7,000 files.
Every field.
Before we wrote a line of code.
Compliance knowledge is earned — inside real brokerages, on real files, with real consequences. Before we automated anything, we did the verification manually.
Why generic AI fails here
Reading a document and verifying one are different things.
Verification requires knowing what the text is supposed to say — which fields are mandatory, which clauses require admin attention versus ones that can auto-pass, what an OACIQ inspector expects to see.
That knowledge doesn’t exist in a language model. It exists in the regulation, in enforcement history, and in 7,000 transaction files we read before we built anything.
The work
We read everything before we built anything.
We came from inside the industry
Our team worked in the Quebec real estate industry as brokers and compliance administrators. The 7,000 files aren't a dataset we assembled — they're the transactions we personally processed, flagged, and reviewed.
Map every required field
For each mandatory form, we identified every required field, its clause reference, its validation rules, and what a missing or incorrect entry means under OACIQ regulations.
Document every edge case
Conflicting addresses, seller(s) represented by 3rd parties, successions, partial GST exemptions — every real-world deviation was catalogued and built into the verification logic.
What we found
What 7,000 files taught us.
The result
The knowledge is in the product.
Every rule inside OpsFlows came from a real file.
When OpsFlows flags an issue, it cites a clause. When it passes a document, it has checked every required field against the same rules we derived from 7,000 real transactions. That’s not an approximation — it’s the standard. Read more about document verification here
See it on your files.
No commitment. Your data stays in Canada.