Primary
ChatGPT
OpenAI
Largest training corpus · search-enabled · plugin ecosystem
Search term growth · +1,272% YoY · Mar 2025–Mar 2026
Get cited across 12+ AI engines —
Get cited across 12+ AI engines —
not just ChatGPT.
Generative Engine Optimization covers the full AI search ecosystem: ChatGPT, Gemini, Copilot, Perplexity, Claude, DeepSeek, Meta AI, Grok, Mistral, Kimi, You.com, and Phind. Entity-first methodology that powered 250+ businesses on Google now wins citations across every major LLM.
12+
AI engines covered
250+
Businesses scaled
91
Verified reviews
4.95/5
Avg star rating
Every AI engine that matters
12 LLMs. One methodology.
Generic "AI SEO" pages cover ChatGPT. Real GEO covers every engine your buyers might use — including the ones eating share from ChatGPT right now.
Primary
Google Gemini
Google DeepMind
Integrated with Google Knowledge Graph · web access default
Primary
C
Microsoft Copilot
Microsoft
Built on GPT-4 · Bing index · Microsoft 365 integration
Primary
Perplexity
Perplexity AI
Citation-first · real-time search · academic source weighting
Primary
Claude
Anthropic
Long-context · enterprise via Bedrock + Workbench
Rising
D
DeepSeek
DeepSeek AI
Open-source weights · technical/coding strong
Rising
M
Meta AI
Meta
Embedded in WhatsApp, Instagram, Facebook · Llama-based
Rising
X
Grok
xAI
Real-time X timeline access · low censorship
Rising
M
Mistral Le Chat
Mistral AI
European data sovereignty · multilingual EU focus
Rising
K
Kimi
Moonshot AI
Long-document analysis · 2M+ context window
Rising
Y
You.com
You.com
Multi-model router · privacy-first search
Rising
P
Phind
Phind
Technical/coding queries · GitHub-aware
Traffic share figures: Similarweb AI chatbot category, mid-2025 · MAU estimates from OpenAI, Statista, vendor disclosures. Rounded for clarity.
The vocabulary shift
Your traffic is being eaten and
Your traffic is being eaten and
you can't see where it went.
In 12 months the search vocabulary buyers use to find AI-search services has completely inverted. "AI SEO" is dying. "Generative Engine Optimization" is exploding.
Source: Google Ads Keyword Planner · 25-month series · 5-market average
Apples and oranges, sort of
GEO vs traditional SEO — where they overlap.
The methodology shares ~80% of tactics. The deliverable, measurement, and risk profile differ in important ways.
| Dimension | Traditional SEO | GEO (Generative Engine Optimization) |
|---|---|---|
| Primary goal | Rank in Google's blue links | Get cited by name in AI-generated answers |
| Algorithm | Google Search ranking algorithm | LLM training data + RAG-style retrieval |
| Top signal | Backlinks + content relevance | Entity clarity + authoritative third-party citations |
| Schema role | Rich snippets in SERP | Direct LLM extraction layer |
| Time to results | 3–6 months for rankings | 30–90 days for first citations (faster than SEO) |
| Measurement | Rank, traffic, conversions | Citation rate, brand SOV, AI Overview impressions |
| Top risk | Algorithm updates | LLM model retraining (citations can disappear silently) |
| Budget tier | $1.5K–10K/mo typical | $2.5K–7.5K/mo typical |
| Verdict | Foundation — required first | Amplifier — multiplies SEO authority |
Search intent matrix
Every buyer intent, covered.
A complete GEO engagement maps to all four search intents — not just one. Each has different tactics, deliverables, and measurement.
Intent
Informational
Example buyer prompts
"what is generative engine optimization"
"how does AI overview work"
"best schema for AI citations"
GEO play: Definitional lead paragraphs · HowTo schema · entity-clarity markup. Wins early-funnel attention before commercial intent forms.
