LLMs like ChatGPT, Claude, Perplexity and Google's AI Overview are increasingly where buyers form first impressions of brands. Whether they cite you, how they describe you, and which competitors they recommend instead — all of it is now a measurable surface, and one you can influence with deliberate work.
This is a structured playbook of 20 actions across 5 layers that move the needle on AI visibility. It's grouped by the mechanism each action targets, not the channel, because the same lever often works across multiple platforms — and some platforms (Claude API, GPT-4 with browsing off) only respond to one specific layer.
How to use this: Sections are grouped by mechanism. Start with Section 1 — entity definition is foundational and everything else builds on it. If you're already cited but inconsistently, jump to Section 5 to diagnose why before adding more content.
1. Entity definition — tell models what you are
Foundational layer. Affects every platform and every mechanism. Before models can recommend you, they need to know what you are: what you do, who you serve, what category you compete in. This layer is about giving models clean, structured, verifiable facts to anchor that understanding.
Create or claim your Wikipedia article (High impact)
Wikipedia is one of the highest-weighted sources in LLM training corpora. A clear, factual article anchors how models categorise your brand for years.
- Write in neutral tone — not marketing copy
- Must meet Wikipedia's notability criteria (requires third-party coverage)
- Include what you do, who you serve, founding year, key products or services
- Link to Wikidata for structured data ingestion by LLMs
Populate your Wikidata entity (High impact)
Wikidata is a structured knowledge graph that LLMs explicitly pull from during training. It defines your brand's type, category and relationships.
- Add instance of (company / product), industry, founded date, website, country
- Link to Wikipedia, LinkedIn, Crunchbase, Companies House
- Keep all claims verifiable with cited references
- Add
sameAslinks to all your other verified web presences
Consistent NAP across the web (Medium impact)
Name, Address and Phone must match across all directories. Inconsistency confuses entity resolution in LLMs and weakens the connection between your mentions.
- Audit Google Business, Bing Places, Companies House, Crunchbase, G2, Trustpilot
- Use the exact same brand name format everywhere — no abbreviations or variants
- Add Schema.org Organisation markup on your homepage with
sameAsreferences
Schema.org structured markup (High impact)
JSON-LD schema helps both search engines and AI scrapers extract clean, structured facts about your brand, products and FAQs without having to parse messy HTML.
- Organisation schema on homepage: name, url, logo, sameAs, description
- Product schema on all product or service pages
- FAQPage schema for common questions about your category
- BreadcrumbList for site structure — helps scrapers understand hierarchy
2. Content structure for AI citation
Targets search-augmented platforms: Perplexity, Google AI Overview, ChatGPT with browsing. These systems run a live search at query time and stitch the top results into an answer. Your job is to make sure the page they pick up has a clean, citable answer in the first paragraph.
Answer the exact query in the first paragraph (High impact)
AI Overview and Perplexity pull heavily from opening paragraphs. Lead with the direct answer — save supporting context for later.
- Identify your 10–20 target AI queries — what would your buyer ask an LLM?
- Build a dedicated page or section that opens with a direct, citable answer
- Use the query's natural phrasing within the first 50 words
- Avoid long intros, preambles or padding before the actual answer
Structured "what is X" and comparison pages (High impact)
Definitional content is heavily cited. A strong "what is [your category]" page positions you as the authoritative source on the topic.
- Dedicated glossary or learn-hub pages for your core category terms
- "[Your brand] vs [competitor]" comparison pages — you control the framing
- "Best [category] tools" — include yourself in curated lists you author
- Clear H2 / H3 heading structure so scrapers extract clean sections
Featured snippet and PAA optimisation (High impact)
Google AI Overview heavily borrows from featured snippets and People Also Ask boxes. Winning these feeds directly into AI responses.
- Format paragraph answers at 40–60 words for paragraph-style snippets
- Use numbered lists for "how to" queries, tables for comparisons
- Map all PAA clusters around your category and answer each individually
- Use FAQPage schema to surface these in structured results
Keep content crawlable and clean (Medium impact)
AI scrapers struggle with JS-rendered content, paywalls and complex navigation. Clean, server-rendered HTML wins every time.
- Critical content must be server-side rendered — not dependent on client-side JS
- Don't block GPTBot, ClaudeBot or Google-Extended in
robots.txtunless intentional - Fast load times — scrapers time out on slow pages
- Clean H1 → H2 → H3 heading hierarchy throughout
3. Third-party authority signals
Influences trained weights and inference-time search alike. When an LLM was trained, it saw the open web; the sources it saw most often and most authoritatively are the ones it now treats as trusted. Press, Reddit, reviews and podcasts are all heavily weighted here.
Press coverage in high-authority publications (High impact)
TechCrunch, Forbes, BBC, FT — these are disproportionately weighted in training data and cited heavily at inference time.
- PR outreach targeting publications with domain authority 70+
- Newsjacking: comment on industry trends to get quoted as a subject expert
- Original research and data — journalists cite stats, which creates citation chains
- One mention in a major outlet outweighs dozens of niche blog posts
Reddit and forum presence (High impact)
Reddit is one of the most heavily weighted sources in recent LLM training runs. Organic mentions in relevant subreddits carry serious signal.
