AI for real estate and commercial real estate firms
The deal knowledge stops livingin one broker's head.
AI for a real estate or CRE firm is worth building where your deal history already lives: leases, offering memoranda, rent rolls, past comps and client notes. A system indexed on that data answers questions like what did we agree to on the last three deals with this tenant, in seconds rather than in an afternoon of digging through a shared drive.
The highest-return builds we see are document search across the deal archive, first-draft offering memoranda and listing copy pulled from real property data, client and listing workflow automation, and research that summarizes a submarket from sources you already pay for.
This is the sector we know best. Ara Mamourian ran a Toronto real estate practice before Bottleneck Labs, and two of our own products, Closr and EchoMe, were built for it.
The conditions this work is for.
The deal archive is not searchable in any useful way
Every lease, LOI and OM from the last decade is technically saved. Finding the one clause you need means remembering which deal it was on, which means asking the one person who was there.
Offering memoranda and listings start from zero
The data already exists in your rent roll, your CRM and the last five OMs. Rebuilding the document by hand each time is copy-paste work being done by the person who should be talking to buyers.
Follow-up depends on who remembers
Leads, tours, expiring leases and check-in calls live in someone's head or in a spreadsheet only they maintain. Deals do not usually die loudly, they just go unanswered.
Junior brokers take years to become useful
Not because the market is hard, but because the firm's own knowledge, how you underwrite, what you concede, who to call, is undocumented. They learn it by watching, slowly.
The coaching team got their week back.
Real estate coaching platform. Toronto.
A Toronto real estate coaching platform, where the standards existed but lived so deep in three reviewers' heads that nobody had written them down.
Priced against your budget, not our hours.
- Flat rate, scoped in writing before we start. No hourly billing, no overrun charges, no mid-project upsell.
- Engagements run $12K to $100K or more. The number depends on what we are building, not on how long it takes us.
- Pricing is tied to a line item in your budget. If you spend a known amount on proposal writing or contract review each year, that number is the target. If the system does not move it, we failed.
- Six weeks from kickoff to a working system, with 50+ hours spent inside your firm before anyone writes code.
Scope and price are written down after the first two phases and reviewed by you. If we cannot agree on both, we do not start.
What people ask before they hire us.
What can AI actually do for a real estate firm today?
Reliably: search your own deal documents and answer with citations, draft first-pass offering memoranda and listing copy from real property data, summarize leases and rent rolls, keep follow-up moving, and pull together submarket research. Not reliably: predicting values, replacing underwriting judgment or negotiating. Buy the first list, ignore anyone selling the second.
Can it work with our CRM and the systems we already use?
Yes, and it should. We build on top of or beside what you run, whether that is a CRM, a document system or a shared drive, rather than asking the team to move into a new platform. Software that requires everyone to change tools is software that gets abandoned in month three.
Our data is messy and spread across ten years of folders. Is that a problem?
That is the normal starting point, and making it queryable is part of the work. We do not ask you to run a cleanup project first. What we do need is access and a couple of people who can tell us which folders are authoritative and which are graveyards.
Does this work for a small brokerage, not just a large firm?
Yes, and small teams often see it faster, because there is less process to unpick. A tighter scope, for example deal document search alone, lands at the low end of our range and is usually the right first build.
Do you understand commercial real estate specifically?
Yes. Ara ran a Toronto real estate practice before this firm, and we have built products for the industry rather than only consulted about it. You will not spend the first two meetings explaining what a cap rate or an estoppel is.
AI consulting, Toronto
The general engagement. Six weeks, flat rate, a working system rather than a roadmap.
AI for law firms
Contract review, precedent search and intake, built on your firm's own documents and standards.
AI for professional services
Engineering, accounting, agencies and consultancies. Proposals, compliance, reporting and internal knowledge.
Tell us where the work is stuck.
The first conversation is a diagnosis, not a pitch. If you do not need us, we will tell you that instead.
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