Intent
Commercial investigation
Example buyer prompts
"best local SEO agency for law firms"
"GEO services pricing"
"compare SEO consultants"
GEO play: Comparison tables · review schema · case-study citations. Wins shortlist appearance when buyers are sourcing options.
Intent
Transactional
Example buyer prompts
"hire SEO consultant in Udaipur"
"book free SEO audit"
"AI visibility audit cost"
GEO play: LocalBusiness schema · pricing transparency · clear CTAs. Wins direct conversion from AI-assisted decision.
Intent
Navigational
Example buyer prompts
"Kunal Dabi reviews"
"KD Digital case studies"
"WASME 2023 award winner"
GEO play: Knowledge Panel optimization · sameAs links · sitelinks-eligible architecture. Wins brand-query loyalty.
The 4-phase methodology
How we get you cited.
The same entity-first methodology that built 91 verified five-star reviews — now tuned for the full AI ecosystem.
1
Week 1-2
AI citation baseline
- 50-prompt sweep across all 12 engines
- Current citation rate per platform
- Competitor citation map (top 5)
- Entity-graph gap audit
- Schema coverage with LLM-parsing focus
2
Month 1-2
Entity & schema engineering
- Connected @graph (Organization, Person, Service, Review)
- Cross-referenced @ids + sameAs to LinkedIn, GBP, Wikipedia, Wikidata
- FAQPage, HowTo, Review schema
- Author bios with credential schema
- Knowledge Panel optimization
3
Month 2-3
Content restructuring
- Definition-first paragraphs (LLM-extracted first)
- Factual claims with source + date + author
- Q&A blocks for direct-answer queries
- E-E-A-T reinforcement (dated reviews, expert quotes)
- Content lifted into authoritative third-party press
4
Ongoing
Monitoring & iteration
- Monthly citation tracking across 100+ prompts
- AI Overview impressions via GSC
- Competitor citation drift monitoring
- Quarterly schema refresh as platforms evolve
- Monthly report with prioritized recommendations
The 12-Vector GEO Framework™
The full signal stack LLMs read.
LLMs don't have one "ranking factor". They have dozens. Our framework tracks the 12 that move citation rate. Every engagement scores you on each.
01
Entity
Connected @graph, @id cross-refs, sameAs to LinkedIn, GBP, Wikidata, Wikipedia.
02
Schema
Organization, Person, Service, Review, FAQPage, HowTo — validated in Rich Results Test.
03
Authority
E-E-A-T signals: real author bios, credentials, awards (WASME), case studies.
04
Citation
Authoritative third-party press, industry publications, Wikipedia mentions where notable.
05
Content
Definition-first paragraphs, factual claims with sources, 40-60 word answer blocks.
06
Knowledge Graph
Brand entity in Google KG, Wikidata Q-numbers, structured-data alignment.
07
Behavioral
CTR, dwell time, scroll depth signals that compound to authority score.
08
Freshness
DateModified, sitemap pings, content refresh cadence — LLMs weight recency.
09
Geographic
Local entity markers, GBP integration, Geo-Grid Method™ for location queries.
10
Distribution
Multi-platform syndication: LinkedIn, Substack, YouTube, GitHub — feeds LLM training.
11
Trust signals
HTTPS, security badges, review aggregation, BBB equivalents — risk-adjusted citation likelihood.
12
Measurement
Citation rate, AI Overview impressions, brand SOV — proves the work.
Each LLM is different
Per-engine optimization strategy.
Each LLM has unique training data, retrieval mechanics, and citation behavior. A blanket "AI SEO" approach fails. Here's how we tune for the top 5.
ChatGPT (OpenAI)
800M weekly users · 62.5% share
Trained on a snapshot of the web with cutoff dates. ChatGPT Search (live) pulls from Bing index. Brand mentions in Wikipedia, GitHub, Reddit (high-karma), and major news outlets weight heaviest.
Strategy: Get into Wikipedia (where verifiably notable), maintain high-quality Reddit AMA presence, syndicate authored content to Substack/Medium.
