into customers, invoices and accounts.
stg_ → int_ → martscustomersinvoicestested, documented - 03 → define
Semantic layer
Each metric defined once — the reason answers stay consistent.
revenuechurn_rateactive_usersone definition each - 04 → ask
AI & BI
Claude, ChatGPT or dashboards, all reading the same definitions.
claude (mcp)chatgptdashboardsyour own agents
Your data, your infrastructure
Run it on your servers. Not someone else’s.
Every layer is built on open-source tools, so the whole stack can live on infrastructure you control — no vendor holding your data, no per-seat platform bills.