AI Visibility Audit for financial services · AI Discovery Intelligence
When a customer asks ChatGPT, Claude, Gemini, or Perplexity which bank to use, whether to refinance, or if they will qualify, the engine answers - naming a competitor, a comparison site, or a partner, and quoting terms it pulled from somewhere you do not control. That conversation used to start with you.
28 Labs measures your share of AI recommendations for the high-intent money queries that drive acquisition, ties it to your real traffic and acquisition cost, and surfaces where the engines describe you incorrectly.
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 acquisition
scored
A bank has tens of thousands of meaningful customer questions. We score every one by commercial intent and track the set that drives applications and deposits, not the financial-literacy long tail.
The shift
When a customer asks an engine a money question, it answers off your property - naming someone, sometimes quoting your terms, and you never see it. You do not know if your name came back, or a competitor's.
The "should I / which one" research that used to open a banker relationship now resolves in a chat window. If you are not named, acquisition softens and you rebuy it as paid - because you buy back the demand AI stopped sending you for free.
We measure your share of AI recommendations - not raw citation counts - across the high-intent money queries on all four engines, tie it to acquisition cost, and flag where the engine states your terms wrong.
The board-level risk
A bank's franchise is being the institution customers go to discover, compare, and transact on money. Answer engines now sit in front of all three. The exposure is specific, it is regulated, and it is measurable.
The "should I refinance / which account / how much can I borrow" questions that used to open a relationship now get answered directly - the top of your funnel moves off your property and into a chat window.
For "best {product}" queries, engines lean on comparison sites and rate tables, naming third parties and competitors ahead of you. Your role as the place people go to decide is exactly what an answer engine is built to absorb.
The highest-margin moments - rates, eligibility, pre-qualification - are exactly where a competitor or partner gets named at the point of intent. That is the part of the funnel you least want decided off-platform, and the easiest to leak without seeing it.
Engines state your rates, fees, eligibility, or terms - and sometimes get them wrong, or give regulated advice attributed to your brand. A control risk your risk teams have no line of sight into.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another institution's numbers to make the point.
The query universe
A bank's customers 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 drive acquisition, mapped to where the customer is in the decision.
Eligibility, rates, and application queries carry the acquisition value. Open-ended financial-literacy research like "how compound interest works" 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 - 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 acquisition-cost 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 acquisition cost. 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 acquisition-cost 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-acquisition-cost loop comes when we wire into your systems.
What winning takes
An institution 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 customers 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-acquisition-cost 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.