The AI platform Financial Services use to get recommended on
People ask an AI who to bank with, borrow from and invest through before they ever compare providers themselves. LBOX measures what the engines say about you, finds where a comparison site is answering in your place, and builds the evidence that changes it.
Measured across
Perplexity
Gemini
Google AI
Claude
Copilot
Meta
DeepSeekGrok... and moreEvery one of them is competing for the same answer. One name comes back when a buyer asks.
What makes Financial Services different
Not general marketing advice. These are the constraints your category actually operates under.
Every claim you make is governed. The paraphrase is not.
Suitability, disclosure, risk warnings and jurisdiction rules bind every sentence you publish. An engine summarising your product drops all of it, keeps the attractive half, and presents the result as neutral guidance.
Comparison sites are the cited authority.
NerdWallet, Bankrate and Investopedia are what the engines quote when asked where to put money. Those tables are built on affiliate economics, and they now function as the source a model treats as impartial.
Your trust signals live in the fine print.
Regulatory status, licensing, deposit protection and audited performance are what actually decide this category, and they sit in footers, PDFs and disclosure pages that answer engines skip entirely.
The engine gives the advice and names whoever it cites.
People ask questions that sit right at the edge of advice. The model answers anyway, and the provider named in that answer inherits the trust that comes with it.
What you get back
The console, measuring a company in Financial Services against the questions its buyers ask. Every brand named, every domain cited, every engine separately.
A worked example on an illustrative company, shown to display the console. Your own numbers come from measuring your questions through the engines’ own APIs.
See how every model describes your products
Visibility and position on the unbranded money questions people actually ask, measured per engine.
- Ranked against the providers the engines actually named
- See where a model reads your terms without naming you
You against the field, engine by engine
unbranded questions| Brand | Gemini | AI Overviews | OpenAI | Perplexity | Claude | ChatGPT |
|---|---|---|---|---|---|---|
| Calvert & Poole | 33% | 29% | 28% | 24% | 16% | 32% |
| Northgate Capital | 26% | 23% | 21% | 19% | 12% | 25% |
| Ridgeline Financialyou | 23% | 13% | 20% | 15% | 9% | 3% |
| Ambervale Bank | 15% | 12% | 10% | 13% | 6% | 12% |
Turn what the answers get wrong into work compliance can approve
Findings arrive as a worklist sorted by who can act: what LBOX drafts, what needs compliance sign off, what needs your web team.
- Scored against your category, not a generic checklist
- Nothing publishes without your approval
The worklist
Prove the change, quarter after quarter
Every measurement is stored, so the line lengthens each cycle and the movement is on the record rather than asserted.
- The same questions, asked again and checked
- Sources broken out per model, per page
Visibility over time
every measured runKnow when the models come to read your disclosures
Crawler activity captured at the edge, so you can see which models fetch your product and disclosure pages, and how often they return.
- Every model, every page, logged over time
- Robots and content signals kept current
Crawler activity
hits by model, last 30 daysWhere you disappear as the customer gets closer to opening an account
Presence falls away as intent rises. The who-should-I-use questions are the ones that convert, and the ones comparison sites win.
- Visibility split by where the question sits in the journey
- Names the stage that costs you the account
Visibility by buying stage
where you show up in the journeyThe sources the engines trust in financial services
Comparison sites, regulators and forum threads decide what the engines repeat about you. Almost none of it is yours, which is exactly why it can be changed.
- Owned versus earned, so you know what to build and what to earn
- Every citation traced to the answer it supported
Which domains the engines cite
886 citationsThe program for Financial Services
Everything below is included. Measured through the engines’ own APIs, so the same questions can be asked again and the result checked.
- Measure what every major AI engine says about your products today, through the engines own APIs
- The money questions your customers actually ask, mapped by product, life stage and jurisdiction
- Where a comparison site is answering in your place
- Whether the engines describe your rates, terms and eligibility correctly
- Which sources the engines cite for your category, and which of those you control
- Evidence pages built from your own disclosures and product terms, in a form engines can read
- Re-measurement on the same questions, so the change is provable rather than asserted
What this does not show: what any individual user saw. LBOX measures what the engines answer through their own APIs, which is repeatable and checkable. It is not a record of any one person’s session, and it does not predict revenue.