AI Visibility Audit for job platforms · AI Discovery Intelligence
When a candidate asks ChatGPT, Claude, Gemini, or Perplexity the best place to find a job - or an employer asks where to hire - the engine names a platform. You cannot see whether it is you or a competitor, and that choice is already moving both sides of your platform and pushing up your cost per lead.
28 Labs measures your share of AI recommendations for the candidate and employer queries that drive your platform, 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 both sides
scored
A job platform has tens of thousands of meaningful candidate and employer queries. We score each by intent and track the set that drives applications and job posts.
The shift
When either side asks an AI engine where to go, it names a platform as the answer. That recommendation happens off your property, in a place your analytics never see. You do not know if your name came back, or a competitor's.
The demand the engines used to send you organically now lands on whoever they recommend, on both sides - candidates and employers alike. To hold volume you rebuy it as paid, so the gap shows up as softer organic traffic and a higher cost per lead.
We measure your share of AI recommendations - not raw citation counts - across the high-intent candidate and employer queries that drive revenue, on all four engines. Then we tie the movement in that share to the movement in your traffic and CPL.
The board-level risk
A job platform's moat is being the place candidates and employers go to discover, compare, and transact. Answer engines now sit in front of both sides. The exposure is specific, and it is measurable.
"Best job sites for {role}" gets answered directly; the job-seeker who used to start on your platform starts in a chat window. The discovery moment that used to begin on your property now begins inside a chat.
"Best platform to hire {role}" names competitors or ATS vendors ahead of you, where your highest-value demand lives. The employer relationship is exactly what an answer engine is built to route elsewhere.
Pay and "what's it like to work at {company}" queries get answered from aggregators, not you - so the research that builds trust and brings both sides back happens elsewhere, off-platform and out of your sight.
Employer-brand and platform-trust verdicts get decided by the review sites the engine leans on. A trust verdict gets rendered about your platform, without you, and it shapes who chooses to job-hunt or hire with you.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another platform's numbers to make the point.
The query universe
A job platform's candidates and employers do not ask four questions. They ask thousands, across both sides of 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 drive revenue, mapped to where each side is in the decision.
Availability, comparison, and employer-hire queries carry the value. Open-ended career-advice research like "how to write a CV" carries 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, on both the candidate and employer sides - 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 job platform wins when it becomes the source the engines trust to answer the category - on both sides. That is structural. More blog posts will not do it. Being the authoritative, reachable source for the data candidates and employers 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.