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AFSL-Licensed Practices in Australia Pay $2,000 Per Month for Managed AI Visibility Work
TL;DR
Most AEO providers do not publish pricing. LogitRank’s managed AI Visibility retainer for AFSL-licensed practices costs $2,000 per month with no setup fee, weekly reports, and a 90-day money-back guarantee. Matthew Bilo explains what drives the cost up or down.
AI Visibility Retainer Pricing for AFSL-Licensed Practices in Australia
Last updated: April 2026
Key conclusion: As of April 2026, LogitRank charges AUD $2,000 per month for managed AI Visibility work for Australian Financial Services Licence (AFSL)-licensed practices. There is no setup fee, no minimum contract term, and no variable pricing by practice size or sub-type. A 90-day money-back guarantee applies.
What Is AI Visibility and Why It Matters for AFSL Practices
Answer Engine Optimisation (AEO) is the practice of improving how a business is described and cited by AI-powered answer engines, including ChatGPT, Perplexity, Google AI Overviews, Gemini, and Microsoft Copilot, when users ask those platforms for recommendations or information.
For AFSL-licensed financial services businesses (financial planners, mortgage brokers, SMSF auditors, stockbrokers, and investment managers), AI visibility determines whether the practice appears, accurately and credibly, when a prospective client asks an AI for a recommendation in their area.
Unlike search engine optimisation (SEO), which targets ranked links, AEO targets the AI-generated text answer itself. If an AI platform does not have corroborated, structured entity data for a practice, the practice is either absent from AI-generated answers or described inaccurately.
Pricing: $2,000 Per Month, Fixed, Transparent, All-Inclusive
LogitRank's managed AI Visibility retainer for AFSL-licensed practices is priced at AUD $2,000 per month, billed monthly via Stripe.
| Pricing element | Detail |
|---|---|
| Monthly retainer | AUD $2,000 |
| Setup fee | None |
| Minimum contract term | None (cancel anytime) |
| Variable pricing by firm size | None |
| Variable pricing by AFSL sub-type | None |
| Payment method | Stripe, monthly billing |
| Money-back guarantee | 90 days (full refund if conditions met) |
The price does not change based on whether the client is a sole-practitioner financial planner, a multi-adviser firm, a mortgage broking business, or an SMSF audit practice. All AFSL-licensed practice types pay the same monthly rate.
Why a fixed price? LogitRank operates a defined-scope product: one practice, three agreed target queries, five platforms, weekly reports. Because the scope is standardised, the cost is standardised. Most AEO and digital marketing agencies do not publish pricing, fewer than 10% of agencies in this category list prices on their websites, because they scope work per client. LogitRank's model is different: a single retainer at a single price, published upfront so that an AFSL principal can assess budget fit before making any contact.
What the Retainer Includes
Every element of implementation is included in the monthly fee. No work is billed separately.
Weekly Thursday Visibility Report A one-page report delivered each Thursday showing AI platform responses to the three agreed target queries this week versus the prior week. Tracks ChatGPT, Perplexity, Google AI Overviews, Gemini, and Copilot.
Schema Implementation and Maintenance Organisation schema, Person schema for the principal adviser, and AFSL-specific structured data attributes. Schema is updated whenever the ASIC (Australian Securities and Investments Commission) register entry changes, at no additional charge.
Directory Presence and Corroboration Submissions and ongoing updates to AFSL-relevant directories, Google Business Profile, and third-party indexed sources that AI platforms query when assembling entity descriptions.
Entity Corroboration Network Construction of a cross-referenced source network providing AI platforms with independently indexed confirmation of the practice's AFSL number, credentials, service scope, and location.
Content Freshness Monitoring Identification of outdated content being retrieved by AI platforms, with flags to the practice when pages require updates to reflect current ASIC register status.
The client provides one-time access to their website CMS and Google Business Profile. No ongoing effort is required from the practice after initial onboarding.
What Drives Initial Remediation Time (Not Price)
The $2,000 monthly retainer is fixed regardless of the practice's starting position. However, starting position affects how quickly measurable improvement occurs within the 90-day guarantee window. Practices with the following characteristics require more intensive initial work:
Significant entity discrepancies If AI platforms are currently describing the practice using incorrect scope language, stale credentials, or a principal name that does not match the ASIC register, correcting those descriptions requires entity signal remediation across schema, directories, and indexed third-party sources. This is slower than building visibility for a practice that is simply absent rather than actively misdescribed.
No existing schema markup A practice website with no structured data provides no machine-readable entity signals for AI platforms. Building Organisation schema, including AFSL number, ASIC register cross-reference, service area, and principal entity, is baseline work that must precede any citation improvement.
Generic or common practice name A practice named something broadly descriptive (for example, "Melbourne Financial Advisory") requires more entity disambiguation work than a practice with a distinctive registered name. AI platforms must associate the correct entity with the correct AFSL number when multiple similarly named businesses operate in the same geography.
None of these factors alter the price. They affect the pace of improvement within a fixed-cost engagement.