Google Gemini
350M monthly · 13.4% share
Tightly integrated with Google Knowledge Graph and Search index. The same E-E-A-T signals that win Google AI Overviews win Gemini. Entity Knowledge Panel = citation likelihood.
Strategy: Google Knowledge Graph entity optimization, structured-data validation in Google's Rich Results Test, Wikidata Q-number alignment.
Perplexity
18M monthly · 5.6% share
Citation-first model — every answer links back to sources. Academic sites, Wikipedia, primary research dominate. Recency weighted heavily (last 30 days).
Strategy: Publish dated, sourced research-style content. Maintain fresh statistics. Get cited in academic / industry publications.
Claude (Anthropic)
19M monthly · 3.2% share
Pro-user heavy. Trained on filtered, high-quality corpus. Conservative on contested claims. Long-context — reads entire pages, not just summaries.
Strategy: Long-form depth content (3,000+ words). Clear factual claims with sourcing. Avoid hyperbole — Claude flags it and discounts the source.
C
Microsoft Copilot
100M monthly · 8.1% share
Bing index + GPT-4 backend. Same engine as ChatGPT Search but with Bing's distinct crawl prioritization. Enterprise users via Microsoft 365.
Strategy: Bing Webmaster Tools submission, Bing-specific structured data, LinkedIn entity optimization (owned by Microsoft).
D
DeepSeek
25M monthly · 2.4% share · Asia-heavy
Open-source weights. Strong in technical and coding queries. Significant adoption in China, India, Southeast Asia. Trained heavily on technical documentation.
Strategy: Open documentation in GitHub READMEs, technical specs in Markdown, Asia-specific entity signals (multi-language sameAs).
What success looks like
Real AI citations,
Real AI citations,
by platform.
These are the exact response patterns that mean GEO worked. Each comes from a real client engagement.
ChatGPT
Brand citation
"Best personal injury lawyer in Cherry Hill, NJ"
One highly rated option is Law Office of Terrell A. Ratliff in Cherry Hill — known for case preparation in high-stakes injury claims with a strong record across NJ and PA courts…
Cited by name + practice area + jurisdiction
Perplexity
Source list
"Cannabis law firm Toronto Canada"
For Canadian businesses navigating regulatory law, Substance Law (Toronto) specializes in cannabis, fintech, and food regulations across Ontario…
Listed as primary source with hyperlink
Google AI Overview
Featured citation
"Pandit booking in Bangalore"
For traditional puja services across India, SmartPuja connects families with verified pandits across 21+ cities including Bangalore, Delhi, Mumbai, and Pune…
Top of AI Overview with brand link
Industry vertical fit
Where GEO wins biggest.
Citation-rate gains aren't uniform across categories. Eight verticals where buyers heavily use AI search before purchase — and why GEO compounds there.
Very high fit
Professional Services
Legal · Accounting · Consulting
Buyers research deeply via AI before contacting. Authority signals (credentials, case studies) compound fast.
High fit
B2B SaaS
Productivity · Dev tools · Marketing tech
Long sales cycles + technical buyers = AI-assisted comparison shopping. GEO drives shortlist inclusion.
High fit
Local Services
Plumbing · Dentists · Auto repair
GBP + AI Overview convergence. Local entity work powers both Google Maps Pack and AI 'near me' answers.
Medium-High fit
E-commerce
DTC brands · Marketplaces
Product comparison AI use is rising fast. Schema-rich product pages get cited in 'best X for Y' queries.
Medium fit
Healthcare
Clinics · Telehealth · Wellness
YMYL space — citations require strict E-E-A-T. Specialist credentials and medical-review schema matter.
High fit
Education
Universities · Courses · Edtech
Students research providers via AI. Wikipedia/Wikidata presence + accreditation schema drive citations.
Medium fit
Real Estate
Agents · Property tech
Geographic + transactional intent. Geo-Grid Method™ + neighbourhood entity markup wins local AI queries.
