Private AI for real estate and property management
Ten years of minutes, contracts and special assessments filed in folders by year. Everything an owner asks is already written down. The problem is finding it before they hang up.
Managing buildings or a property portfolio is, above all, managing documents: minutes, leases, quotes, assessments, notices. Private AI indexes that history and answers in plain language, citing the specific minutes or contract. It runs on a server in the office, so owner and tenant data, arrears included, never leaves your network.
The real pains
The same ones in an agency and in a property management office.
"What was agreed about this at the meeting?"
The answer is in minutes from four years ago, scanned into a PDF with no way to search inside it.
The phone never stops with the same questions
Fees, assessments, deadlines, who pays for what. Repeat questions that eat the office's entire morning.
Hundreds of contracts, each with its own clauses
Rent reviews, deposits, renewals, maintenance obligations: checking any of it means opening contracts one by one.
Every building has its own history
The context lives with whoever manages it. If that person is away, the rest of the office works blind.
What AI does about those four
Searches inside the minutes, not the filename
"What was agreed about installing the lift?" returns the passage from the minutes, cited, including a 2019 scan, because indexing runs OCR over it.
Answers repeat questions on the spot
The office answers with the document in front of them instead of promising "I'll check and call you back".
Finds clauses without reading the whole contract
Ask about the rent review, the deposit or the term of a specific lease and it returns the passage, citing the contract.
Spreads the context of each building
Anyone on the team can get up to speed on a building's history in minutes, with sources cited.
Third-party data in every folder
A property management office holds personal data on hundreds of owners: fees, missed payments, incidents, correspondence. Agency work adds anti-money-laundering obligations, with client identification documents in the mix. None of that sits comfortably with "I'll paste it into a website to get a summary".
With private AI, the model and the search engine run on a server in the office. There is no outside provider to disclose and no transfer to assess: the documents stay where they already were.
What it does not do
It does not keep the buildings' accounts or replace your management software: it issues no invoices, raises no assessments and signs no certificates. It gives no legal opinion on property law: that is the manager's job. It cannot find what was never written: if something was agreed verbally, there is no document to retrieve it from. And it produces no exhaustive listings ("which leases expire this month"): it answers over the documents it retrieves for each question, not by sweeping the whole collection. And today it does not compartment by user: access is protected by a login, but whoever gets in can query everything that has been indexed. If each manager must see only their own buildings, the scope of what gets indexed has to be narrowed.
Before you ask
Can it find anything in scanned PDF minutes?
OCR is applied during indexing, so generally yes. The result depends on scan quality: a clean PDF reads well; a skewed, low-resolution one, or one covered in handwritten notes, may come out incomplete. This matters a lot in property management, where most of the history is scanned and currently unsearchable.
Why not just use a cloud tool?
Because you hold personal data on owners and tenants, arrears situations, contracts and amounts. Real estate agency work also falls under anti-money-laundering obligations, with client identification documents that are best kept inside your own perimeter.
Can it draft notices and minutes?
It can prepare drafts from your own templates and from previous minutes for that same building. Review and signature stay with the manager.
Let's start with the minutes archive
A 30-minute call to see which part of the archive is worth indexing first.