The verdict in three sentences
Occupiers, landlords and investors preparing a property decision in Toronto increasingly ask AI assistants like ChatGPT before they contact an advisory, and the answer names a handful of firms, rarely more than three or four. A firm makes that list when its public information is consistent, factual and echoed by third parties: clear expertise pages, dated market research, a named team, and transactions mentioned only when they are public. The work is measured with a dated record of the answers to the same questions, and no serious provider can promise that an AI assistant will recommend you.
What do buyers actually ask AI assistants in Toronto?
The shape of the question has changed. A head of corporate real estate no longer types 'office broker Toronto': they describe the situation, the footprint and the timeline, then ask for names. These are realistic questions a buyer might put to an assistant in 2026.
| Who is asking | Question put to the AI assistant | What the answer needs to find about you |
|---|---|---|
| Real estate director at a tech company | 'Which firm can represent a tenant for 20,000 sq ft of office space in the Financial District or King West?' | Tenant representation page, submarkets covered, named team |
| CFO of an industrial group | 'Who advises on a sale-leaseback of warehouses in Mississauga or Vaughan?' | Industrial page, GTA research, public mandates |
| Overseas family office | 'Which Toronto advisors know the multifamily rental market?' | Investment page, dated reports, languages spoken |
| Office building owner | 'Which firm can lease up a Class B building with 30% vacancy downtown?' | Landlord leasing page, general approach, public references |
| Founder of a growing company | 'What does a tenant rep broker cost in Toronto and who pays the commission?' | Factual FAQ on how fees work |
| Fund manager | 'Which firms publish reliable research on the GTA industrial market?' | Dated quarterly reports in readable HTML, cited elsewhere |
| Company relocating | 'Who can compare Toronto, Markham and Oakville for a regional head office?' | Submarket pages, figures-based comparisons |
The common thread: every question names a submarket, a size, an asset class or a specific deal type. AI assistants answer from the sources they can find and judge consistent. A firm whose site talks about 'integrated real estate solutions' with nothing verifiable has little chance of being named.
Why some advisories show up and others do not
Nothing is assured, but the firms that get named share visible traits.
| Signal | What helps | What hurts |
|---|---|---|
| Consistent information | Same name, address and specialties on the site, directories and LinkedIn | Conflicting listings, old address, outdated team |
| Expertise pages | One page per service and asset class, written in facts | Generic pages, jargon without figures |
| Market research | Dated reports, explained methodology, readable HTML version | Scanned PDFs, undated or locked behind a form |
| Third-party mentions | Trade press, industry associations, conference programmes | No mention anywhere off the site |
| Structured data | Organization, people, addresses and FAQ marked up | No markup, information only inside images |
| Freshness | Pages updated with a visible date | 2022 market statistics still live |
| Listings | Only properties genuinely under mandate, removed once leased | Expired listings, approximate square footage |
Two Ontario constraints deserve attention. Real estate brokerage there sits under a licensing and advertising regime: every claim about a transaction or a listing must stay accurate and authorised, and the site must reflect each advisor's actual registration status. Websites of Ontario organisations are also in scope of AODA accessibility requirements, email campaigns fall under CASL and personal data under PIPEDA. This is not legal advice: have your pages reviewed by your own counsel.
For transactions the rule is simple: cite only what is public and approved by the client. An assistant repeating an inaccurate figure about a deal creates a real exposure for the firm.
What the work covers and what it costs on the market
In broad terms, the work covers an audit of what AI assistants currently say about your firm and your competitors, cleaning up your public information so it is consistent, factual pages by service, asset class and submarket, structured data markup, making your research readable and citable, and a dated tracking record. The market is young: prices vary widely and offers often overlap with SEO.
| Type of engagement | 2026 ballpark (USD, approximate) | What you usually get |
|---|---|---|
| One-off AI visibility audit | 2,000 to 7,000 USD (about 2,800 to 9,500 CAD) | Record of answers, gaps, priorities |
| Monthly retainer, mid-size advisory | 1,500 to 6,000 USD a month | Content, consistency work, monthly tracking |
| Retainer for a large multi-office firm | 6,000 to 15,000 USD a month | Several submarkets, reports, press relations |
| Rewrite of 20 to 40 expertise pages | 5,000 to 20,000 USD | Factual pages, markup, internal linking |
| HTML version of a quarterly report | 800 to 3,000 USD per report | Readable, dated version |
| Dated tracking only | 300 to 1,200 USD a month | Monthly record of answers |
These ranges reflect typical agency quotes and public rate pages: use them to frame a budget, not to compare two proposals line by line. At Kolonell this work is on quote, after a diagnostic. We built the website of Orven Properties, a real estate company in Dubai, a project where accurate listings and submarket pages mattered as much as design.
How to measure it: a dated record, not a feeling
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Serious measurement relies on a fixed list of 15 to 40 questions, asked of AI assistants like ChatGPT at regular intervals, logging the date, the full answer and three indicators: the share of answers naming your firm, the accuracy of what is said (specialties, address, team) and the sources cited. Add the business signals: inbound enquiries that mention an AI assistant, referral traffic, calls. Allow 3 to 6 months before a readable trend; answers vary from one day to the next, which is why repetition matters.
Mini case study
Illustrative example: Harbourline Partners, a fictional commercial real estate advisory with 14 advisors in downtown Toronto. Across 30 tracked questions in January, the firm appears in 3 answers, or 10%, and two of them give an old address. It commissions an audit at 4,000 USD, then a retainer at 2,500 USD a month for 6 months: 4,000 + 15,000 = 19,000 USD. By July, the record shows 8 answers out of 30, about 27%, all with the correct address. If a single new tenant representation mandate brings a commission of around 40,000 USD (market assumption), the spend is covered, but cause and effect still has to be checked against actual inbound enquiries.
FAQ
Are AI assistants like ChatGPT replacing Google for commercial real estate buyers?
No, they add to it. Many decision makers still use Google, LinkedIn and their network, but a growing share builds a shortlist with an AI assistant first. Tracking 20 to 30 questions is enough to see where you stand.
How long before we see an effect?
Usually 3 to 6 months for a measurable trend, depending on the starting state of your public information. Corrections to wrong facts, such as an address or a team change, sometimes show up faster.
Can we cite our transactions to be picked up more often?
Only those that are public and approved by the client. An inaccurate figure repeated by an assistant exposes the firm; 5 well-documented public deals are worth more than 50 logos without context.
What budget should we plan for a first diagnostic?
On the market, a one-off audit sits around 2,000 to 7,000 USD depending on the number of questions and competitors tracked (2026 ballpark). At Kolonell, the diagnostic is on quote.
Can a provider promise that an AI assistant will recommend us?
No. Answers depend on the sources available and shift over time: a serious provider commits to the work and the measurement, never to a recommendation.
Find out how AI assistants describe your firm. A dated record of 20 to 30 questions specific to your Toronto market shows where you stand against competitors. Request a diagnostic quote: reply within 48 hours.
Mohamed Bah
Fondateur, Kolonell
Passionate about digital and entrepreneurship in Africa, Mohamed has been helping Sénégalese businesses with their digital transformation since 2020. Founder of Kolonell, he believes every SME deserves a professional and accessible online présence.
