Most real estate platforms are built on MLS feeds that fragment history, lag transactions, and lose the property thread across relists. BrightCat is the dataset underneath — continuous, structured, and lifecycle-complete.
If your product depends on property data, this is the foundation layer.
If you're building a proptech product, a valuation model, an investment platform, or a brokerage tool — you're probably working with MLS data. And you've probably noticed the problems.
A property that lists, expires, and relists gets a new MLS number each time. Your system sees three different properties. It's one. The price history, the days on market, the listing trajectory — all fragmented across disconnected records.
Transaction prices appear weeks after closing. By then, your valuation model is already stale. Worse, the sold record often references a different MLS number than the listing — breaking the link between what was asked and what was paid.
Most feeds show you what's active today — a snapshot. What listed last month and was pulled? What relisted at a lower price? What sold privately without appearing in the listing feed? If you can't track a property through its full lifecycle, you're working with fragments.
Canada has dozens of regional MLS boards, each with its own data format, API, access agreement, and update schedule. Building national coverage means negotiating with each one individually — or paying an aggregator who normalises away the detail you actually need.
One dataset. National coverage. Weekly updates. Every property tracked with a persistent identifier across its full lifecycle. Delivered directly into your Snowflake account or accessible by AI agents via MCP.
Every province. Every listing status. Every price change. 118 columns per property. Updated weekly since 2014.
Confirmed prices matched to listing history. 194K repeat-sale pairs for property-level appreciation. Not estimates — outcomes.
Sale and lease activity tracked together. 10,093 dual-listed properties. Transaction outcome inference in real time.
Built from confirmed transaction pairs. Property-level appreciation, not smoothed regional averages.
Observed listing signals, not modelled predictions. Identify households entering a move cycle the week the property lists.
No file transfers. No ETL. Query live data inside your Snowflake account. Connect AI agents via MCP. Build on it, not around it.
Connect Claude, GPT, or any MCP-compatible agent directly to live Canadian property data. No preprocessing. No transformation layer. Your model queries the actual market.
See how it works →Request sample data. See the depth for yourself.