- Identify your key subreddits by category and buyer type
- Build genuine community presence — don't spam or self-promote
- Answer questions where your brand or category is already discussed
- Track mentions systematically
- AMAs and community Q&As build sustained mention density over time
Review-site presence (G2, Trustpilot, Capterra) (Medium impact)
Review aggregators are heavily cited when LLMs answer "what's the best X for Y" queries. Volume and recency both matter.
- Claim and complete your profile on all major relevant review platforms
- Actively solicit reviews post-purchase and post-onboarding
- Respond to all reviews — signals an actively managed brand
- Target category-specific aggregators, not just generic ones
Podcast and video transcript presence (Medium impact)
Transcripts from podcasts and YouTube are increasingly included in training data. Being discussed builds rich, contextual signal.
- Guest appearances on industry podcasts in your category
- Ensure your own podcast or video content has published transcripts
- Detailed YouTube descriptions and chapters aid AI scraping
- Conference talks get transcribed and redistributed — high-value exposure
4. Topical authority — own your category
Depth of coverage signals expertise. Models preferentially cite brands associated with a topic cluster. A single thin blog post on a topic won't move the needle; a comprehensive cluster of pillar plus supporting content will.
Build a topic cluster around your core category (High impact)
A pillar page supported by 10–20 in-depth articles signals topical authority. LLMs associate brands with topics they cover deeply.
- Map your category's full question landscape using PAA and keyword tools
- Create a pillar page that covers the category comprehensively
- Supporting articles answer specific sub-questions in depth
- Dense internal linking connects the cluster explicitly
Publish original data and research (High impact)
Original statistics get cited repeatedly across the web. When an LLM quotes a stat, it often traces back to the primary source — and the primary source gets the brand association.
- Annual industry reports or surveys with original findings
- Benchmark data derived from your own product usage
- Trend reports based on aggregate anonymised data
- Make stats easy to cite: clear headline numbers, clear source attribution
Consistent publishing cadence (Medium impact)
Recency matters for search-augmented models. Dormant content signals an inactive brand; consistent publishing keeps you in the crawl cycle.
- Quality over quantity — 2 strong posts per month beats 10 thin ones
- Update old posts with new data to refresh crawl timestamps
- Fast-follow commentary on industry developments keeps you topically current
Author E-E-A-T signals (Medium impact)
Experience, Expertise, Authoritativeness, Trust. Google's framework maps directly onto how LLMs weight content credibility.
- Named authors with credentials, bios and schema markup on each article
- Author schema linking to their other published work across the web
- Expert quotes and contributor pieces from known industry figures
- Cite your own original data in articles to establish primary source status
5. Measurement and iteration loop
Without measurement you're guessing. AI visibility moves slowly enough that without a baseline and a diagnostic loop you'll spend months on the wrong fixes. The actions here close that loop.
Track visibility across platforms regularly (High impact)
Baseline → change → measure impact. Without consistent tracking you can't attribute what worked.
- Run scans before and after each content push to isolate the variable
- Track by platform — a win on Perplexity is not the same as a win on Claude API
- Track by query type: definitional, comparison and recommendation queries respond differently
- Monitor competitor visibility alongside your own to identify relative gains
Diagnose the gap — why aren't you cited? (High impact)
Low visibility has distinct root causes. Knowing which one determines the correct fix — don't apply a generic solution to a specific problem.
- Not mentioned at all → entity definition problem. Start with Wikipedia and Wikidata.
- Mentioned but not cited → content structure problem. Add schema and clean up crawlability.
- Cited but with negative framing → reputation problem. Focus on review volume and PR.
- Good on Perplexity, poor on Claude → trained weights lag. Long-term fix: press and forums.
Monitor competitor citation patterns (Medium impact)
If a competitor is being cited where you aren't, reverse-engineering why reveals exactly what content or signals you're missing.
- Which queries trigger competitor mentions that don't trigger yours?
- What content do they have that you don't — definitional pages, comparison pages?
- Are they present on authoritative third-party sources you're missing?
- Use citation domain data to identify which sources are driving their visibility
Prioritise by platform mechanism (Medium impact)
Different platforms respond to completely different levers. A one-size-fits-all approach wastes effort on the wrong signals.
- Perplexity → SEO rank + citation-worthy content structure
- Google AI Overview → featured snippets + schema + E-E-A-T signals
- ChatGPT (browsing on) → Bing rank + structured, cleanly crawlable content
- Claude / GPT-4 API (no tools) → long-term: press, Reddit, Wikipedia presence
- Microsoft Copilot → Bing SEO + strong LinkedIn and Microsoft ecosystem presence
Where to start
If you read this and aren't sure where to spend your first month, the order that's worked best across the brands we've tracked is:
- Audit your entity layer first. Schema, Wikidata, NAP consistency. This is one or two weeks of focused work and it underpins everything else.
- Build your top 10 AI queries into dedicated pages. Direct-answer opening paragraphs, FAQPage schema, clean H2 structure. Two to four weeks.
- Set up a measurement baseline before the third push. Without it you'll never know if what you do next is working.
- Then go after the long-tail signals — press, Reddit, original data, topic clusters. These compound over six to twelve months.
If you want help running the AI Search Optimisation side of this end-to-end, that's exactly what we do at Receptive Media — from the schema audit through to ongoing visibility tracking. Get in touch and we'll scope it against your category.
