For Mortgage Brokers
LogitRank helps Melbourne mortgage brokers appear in AI-generated suburb comparisons.
When a prospective buyer or refinancer asks ChatGPT or Perplexity for a mortgage broker in their suburb, the response names specific practices — not generic directories. Brokers whose entity data AI platforms can verify appear. Brokers without structured entity signals don't.
LogitRank builds the entity architecture that puts your brokerage in that answer — for the specific high-intent queries your prospective clients are already running.
The Problem
Pre-contact trust formation has moved to AI platforms. Most brokers aren't in the conversation.
Before calling a mortgage broker, prospective clients increasingly ask AI platforms who to contact in their suburb. These platforms generate responses from ASIC's register, directory listings, review sites, and entity-structured web content. The result is a short list of named brokers — and if your practice isn't in that list, the prospective client calls someone else.
This isn't a marketing problem — it's an entity data problem. AI platforms can only cite brokers whose credentials they can verify. Without a consistent, structured entity footprint, your practice is invisible to the systems your prospective clients are using before they pick up the phone.
LogitRank fixes the entity data AI platforms read about your brokerage: your AFSL number, ASIC register link, authorisation scope, credit licence conditions, and corroboration network — structured so AI platforms can confidently name you in response to high-intent client queries.
What's included
Week 1 — Baseline and gap analysis
We agree on three high-intent queries your prospective clients are running — for example, 'mortgage broker Fitzroy first home buyer' — and run your free report as the formal baseline. We then audit your entity signals across all five platforms and map every gap.
AFSL and credit licence entity signals
Your AFSL number, ASIC register link, credit licence conditions, and authorisation scope — structured in schema markup and entity-verified across the corroboration network AI platforms read before citing a brokerage.
5-platform weekly monitoring
ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot — tested weekly against the same three high-intent queries your prospective clients are running.
Weekly report, every Thursday
A one-page update showing what AI platforms said about your brokerage this week versus last week, tracked against your agreed target queries.
Knowledge Graph enrichment
Wikidata verification, directory submissions, and entity corroboration signals — the network of sources AI platforms read to confirm your brokerage entity before citing you.
Three-layer guarantee
90-day money-back if not appearing in target queries. No-worse guarantee: billing pauses if any platform describes you less accurately. Compliance-safe: every change is factual entity data, no marketing claims.
$2,000/month. Cancel anytime.
Billed monthly. No setup fee. No minimum commitment. One hour of your time in month 1 — website access and AFSL scope confirmation. Nothing after that.
Common questions
- What is Answer Engine Optimisation (AEO) for mortgage brokers?
- Answer Engine Optimisation (AEO) for mortgage brokers is the practice of structuring a brokerage's entity data so that AI platforms — including ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot — cite the practice accurately in response to consumer comparison queries. LogitRank applies the Kalicube Process™ developed by Jason Barnard to build the entity signals AI platforms need to confidently recommend a mortgage brokerage in suburb-level client searches. The results are tracked weekly and documented at logitrank.com/case-studies.
- Do AI platforms recommend specific mortgage brokers by suburb?
- AI platforms including ChatGPT and Perplexity regularly respond to consumer comparison queries — such as 'best mortgage broker in Melbourne for first home buyers' or 'mortgage broker Fitzroy refinancing' — with specific broker names and practice profiles. Mortgage brokers without structured entity data are absent from these responses, ceding pre-contact trust formation to competitors who appear. LogitRank builds the entity architecture that allows AI platforms to confidently cite a broker's practice in response to high-intent consumer queries.
- What queries do prospective mortgage clients run in AI platforms?
- Prospective mortgage clients typically run suburb-specific comparison queries in AI platforms before making contact with any broker. Common query patterns include 'best mortgage broker [suburb] first home buyer,' 'mortgage broker [suburb] refinancing,' and 'AFSL-licensed broker Melbourne [loan type].' These queries return named brokers, not generic directories — and the brokers named are the ones whose entity data AI platforms can verify from structured sources.
- Does AI description accuracy affect a mortgage broker's AFSL compliance?
- AI-generated descriptions of mortgage brokers can create AFSL compliance risk when platforms infer authorisation scope or credit licence conditions from inconsistent or absent entity data. A prospective client reading an AI-generated description forms expectations before making contact — expectations that may not match the broker's actual AFSL authorisation. LogitRank structures entity data so that AI-generated descriptions of a mortgage brokerage are grounded in ASIC-verified credentials and accurate authorisation scope.
Start with the free report.
We agree on three high-intent queries your prospective clients are running — for example, "mortgage broker Fitzroy refinancing" — and test them across all five AI platforms. You receive verbatim results, who's appearing instead of you, and one finding you can act on immediately. No commitment.