Generative Engine Optimization for AI Startups
Buyers now ask ChatGPT, Perplexity, and Gemini for the shortlist, and the answer names two or three tools. GEO makes an AI startup one of those names, the recommended pick, not the option a model leaves out. Built on recommendation positioning, comparison content, and corpus consensus, tied to pipeline. No $5K/month retainer. No paid ads.
Free 30-min call with a quick generative visibility check of the brand. No obligation, no hard pitch.
The Problem
The buying question has moved inside the model. A founder still does the research, but now a model hands back a short, confident set of recommended tools and the decision narrows on the spot:
More content is not the fix. Becoming the recommended name is, so the brand shows up inside the shortlist the moment a buyer asks a model which tool to choose.
The Solution
Avinash runs GEO for AI startups and B2B SaaS around one idea: the recommendation is the new sale. A citation proves a model read the brand. A recommendation means the model puts the brand forward when a buyer inside ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews asks which tool to pick.
The work pairs sharp recommendation positioning with the comparison and best-X content where shortlists form, plus a consistent narrative across the third-party sources models already trust, from G2 and Reddit to listicles and docs. Built as one system by a founder who ships products, not an agency desk. That means direct access, fast execution, and every asset mapped to a real buying prompt and a clear path to signup.
What's Included
Run the real buying prompts across ChatGPT, Perplexity, Gemini, Claude, and AI Overviews, then score how often the brand lands in the recommended set versus a competitor that owns the slot today.
A clear recommendation-share baseline: the prompts where the brand wins, loses, or never appears, ranked by buying intent.
Pin down the specific use case, segment, and ICP the brand deserves to be recommended FOR, so models have a sharp, defensible reason to name it instead of a generic category leader.
Engines start recommending the brand for the exact job its best-fit buyers are asking about.
Own the high-intent prompts where shortlists actually form: best tools for a job, top alternatives to an incumbent, and head-to-head matchups buyers ask a model to settle.
The brand enters the recommended set at the precise moment a buyer asks which option to choose.
Build consistent, credible presence across the sources models read most, G2, Reddit, listicles, product docs, and directories, so a single coherent narrative surfaces wherever a model looks.
Recommendations compound from independent sources models trust far more than any owned page.
Ship clean Organization, Product, and SoftwareApplication schema plus consistent entity references, so models map the brand to the right category, capabilities, and competitors without guessing.
Engines stop confusing the brand with look-alikes and confidently slot it into the right recommendation.
Earn the third-party reviews, ratings, and community signals that tip a model from merely aware of a brand to actively willing to recommend it over alternatives.
Positive consensus accumulates in exactly the places that push a tool into the recommended set.
Monitor a named set of buying prompts across every major engine over time, watching how often the brand is recommended, in which position, and against which rivals.
Reporting tied to recommendation share and inbound, not a vanity wall of impressions nobody can bank.
Proof
This is some of the sharpest SEO execution I've seen. We're ranking on high-search-volume keywords earlier, with less competition than we expected.
Tibo
Founder, Revid AI
Avinash took us from near-zero to 340K+ impressions in six months at ~0.9% CTR — but the real win was that he treats SEO and narrative as one system. Our sales team felt it in inbound conversations, not just dashboards.
Alpesh Nakrani
Head of Sales & Marketing, ViitorCloud (EveryCRED)
He owned SEO like a product lead — scaled us to ~140K monthly impressions at ~1.8% CTR in three months, and kept improving messaging and performance the whole way. I'd work with him again without hesitation.
Vishal Rajpurohit
Founder, LaraCopilot
Organic became a real revenue lever — roughly ₹20–30 lakh in inbound B2B opportunity attributed to search and content. Qualified conversations, not vanity sessions.
Alpesh Nakrani
Head of Sales & Marketing, ViitorCloud (EveryTicket)
Trusted by founders across the US, UK, Canada, Europe, Australia, Singapore & the UAE.
How It Works
The call covers the product, the category, the competitors, and the exact buying prompts a target customer would type into a model. A quick generative visibility check comes prepared.
Auditing reveals which engines already recommend the brand, which name a rival instead, and why. The output is a prompt-by-prompt map ranked by buying intent and recommendation-share gap.
Recommendation positioning first, then comparison and best-X content, then corpus presence across the third-party sources models trust. Every asset maps to a prompt where the shortlist forms.
Ongoing recommendation-share tracking, content refreshes, and corpus expansion that grow the brand's share of the named set month over month. Reporting tied to inbound, not vanity counts.
Positioning and schema fixes ship in week one. Recommendation share compounds over the months that follow, the way generative consensus is actually earned.
Who It's For
A fit if
Probably not a fit if
FAQ
GEO (Generative Engine Optimization) is the work of becoming the tool that AI engines recommend by name inside the answer they generate. When a buyer asks ChatGPT or Perplexity which option to pick for a job, GEO is what puts the brand into the two or three names that come back. It shapes recommendation positioning, the comparison and best-X content where shortlists form, and the third-party corpus that models actually read before they answer.
AEO wins the citation: becoming the source an engine quotes and attributes. GEO wins the recommendation: being the named option inside the generated answer or shortlist. A brand can be cited without being recommended, and recommended without being the cited source, which is why the two run best as one connected motion. SEO, by contrast, optimizes for a ranked blue link on a results page that a buyer still has to click. See the AEO service page for the citation-side detail, linked below.
ChatGPT, Perplexity, Gemini, Claude, Microsoft Copilot, and Google AI Overviews. Each one blends a slightly different training corpus with live retrieval and rewards a slightly different signal, so the positioning, content, schema, and corpus work is tuned to the engines a brand's buyers actually open when they ask for a recommendation.
Positioning and schema fixes can change how a model reads and categorizes a brand within weeks. Meaningful recommendation share, the brand reliably appearing in the named set across target prompts, typically compounds over 2–4 months as comparison content ranks and third-party consensus accumulates. Honest timelines get set on the call, never inflated promises.
Yes. A named set of buying prompts is run across ChatGPT, Perplexity, Gemini, and AI Overviews on a schedule, tracking how often the brand is recommended, in which position, and against which competitors, alongside the inbound and pipeline that follow. Reporting stays anchored to recommendation share and qualified conversations rather than a wall of impressions.
Yes, globally, with founders and teams across the US, UK, Canada, Europe, Australia, Singapore, and the UAE. Avinash is based in Ahmedabad, India, and works async-friendly across time zones, so distance and working hours rarely get in the way of execution.
Scoped monthly engagements based on stage and goals, month to month with no long lock-in. Scope gets set together on the call, so the work that gets paid for is the work that moves recommendation share and pipeline.
Want the citation side too? See AEO for AI Startups, where the focus is becoming the source these engines quote.
Keep Exploring
Stop watching a competitor own the shortlist a model hands back. Book a call to build a GEO engine that wins the recommendation inside ChatGPT, Perplexity, and Gemini, and turns the named pick into signups.
Prefer to send details first? The inquiry form routes straight to a brand review before the call.
Free 30-min call · Quick generative visibility check included · No deck, no hard pitch.
