Client work · B2B
Containeer
AI intelligence for commercial real estate and private credit: ownership, debt and distress, decoded.
- Businesses · B2B
- Data engineering
- In production
The challenge
Ownership, debt and transaction information exists, but it is spread across public documents in very different formats. Tracking a maturity or a troubled asset meant searching and cross-checking by hand.
What we built
- 01Processes that bring in and validate the data on a schedule, with no manual steps.
- 02A data model that joins ownership, debt and transactions by asset, ready to be queried again and again.
- 03The foundation behind the product's map, search, asset pages and AI assistant.
How it fits together
Data comes in raw, gets ordered in layers and comes out ready for whoever decides.
From data to people
✓ All running- Public recordsSources
Documents and records on US property ownership, debt and transactions.
- Scheduled ingestionIngestion
They are downloaded, read and validated before they come in.
- Layered modelData model
From raw to ready: ownership, debt and transactions joined by asset.
- AI assistantAI
Answers questions about assets, debt and transactions, grounded in the same data model.
- ProductProduct
Map, search, asset pages and the assistant, on the same tables.
- Credit teamsPeople
They track maturities, deterioration and deals, and decide.
High-level architecture. We don't name the client's technologies.
How it runs today
It is published and used by people tracking maturities, deterioration and deals. The client's team operates it.
Who does what
- The client
- Decides which assets and signals matter, and runs the platform day to day.
- Brickeer
- Designs and builds the data path, and leaves it documented so the team can run it.
Got something similar on your plate?
Tell us what you want to achieve. If it fits what we do, we will tell you how we would build it with you.