AI visibility optimization and generative engine optimization (GEO) for brands winning AI citation alongside SEO.

The category is young, so the better question is what an AI visibility optimization agency should actually do. The work breaks into four pieces.

• Audit citation share across the six AI engines that matter (ChatGPT, Claude, Gemini, Perplexity, Bing Copilot, Google AI Overviews).
• Implement schema with cross-referenced @id @graph, llms.txt, and bot-tiered robots.txt.
• Build sources-first content tuned for AI extraction.
• Track weekly across engines, not just rankings.

leapbuzz runs this stack out of Singapore and serves clients across Singapore, Malaysia, Australia, the United States, and Canada. The 17.2 percent of brands that have meaningful AI search visibility today (AthenaHQ State of AI Search 2026) are the ones building this capability now.

Generative Engine Optimization is the work of making a brand citable by generative AI systems. The discipline replaces traditional SEO when the user reads the AI answer instead of clicking through.

GEO has four pillars:

• Entity hygiene: Organization schema, Wikidata, Crunchbase, named-publisher mentions
• Structured data: Article, Service, FAQPage, HowTo, BreadcrumbList with @graph cross-referenced @id
• Sources-first content: statistic density, expert quotation, primary citation, self-contained chunks
• Bot access: robots.txt that allows OAI-SearchBot, PerplexityBot, Claude-Web, Gemini

The Princeton-led GEO study (arXiv:2311.09735) measured visibility lifts of up to 40 percent in generative engine responses, with inline source citation, named expert quotation, and sourced statistics the strongest levers across domains.

GEO is end-to-end optimisation for the AI engines that generate answers. AEO is the subset that focuses specifically on answer-format content: FAQ blocks, How-To structures, definitional content, and the question-pattern matching that AI engines extract.

AEO is one of the pillars of GEO. In practice, leapbuzz runs both as a single engagement because the work overlaps and the measurement is shared.

Test directly. Open each engine and run the buyer-intent queries that should mention you. Note which engines cite your brand, which cite competitors, and which return generic answers.

For systematic tracking, use a multi-engine citation monitor:

• Profound: enterprise scale, 10+ engines, Conversation Explorer over 400M prompts
• AthenaHQ: mid-market, 8 engines, Action Center workflow
• Otterly.AI: SMB-friendly, 6 platforms, 2025 Gartner Cool Vendor
• Peec AI, Scrunch AI, Bluefish: mid-market and enterprise alternatives

Run a 30-prompt set weekly and watch citation share, sentiment, and competitor share-of-voice. leapbuzz runs this measurement as a subscription service alongside the GEO implementation work. We pick monitoring tools per engagement, not by default, and we never charge a markup on tool subscriptions.

Ahrefs studied 75,000 brands in March 2026 and found that brand mentions on the web correlate with AI Overview visibility at 0.664. Backlinks correlate at 0.218.

The implication is straightforward. AI engines reward the brand being named in trustworthy content across the web, not just the brand collecting links. Earning a named mention in a named publication is worth roughly three times the AI visibility value of earning a backlink without a brand mention.

This reframes digital PR for the AI era. The brief should be name-and-quote-our-spokesperson, not just include-our-link.

Citations move before impressions. The Lago case study published by AthenaHQ between March and September 2025 showed AI Overview impressions growing eleven times in six months on a sources-first content strategy, but the citation count moved first.

Practically:

• 4 to 8 weeks: citation changes on top-priority queries once the entity graph and schema layer are in place
• 8 to 16 weeks: brand-mention work compounds
• 1 to 2 weeks: schema architecture and llms.txt clean implementation; value accrues over 60 to 90 days as crawlers refresh

On payback, think in two horizons. The technical layer (schema, llms.txt, robots re-tier, entity claims) delivers a step-change in eligibility inside one quarter. The sustained primary-cited content programme delivers the share gain over 9 to 18 months as named publishers compound your authority signal. Do not confuse the two.

That is a choice, not a rule. The cleaner separation is to block training crawlers (GPTBot, Google-Extended, CCBot) if you do not want your content used to train competing AI products, while keeping inference and citation bots fully open (OAI-SearchBot, PerplexityBot, Claude-Web, Gemini, ChatGPT-User).

