Every ai search optimization (aeo / geo) guide we've published.
Pillar guides, field notes, and case teardowns — filtered to the workflows that ship ai search optimization (aeo / geo) for real engagements. Filter by topic, browse by depth, and jump straight into the work.
A curated reading path for ai search optimization (aeo / geo).
Every entry below is either a long-form pillar guide or a field note from a live ai search optimization (aeo / geo) engagement — no curated third-party content, no reposts. Use the filters to narrow by topic and the pagination to work through the whole shelf.
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How commercial security and automation firms can own the queries that drive 7-figure contracts.
Read the pillar guide →A niche-specific worked example of how we rank BMW, Porsche, and Audi specialists in the Central Texas market.
Read the pillar guide →Semrush shows 720 monthly searches for this exact question and rising. Here is the exact citation-earning playbook we ship in engagements — the entity graph, the first-100-words rewrite, the schema stack, and the tracking cadence.
Read the field note →AEO, GEO, LLM-SEO — the labels multiplied faster than the practice.
Read the field note →A prioritized framework for closing content gaps and reclaiming clicks based on our internal audit of over 150 local service markets.
Read the field note →Why wait for GSC updates? How we set up real-time indexing alerts and crawl monitoring for multi-location brands.
Read the field note →Where we ship ai search optimization (aeo / geo).
Optimize Plus is headquartered in Austin. We run local and national programmes for Austin-area operators — from Foreign Affairs Auto in South Austin to SaaS teams working across the Central Texas corridor.
Round Rock has its own search behaviour, its own local competitors, and its own buyer expectations. We build programmes that treat it seriously — with local case studies from HVAC, automotive, and home service operators.
Northwest of Austin, the Cedar Park–Leander corridor has matured into a distinct market with its own high-income buyers and its own competitive set. We build programmes tuned for it.
Georgetown has led US city growth rankings for years. That growth reshapes local search every quarter. We build programmes that keep up with the market — not last year's snapshot of it.
Getting cited by AI Overviews, ChatGPT, and Perplexity
Answer-engine and generative-engine optimisation is the newest discipline in this hub, and it's the one where 'best practice' is still being established through observation rather than a decade of Google guideline documentation. This track is sequenced from the most stable foundation (schema and entity signals, which have documented precedent) toward the most experimental (llms.txt and citation monitoring, which are genuinely emerging).
It's worth being direct about what AEO/GEO can and can't promise: there's no guaranteed mechanism to force a citation in a specific AI answer, because the underlying models don't expose ranking factors the way Google's Search Quality documentation does. What these guides teach is the set of structural and entity signals correlated with citation likelihood, based on direct observation across client accounts.
The sequence we teach
- 1. Get answer-engine schema right first
FAQPage, HowTo, Article-with-author, and Product schema are the structured signals answer engines most reliably parse to identify authoritative, extractable content.
- 2. Clean up entity signalling across the web
Wikipedia, Wikidata, Crunchbase, and LinkedIn consistency guides cover the entity graph that large language models draw on when assessing whether a brand or person is a recognised authority.
- 3. Restructure content for extraction, not just reading
Direct-answer paragraphs, comparison tables, and glossary entries are the structural patterns most consistently pulled into AI-generated answers.
- 4. Publish an llms.txt file and monitor citations monthly
This is the maintenance layer — an emerging protocol plus ongoing tracking of where and how often you're actually being cited.
Skills you should walk away with
- Schema.org implementation for FAQPage, HowTo, Article, and Product types
- Entity graph auditing across Wikidata, Wikipedia, and knowledge panels
- Content restructuring for direct-answer extraction
- llms.txt authoring and maintenance
- AI citation tracking (Semrush AI Search, custom prompt monitoring)
Where teams usually go wrong
Retrofitting a direct-answer paragraph onto a page structured for narrative reading rarely works as well as restructuring the page around the question it's meant to answer.
If your Wikipedia summary, LinkedIn description, and website About page describe your business differently, language models have conflicting signal about who you actually are, which reduces citation confidence.
Schema or content claims that overstate expertise or invent statistics are both an ethical problem and a practical one — they erode trust the moment a user or model cross-references the claim against another source.
llms.txt is a voluntary, unstandardised signal that not all AI crawlers currently respect — it's one input among several, not a guaranteed control mechanism.
How to measure progress
- Citation frequency across ChatGPT, Perplexity, and Google AI Overviews for target prompts
- Share of voice versus named competitors in AI-generated answers
- Schema validation pass rate (Rich Results Test, schema.org validator)
- Entity consistency score across Wikidata, LinkedIn, Crunchbase, and owned properties
- Referral traffic originating from AI assistant citations, where trackable
Questions we get about this track
- Can you guarantee my brand gets cited by ChatGPT or Google AI Overviews?
- No — no agency can guarantee citation in a specific AI answer, because the underlying models don't publish ranking criteria the way Google Search does. What we can do is implement the structural and entity signals correlated with higher citation likelihood and track the results.
- What's the difference between AEO and GEO?
- The terms are largely used interchangeably in practice — answer-engine optimisation (AEO) originally referred to featured-snippet and voice-search optimisation, while generative-engine optimisation (GEO) specifically targets LLM-based answer generation; both now overlap heavily in technique.
- Does traditional SEO still matter if AI Overviews are taking over search?
- Yes — AI Overviews and chat assistants are typically trained on and cite content that already ranks well organically, so traditional technical and content SEO remains the foundation AEO/GEO builds on top of, not a replacement for it.
- What is llms.txt and do I need one?
- It's an emerging, voluntary text file (similar in concept to robots.txt) that tells AI crawlers which content on your site is authoritative and how to reference it — adoption among AI providers is still inconsistent, so treat it as a low-cost addition rather than a primary strategy.
- How do you track whether we're actually being cited by AI tools?
- Through a combination of platform-native tools like Semrush's AI Search tracking and manual/automated prompt testing against a defined set of target queries, checked on a recurring monthly cadence.
- Will optimising for AI answers cannibalise our organic click-through rate?
- It can, in the same way featured snippets sometimes reduced clicks to the ranking page — the trade-off is brand visibility and citation-driven trust even without a click, which is why we track share of voice and branded search lift alongside raw traffic.
Ready to put this into practice?
The guides above document the philosophy. The AI Search Optimization (AEO / GEO) service page documents the engagement — scope, deliverables, cadence, and pricing.