AI Visibility Audit for real estate · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity what their home is worth, the best area to buy in, or the right agent to call, the engine answers - from whatever AVM, portal, or review site it trusts. If that is not you, the highest-intent moment in the transaction just happened somewhere you cannot see.
28 Labs measures your share of AI recommendations for the queries that produce listings and buyer leads, ties it to your real traffic and CPL, and hands your teams the moves to win it back.
4 engines · intent-scored query universe · every read confidence-tagged
Measured from the outside. No SDK in your rendering path. We do not measure training-data exposure - and we say so.
The queries that move listings and leads
scored
A real-estate brand has tens of thousands of meaningful buyer and seller queries. We score each by commercial intent and track the set that drives leads, not the explainer long tail.
The shift
When someone asks an engine what a home is worth or who to call, it answers off your property. You do not know if your valuation and your agents came back, or a competitor's.
The seller's first number and the buyer's first shortlist now come from whoever the engine trusts. When that is not you, leads soften and you rebuy them as paid.
We measure your share of AI recommendations across the high-intent valuation, availability, and agent queries on all four engines, then tie it to your traffic and CPL.
The board-level risk
A real-estate firm's moat is being the place buyers and sellers go to value, discover, and decide who to deal with. Answer engines now sit in front of all three. The exposure is specific, and it is measurable.
"What's my home worth / is this overpriced" gets answered from third-party automated valuations, so the seller's anchor number comes from someone else.
"Best brokerage / agents near me" names competitors directly; the lead that used to land on your site starts in a chat window.
"Homes for sale in {area}" is answered from whichever source the engine trusts - which may not be your inventory, even when it is freshest.
"Is {agent/developer} reputable" gets decided by review aggregators and forums - a trust verdict rendered about you, without you.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another firm's numbers to make the point.
The query universe
A real-estate firm's buyers and sellers do not ask four questions. They ask thousands, across a journey. The work is not to track 50,000 random queries. It is to score every query by commercial intent and monitor the high-intent ones that actually produce listings and leads, mapped to where the buyer is in the decision.
Valuation, availability, and agent-choice queries carry the listings and leads; process-explainer queries carry almost none. We weight what you track to where the money is - so the share number you read is the one that matters to the business, not a vanity average across the long tail.
The decision system
Most tools stop at "you were cited less." That is a metric, not a decision. We frame it as market share - the share of high-intent recommendations you hold against the competitor taking them - and we connect that share to the numbers your business already runs on.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in your own logs, CDP, or AI-engine output. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
In your stack
This is not a deck that ages the day it ships. It is a live read wired into the systems you already run, so the share number and its business impact stay current as the engines re-crawl and your competitors move.
The integrated engagement connects to your logs, CDP, and paid feeds, so the AI-share read sits next to the traffic and CPL data your teams already trust. No SDK in your rendering path.
You get a flag when your share on a high-intent query set moves sharply or a competitor surges, and a monthly read that tracks the trend and ties it to traffic and CPL. Not once a quarter, on a slide.
HIGH, MED, or LOW on every finding, so the team acting on it knows exactly how much weight it carries before committing engineering or budget against it.
Honest about the tiers. The live read on how much of your traffic and CPL movement is AI needs your first-party data - your logs, CDP, and paid feeds. That is the integrated, enterprise engagement, not a free snapshot. A standalone audit measures your share of AI recommendations across the four engines from the outside and shows where you are losing ground and to whom. The traffic-and-CPL loop comes when we wire into your systems.
What winning takes
A real-estate firm wins when it becomes the source the engines trust to answer the category. That is structural. More blog posts will not do it. Being the authoritative, reachable source for the data buyers and sellers actually ask about will.
We diagnose where the engines already trust you, where they trust a competitor instead, and the specific moves that shift that trust toward you. The output is a build plan your teams can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the share read and the traffic-and-CPL loop would shape up for your category, and scope a live run.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.