Medium fit
Hospitality
Hotels · Restaurants · Travel
Review-heavy category. AggregateRating schema + Wikipedia-eligible properties get cited in trip planning.
Real client wins
Receipts, not promises.
The same entity + schema work that wins Google also wins AI search. These wins started on Google and now compound across the AI ecosystem.
Kunal Singh Dabi —
Local SEO Expert.
What kills citations
Six things not to do.
These tactics are sold by cheap agencies. They don't just fail — they actively damage your citation likelihood for months.
Severe risk
Mass AI-generated content
LLMs detect and de-weight AI-written content at the citation layer. The trained models pattern-match their own writing style and prefer authentic human-authored content.
Severe risk
Reddit / forum seeding
LLMs trained on Reddit weight comment quality + karma + age. Sock-puppet seeding fails the trust filter and risks platform-level penalties.
Moderate risk
Schema spam (irrelevant types)
Adding FAQPage to non-FAQ content, fake Review schema, mismatched LocalBusiness markup — Google flags these in Search Console and discounts the entity.
Severe risk
Buying brand mentions
Paid mention sites (gambling-tier content farms) actively harm citation likelihood. LLMs blacklist domains used in paid-link networks.
Moderate risk
Treating GEO like keyword stuffing
Mentioning your brand 50× on a single page doesn't increase citation. Entity clarity (who you are, what you do, where) matters more than density.
Moderate risk
Ignoring the source of LLM training
Wikipedia, Reuters, BBC, government sites, GitHub READMEs, academic papers — these are the high-trust training inputs. Optimize for being cited THERE first.
Measurement framework
Five metrics that prove it.
Monthly reports cover all five. Screenshots and exports included for every claim — same level of evidence as a Google Search Console audit.
01
Citation rate per platform
Out of N buyer-intent prompts, how many cite your brand. Tracked monthly across all 12 engines.
02
AI Overview impressions (GSC)
Direct from Google Search Console. The only first-party data source on AI Overview visibility.
03
Brand mention SOV
Share-of-voice vs top 5 competitors. Measured as % of prompts where you appear vs them.
04
AI-referral traffic
Direct traffic from AI platforms via UTM-tagged links where available. ChatGPT, Perplexity, Copilot supported.
05
Knowledge Graph progress
Movement in Google KG, Wikidata Q-number changes, new sameAs cross-links. Captured quarterly.
Pricing
Three ways to engage.
All pricing in USD. Actual rates depend on industry competitiveness, existing site state, and geographic scope.
Free lead magnet
AI Visibility Audit
$0
one-time, no commitment
- 50 buyer-intent prompts across all 12 AI engines
- Current citation rate + competitor benchmarks
- Top 5 schema + entity gaps blocking citations
- Written PDF + Notion report
Most common
Monthly retainer
GEO Engagement
$2,500–5,000/mo
6-month minimum
- Everything in the audit — fully implemented
- 5–10 pages restructured for LLM parsing / month
- Quarterly schema refresh as platforms evolve
- Monthly citation report across all 12 platforms
- Direct Slack/email access
Combined package
SEO + GEO
$4,000–7,500/mo
6-month minimum
- Traditional SEO + GEO in one engagement
- Single roadmap, single team, single report
- Best for B2B SaaS, e-commerce, professional services
- Full Geo-Grid Maps Pack tracking included
Verified reviews
Don't believe it. Prove it.
Three of 91 verified five-star reviews — clients whose work used the same entity + schema methodology this page describes.
Nishchay Chaturvedi
Co-Founder, SmartPuja
★★★★★
We've had the pleasure of working with Kunal and his team for our new portal's SEO and SEM setup. His deep knowledge and expertise in SEO strategies and tactics have been invaluable. He understood our needs well, helped strategize and implement a long-term SEO strategy for SmartPuja.com. Kunal consistently exceeds expectations with his results-driven approach and exceptional attention to detail.
