For Stockbrokers
LogitRank helps Melbourne stockbrokers appear accurately in AI-driven credential checks.
Before a retail client opens an account or a professional counterparty initiates engagement, they verify credentials. Increasingly, that verification happens in AI platforms — and the response is generated from ASIC's register, directory listings, and entity-structured web content, not from a call to your office.
When your entity data is absent or inconsistent across those sources, the AI response hedges or omits your practice entirely. LogitRank fixes the entity data AI platforms read about your brokerage so that credential checks return accurate, verifiable descriptions — every time.
The Credential Risk
AI platforms answer credential questions whether your entity data is ready or not.
AI platforms don't wait for you to structure your entity data before generating responses about your brokerage. They synthesise whatever is available — ASIC's register, review sites, outdated directory listings, and third-party financial databases — and produce a response that reflects the quality of that data, not the reality of your practice.
For stockbrokers, the most common errors are: AFSL numbers not attributed to the correct entity, authorisation scope described too broadly or too narrowly, and responsible manager credentials assigned to the wrong individual. These errors don't disappear — AI platforms cache and recirculate them until the underlying entity data is corrected.
LogitRank structures the entity data AI platforms read: your AFSL number, ASIC register link, authorisation scope, and corroboration network — so AI-generated descriptions of your brokerage are accurate before a client or counterparty sees them.
What's included
Week 1 — Baseline and credential audit
We run your agreed queries as the formal baseline and audit your entity signals across all five platforms, mapping every gap between what AI platforms say about your brokerage and what your ASIC authorisation records.
AFSL and ASIC entity signals
AFSL number, ASIC register link, responsible manager credentials, authorisation scope, and securities class authorisations — structured in schema markup and verified across the corroboration network AI platforms read.
5-platform weekly monitoring
ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot — tested weekly against the same three queries agreed at onboarding, covering both retail and professional query patterns.
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 it.
Three-layer guarantee
90-day money-back if not appearing accurately in target queries. No-worse guarantee: billing pauses if any platform describes your brokerage less accurately. Compliance-safe: every change is factual entity data only.
$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 stockbrokers?
- Answer Engine Optimisation (AEO) for stockbrokers 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 credential verification and recommendation queries. LogitRank applies the Kalicube Process™ developed by Jason Barnard to build the entity signals AI platforms need to confidently describe a stockbroker's AFSL authorisation, ASIC registration, and service scope. The results are tracked weekly and documented at logitrank.com/case-studies.
- How do AI platforms verify a stockbroker's credentials and authorisation scope?
- AI platforms synthesise information about stockbrokers from publicly available data, including ASIC's register, directory listings, review sites, and entity-structured web content. When a broker's AFSL number, authorisation scope, or entity description is absent or inconsistent across these sources, AI responses hedge or omit the practice entirely. LogitRank structures entity data so that AI platforms can confidently cite a broker's verified credentials in response to both retail and professional queries.
- What queries do clients run when verifying a stockbroker's credentials in AI platforms?
- Retail clients and professional counterparties run queries including 'stockbroker Melbourne AFSL,' 'securities dealer Melbourne equities,' and broker-name verification queries that ask AI platforms to describe a specific practice's credentials and service scope. In both cases, the AI response is the first impression — and it is formed before any direct contact is made.
- Does AI accuracy matter differently for retail versus professional stockbroking clients?
- AI accuracy matters for both retail and professional stockbroking client segments, but for different reasons. Retail clients use AI platforms to verify basic credentials and service scope before deciding whether to open an account. Professional counterparties use AI to verify ASIC registration, AFSL authorisation, and entity scope before initiating institutional engagement. LogitRank structures entity data to satisfy both query types: consumer-facing descriptions are accurate and approachable; professional-facing descriptions are ASIC-verified and precisely scoped.
Start with the free report.
We agree on three queries — the ones a retail client or professional counterparty would run when verifying your credentials — and test them across all five AI platforms. You receive verbatim results and a plain-English summary of every gap. No commitment.