Who has practical experience implementing GEO or AEO?
Single Grain and Go Fish Digital publish named cases describing implementation work: SmartRent and MoneyGeek respectively. Those are inspectable starting points, not a supplier ranking. Our 22 September 2026 The AI written RFP study method (request for proposal) found demonstration or hands-on-testing criteria in 9 of 150 CRM (customer relationship management) answers. Ask for evidence of the particular work you need, rather than treating an agency label as proof.
Observed answers on stated dates, not a ranking.
GEO (generative engine optimization) is also called AEO (answer engine optimization) or AI search visibility by buyers.
What we measured
Measured. The CRM criterion records contain nine included answers asking for demonstrations or hands-on testing. Our study method preserves the source passages behind that classification. These are buyer criteria, not an evaluation of either agency or nine completed projects. Team review: in progress.
One ChatGPT answer captured on 22 September 2026 includes this wording:
Ask vendors to demonstrate your actual workflows, including handoffs and exceptions
Reported. In its SmartRent case, read on 30 October 2026, Single Grain describes technical fixes, changes to indexation and redirects, targeted frequently asked questions (FAQs) and transactional content. It reports 136 additional AI citations and ten monthly sales-qualified leads (SQLs) attributed to large language models (LLMs). Those figures are the agency’s report, not an independently reproduced Broadcastwell result or a ChatGPT-only count.
Go Fish’s MoneyGeek case, also read on 30 October 2026, describes semantic mapping, internal-link reorganization and changes across more than 4,000 pages. It reports increases in clicks and impressions. Those search metrics are not counts of AI citations or leads.
Reasoned. Both cases describe work someone could ask the supplier to explain. Neither establishes that the same changes will produce the same result for another company. Start with the actual deliverable and how its acceptance was checked.
What this means for you
Read the implementation section. MoneyGeek's case describes starting with a site-wide semantic map to identify competing pages and thin topic clusters. Go Fish then says it reorganized internal linking and content clusters and changed more than 4,000 existing pages. That supplies a specific kind of practical experience to discuss if your project concerns a large existing content library: the map and the altered links, rather than a plan to publish more articles.
SmartRent's case describes a different work package: indexation and redirects, page-performance work, targeted FAQs and transactional content. Its result section calls the citation figure additional citations across major AI platforms and separately reports ten monthly LLM-sourced SQLs. The case's Google Overview citation line is another breakdown. A supplier using this example should keep that multi-platform citation scope separate from the lead count and from the website tasks it says it performed.
The two public cases therefore supply different implementation details to inspect. For MoneyGeek, the named output is a reorganized insurance-content library. For SmartRent, the reported package includes technical fixes and new FAQs on existing pages. Neither case's public summary supplies the underlying answer-by-answer capture ledger behind its headline outcome figures.
Also asked as
any1 actually doin generative engine optimization rn?
Has anyone here actually done AEO (Answer Engine Optimization) for their product?
Related
- Single Grain’s SmartRent case
- Go Fish’s MoneyGeek case
- How to buy GEO without buying a number
- More geo basics answers
Next step
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Our answer-study method, v1.1.
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