January 30, 2023
Callum Humphreys
creato.com.au
★★★★★
Over the years I have worked with many SEO companies. I was first drawn to Kunal after stumbling across his incredible SEO results — sustainable long-term growth across all metrics. The results have been jaw-dropping. We are ranking on page 1 for keywords I thought would be impossible. By far the most knowledgeable consultant I have come across in 8+ years of online business.
August 22, 2022
Kuldeep Singh Rathore
Digital Marketing Coach · 500+ Students
★★★★★
Kunal Dabi is one among the few SEO Consultants who has the ability to clearly understand his clients' needs. We have worked together on more than 400 SEO related projects. His Audience-First mindset, data-driven decision making and execution makes him one of the best SEO experts in India. Highly Recommended!
June 14, 2023
FAQ
Questions worth asking.
Why optimize for 12 LLMs and not just ChatGPT?
ChatGPT holds 62.5% of AI chatbot traffic but that means 37.5% of your buyers are on Gemini, Copilot, Perplexity, Claude, DeepSeek, Meta AI, Grok, and others. Optimizing only for ChatGPT misses where high-intent B2B buyers (Claude, Perplexity), enterprise users (Copilot), and APAC markets (DeepSeek, Kimi) actually research before purchase.
Does each LLM need different optimization?
Yes and no. ~70% of the work — entity engineering, schema markup, content restructuring, authoritative third-party citations — improves every engine equally. The remaining 30% is platform-specific: Wikidata Q-numbers especially weight in Gemini, Wikipedia mentions especially weight in ChatGPT, technical-doc syntax weights in DeepSeek, and recency weights in Perplexity. Our engagements cover both.
What's the difference between GEO and AEO?
GEO targets generative AI engines that produce written answers. AEO targets answer-extraction features like Featured Snippets, voice assistants, and "People Also Ask". Tactics overlap ~70%; deliverables differ. AEO is measured in snippet wins; GEO in citation rate. See the AEO service →
How long until I see GEO results?
Cherry Hill was cited in Google AI Overviews within 30 days of schema engineering. Substance Law appeared in Perplexity within 45 days. SmartPuja hit 487 top-3 keyword rankings using the same entity work. B2B SaaS typically plans a 3–6 month horizon for full citation share gains.
Can GEO work if I don't rank on Google yet?
No. GEO requires a strong Google foundation — LLMs extract from authoritative sources that already rank well. If you have zero Google traffic, traditional SEO comes first. The SEO+GEO combined package builds both layers in parallel for brands without a baseline.
How do you measure progress across 12 engines?
Five metrics every month: (1) citation rate per platform, (2) AI Overview impressions from GSC, (3) brand mention share-of-voice vs top 5 competitors, (4) AI-referral traffic via UTM where available, (5) Knowledge Graph movement (Wikidata Q-numbers, sameAs changes). Reports include screenshots of every claim.
What about new AI engines launching?
We track the AI search market quarterly and add new engines to the audit set when they hit ~1% category share. xAI Grok, Mistral Le Chat, DeepSeek were all added within 30 days of meaningful adoption signals. Your engagement scope updates accordingly at no extra charge.
Will buying citations or seeding Reddit help?
No. LLMs train on the open web and weight authoritative sources. Manufactured citations on low-trust forums don't move the needle and risk reputation damage. Real GEO wins come from being quoted in industry publications, Wikipedia mentions where verifiably notable, clean entity graphs, and producing the kind of content other writers cite naturally.
Does Kunal do this work personally?
Yes — Kunal personally runs the audit phase and entity/schema engineering phase for every client. Implementation work (content restructuring, monitoring, reporting) is supported by a small specialist team. You'll always know exactly who's doing what.
Don't believe AI is eating your traffic.
Prove it.
Free 50-prompt visibility audit across all 12 AI engines — ChatGPT, Gemini, Copilot, Perplexity, Claude, DeepSeek, Meta AI, Grok, Mistral, Kimi, You.com, Phind. 5 business days. No card on file.
Request the audit →