AI Visibility Audit for property developers · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity which developer to trust, which off-plan project to buy, or who actually delivers on time, the engine answers - from whatever forums, review sites, portals, and news it trusts. If that answer is not you, the buyer's first shortlist formed at the research stage, before your sales center or your brokers ever saw them.
28 Labs measures your share of AI recommendations for the off-plan buyer queries that produce sales, ties it to your real cost per qualified lead and direct-sales pipeline, 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 off-plan sales
scored
A developer has tens of thousands of meaningful buyer queries across every live and upcoming project. We score each by commercial intent and track the set that drives qualified buyers, not the explainer long tail.
The shift
When a buyer asks an engine which developer to trust or which project to buy, it answers off your property. You do not know if your brand and your projects came back, or a competitor's.
The buyer's first shortlist now comes from whoever the engine trusts. When that is not you, direct interest softens and you rebuy the same buyer through paid campaigns or a broker who charges commission on it.
We measure your share of AI recommendations across the high-intent developer, project, and payment-plan queries on all four engines, then tie it to your cost per qualified lead and direct-sales pipeline.
The board-level risk
A developer's advantage is being the brand buyers trust to launch, deliver, and hand over. Answer engines now sit in front of the moment that trust is decided. The exposure is specific, and it is measurable.
"Best developer in {city}" answers name competitors. The buyer's first shortlist forms in a chat window before your sales center or your brokers ever see them, and you never get on it.
"Is {developer} reputable / do they deliver on time" gets decided by forums, review aggregators, and news the engine trusts - a verdict on your delivery record rendered about you, without you.
When AI routes buyers to portals and brokers instead of your projects, you pay commission on demand that started as direct interest - margin handed away on a buyer who was already looking for you.
A new project has no history. If the engines cannot read and corroborate your project data at launch, the highest-spend weeks of the campaign are invisible in AI answers, exactly when the buyer is forming a view.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another developer's numbers to make the point.
The query universe
A developer's buyers do not ask four questions. They ask thousands, across a journey from first research to a sales-center visit. 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 qualified buyers, mapped to where the buyer is in the decision.
Developer-trust, project-comparison, and payment-plan queries carry the qualified buyers; explainer queries like "what is off-plan property" carry almost none. We weight what you track to where the pipeline 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 developer 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 sale. 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. We measure from the outside, retrieval-based - we do not measure training-data exposure directly, and we tell you where that line sits.
Directly observed. The signal is present in your own logs, CRM, 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, you launch the next project, and your competitors move.
The integrated engagement connects to your logs, CRM, and paid feeds, so the AI-share read sits next to the CPL and pipeline 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 competing developer surges - useful in the weeks around a launch - and a monthly read that ties the trend to CPL and pipeline. 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 campaign budget against it.
Honest about the tiers. The live read on how much of your CPL and pipeline movement is AI needs your first-party data - your logs, CRM, 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 which developer. The CPL-and-pipeline loop comes when we wire into your systems.
What winning takes
A developer wins when it becomes the source the engines trust to answer the category - which developer delivers, which project fits, which payment plan. That is structural. More blog posts will not do it. Being the authoritative, reachable, and unambiguous source for your project and track-record data will.
We diagnose where the engines already trust you, where they trust a competing developer 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 CPL-and-pipeline loop would shape up for your market, 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.