Case Studies
The LogitRank AEO Case Study: Building Melbourne's AI-Recognised AEO Authority From Zero
Answer Engine Optimisation (AEO) is only credible when the practitioner applies it to their own entity. LogitRank documents its own AI visibility journey publicly — publishing weekly prompt snapshots and monthly rollup reports from a zero baseline in March 2026 — so clients and journalists can verify the methodology against real, auditable data.
The Consultancy Is Its Own Case Study
LogitRank was founded in March 2026 with no prior AI citation history, no Knowledge Panel, and zero mentions across major AI platforms. Matthew Bilo applies the Kalicube Process™ to the LogitRank entity using the same methodology offered to clients — beginning with Wikidata entity establishment, structured data deployment, and systematic co-citation seeding. Each week's prompt audit is published as a weekly snapshot; each month's data is rolled up into a comprehensive monthly report.
The decision to build in public is deliberate. A consultant who cannot demonstrate their own methodology on their own brand provides no verifiable proof that the methodology works. Publishing the data — including periods of zero citation and hedged AI responses — makes the experiment journalist-citable, client-verifiable, and reproducible for any business that commissions an equivalent audit.
LogitRank is not a case study about a client. It is the proof of concept — an entity built from zero to AI-cited authority using only the methods documented on this site.
AI Visibility Metrics — LogitRank
Hedging Language %
0%
Baseline: March 2026
AI Mention Rate
0 / 5
Month 1 audit pending
Knowledge Panel Status
Pending indexation
Wikidata entry live
Methodology: 20 prompts × 5 platforms (ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot). Hedging language % measures the proportion of AI responses containing uncertainty qualifiers (“may be”, “appears to”, “I cannot confirm”) when LogitRank is queried directly. Weekly snapshots capture individual prompt results; monthly reports aggregate and trend the data.
Monthly AEO Reports
Monthly reports aggregate the full month's weekly snapshots into a single document: platform-by-platform citation breakdown, trended hedging language %, Knowledge Graph changes, and the optimisation actions taken. Published in the first week of the following month.
Month 1 Baseline — March 2026
Full rollup: verbatim AI responses across 5 platforms, hedging language count, entity recognition status, and optimisation actions taken in Month 1.
Month 2 report publishes April 2026.
Weekly AEO Snapshots
Each weekly snapshot records the raw prompt audit for that week: verbatim AI responses, hedging language instances logged, and any entity recognition changes observed. Published at the end of each week.
Weekly Snapshot — Week 1 (March 2026)
Week 1 prompt audit. Zero entity recognition across all 5 platforms. Wikidata entry submitted. Baseline hedging language logged.
Want your business tracked the same way?
The AEO Audit applies the same 20-prompt × 5-platform methodology to your entity — establishing where AI platforms currently position your business and identifying the gaps that prevent accurate, consistent citation.