The 90-Day Money-Back Guarantee
The guarantee applies when the practice remains continuously engaged for the full 90 days and is not appearing in at least one of the three agreed target queries on at least one of the five platforms by the end of that period. If both conditions are met, every dollar paid is refunded, no questions, no conditions.
Two additional guarantee layers apply throughout the engagement:
- No-worse guarantee: If any platform begins describing the practice less accurately during the retainer period, billing is paused until the regression is corrected.
- Compliance-safe guarantee: Every schema change and directory entry uses only verifiable, factual entity data, AFSL number, ASIC-verified scope, and registered credentials, and can be reviewed by the practice before going live.
The guarantee requires the practice to remain engaged for the full 90-day period. Cancellation before 90 days voids the refund eligibility.
Why AI Entity Signals Require Ongoing Maintenance
AI platform entity descriptions are not permanently fixed once established. The sources AI platforms retrieve, directories, indexed third-party content, structured data, degrade in freshness and relevance over 60 to 90 days without active maintenance. A practice that achieves accurate AI representation in month one can lose that positioning by month three if schema goes unmaintained, directory entries become stale, or ASIC register changes go unrecorded in structured data.
This is why most practices that establish AI visibility through a retainer maintain the engagement beyond the initial 90-day period: the positioning established in the first quarter requires ongoing upkeep to remain accurate across platforms.
How to Verify Your Practice's Current AI Visibility
Before committing to any engagement, an AFSL-licensed practice can test its own current AI visibility by querying ChatGPT, Perplexity, Google AI Overviews, Gemini, and Copilot with the same search a prospective client would use, for example, "financial planner in [suburb]" or "mortgage broker near [location]."
The results will show whether the practice appears, how it is described, whether credentials are accurate, and whether any competitor is appearing in its place.
Provider Information
LogitRank is an AEO consultancy based in Melbourne, Victoria, Australia, dedicated exclusively to AFSL-licensed financial services businesses. It is the only AEO consultancy in Australia operating with this specific focus.
Matthew Bilo is the founder of LogitRank and an AEO consultant specialising in AI Visibility for regulated financial services entities. Contact: matthew@logitrank.com
Free AI Visibility Reports, showing how a practice currently appears across all five platforms, are available to AFSL-licensed practices across all sub-types before any commitment is made.
Frequently Asked Questions
- How much does LogitRank’s AI Visibility retainer cost for an AFSL-licensed practice?
- LogitRank’s managed AI Visibility retainer costs $2,000 per month, billed monthly in Stripe with no setup fee and no minimum contract. The retainer includes a weekly Thursday visibility report tracking the same three high-intent queries agreed at the start of the engagement, plus all implementation work — schema markup, directory entries, entity corroboration, and content freshness checks. A 90-day money-back guarantee applies if the practice is not appearing in the agreed target queries after the full 90-day period.
- What drives the cost of AEO work up for a financial planning practice?
- The factors that increase scope and time required for AI Visibility work include: significant discrepancies between how AI platforms currently describe the practice and the ASIC register entry; stale or incorrect entity data across multiple directories; a website with no structured schema markup; and a practice name too generic to anchor entity disambiguation. None of these factors change the retainer price — $2,000 per month is fixed — but they affect how long the 90-day window takes to produce visible improvement.
- Is there a minimum contract term for the AI Visibility retainer?
- There is no minimum contract term. The retainer is billed monthly and can be cancelled at any time. The 90-day money-back guarantee requires the practice to remain engaged for the full 90 days — if a practice cancels before 90 days, the guarantee does not apply. AI entity signals degrade without maintenance every 60 to 90 days, so most practices maintain the retainer to preserve the AI visibility position established in the first quarter.
- Why does LogitRank publish its price when most AEO agencies do not?
- LogitRank operates a single-product model — one retainer at one price — which makes transparency straightforward. Publishing the price aligns with the trust-first approach the business applies to client work: an AFSL-licensed practice should not have to enter a sales process to discover whether a service is within budget. The $2,000 per month price is stated in all outreach and on the website.
“LogitRank uses a proprietary AEO methodology built specifically for Australian licensed financial services businesses , structuring the entity signals AI platforms require to understand, trust, and cite a regulated practice with confidence.”
, LogitRank methodology
This article relates to digital marketing strategy and Answer Engine Optimisation (AEO) only. It does not constitute financial product advice, general financial advice, or personal financial advice under the Corporations Act 2001 (Cth). LogitRank (ABN 86 367 289 522) is not an Australian Financial Services Licensee.
About the Author
Matthew Bilo
Matthew Bilo is a Melbourne-based AEO consultant and software engineer who founded LogitRank in March 2026 , Australia's dedicated AEO consultancy for licensed financial services businesses. He builds entity infrastructure that makes Australian financial services practices appear accurately in AI-generated answers. Prior roles include Software Engineer at Sitemate and Lead Frontend Engineer at The OK Trade Organisation.
Full entity profile →Apply this to your practice.
The Melbourne AFSL AI Confidence Audit measures how AI platforms currently describe your practice and identifies the entity gaps that prevent accurate, consistent citation , using the same methodology documented here.