The mistake we audit out of most accounts: a single Disallow blocking all bots, including the inference crawlers that decide whether ChatGPT cites you. That mistake costs more AI citations than a year of GEO work can earn back.

Two to three weeks of senior specialist work. The deliverable is a written findings document plus a 90-day execution plan.

Coverage:

• Baseline citation share across the six AI engines on a 30-prompt set
• Schema audit with @graph and @id validation
• Robots.txt training-versus-inference bot tier analysis
• llms.txt presence and content review
• Entity graph audit (Wikidata, Crunchbase, LinkedIn Company, Google Business Profile sameAs ladder)
• Brand-mention audit across the open web
• Content extractability audit (BLUF, statistic density, expert quotation, chunk structure, FAQ schema)
• Competitor share-of-voice analysis
• Prioritised 90-day execution plan with weekly milestones

The findings document is yours regardless of what happens next.

Every engagement is scoped to the data, industry, and market. We don't publish standard rates because no two engagements are the same. Talk to us about your specific challenge and we'll come back with a scoped proposal.

Yes, with compliance discipline.

• 94 percent of B2B buyers now use AI during the buying process (Forrester 2026 State of Business Buying)
• 80 percent of search users rely on AI summaries at least 40 percent of the time (Bain 2025 AI search behavior research)
• MAS Digital Advertising Guidelines apply to financial promotions including AI-generated and AI-disseminated content (MAS)
• NAIC Model Bulletin on AI Use by Insurers, adopted by 30+ US states
• ASIC RG 234 in Australia, Quebec Law 25 in Canada. all govern how the brand can show up in AI answers

The work needs to be done with compliance built in, not as an afterthought.

Frame it as eligibility spend, not channel spend. Three reads.

1. Where your buyers research: if unbranded buyer-research queries in the AI engines do not cite you, you are absent from a funnel stage that paid media cannot buy back.
2. Cost asymmetry: the technical layer is weeks of one-off work; staying uncited compounds against you every quarter a competitor is the answer instead.
3. Measurability: a 30-prompt citation-share baseline gives the board a hard number inside two weeks. If the baseline comes back healthy, you do not need the budget line. That is a cheap question to answer.

Two reads.

1. Buyer-research signal: if your category buyer is using AI engines to research vendors before they hit your paid funnel (B2B SaaS, professional services, regulated insurance, fintech), the next dollar splits toward visibility optimisation. The paid funnel converts buyers who already chose to be in it; visibility optimisation determines who chooses to enter.
2. Branded vs unbranded: if your branded search is healthy but unbranded buyer-research queries do not cite you in any AI engine, the next dollar goes to visibility optimisation. Paid media will not fix that gap.

The audit reads which side of the line you sit on.

Three numbers, not a dashboard.

1. Citation share across the five engines (Perplexity, ChatGPT, Gemini, Claude, Google AI Overviews) for your priority unbranded query set. Quarter-over-quarter delta.
2. Competitor share-of-voice on the same query set. Names of the competitors who are being cited when you are not.
3. Pipeline tie: assisted-conversion attribution from organic-AI traffic, treated as the upstream signal for downstream branded paid efficiency.

That is the board reporting format. Leave the engine-level diagnostics out of the boardroom; bring them to the working session.

Six items, sequenced.

1. Entity hygiene: Organization schema with sameAs across Wikidata, Crunchbase, LinkedIn Company. Settled at least 30 days before launch day so the AI engines have indexed it.
2. llms.txt and robots.txt at root, with inference-bot allow list explicit.
3. FAQPage and Article schema on every product page with @graph cross-referenced @id.
4. Primary-cited launch content: announcement post with statistic density, expert quotation, primary citation. Indexed at least 14 days before launch day.
5. Citation-tracking baseline: poll the five AI engines on the launch-relevant unbranded queries 14 days before launch day to lock in the pre-launch competitive position.
6. Press release with structured-data integration distributed at least 7 days before launch day so AI engines have crawled the citation.