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SaaS: AI startups

Best SEO and GEO agency
for AI startups.

AI buyers shortlist tools by asking ChatGPT, Claude and Perplexity. Passionfruit makes sure those answers know what you do today, place you in the right category, and send signups you can trace.

AI tools are chosen by the models they compete with

  1. Your buyers are the heaviest AI users

    People evaluating an AI writing, coding or support tool already live in ChatGPT and Claude. They ask there first, and often never open a search results page.

  2. Labs keep shipping your feature

    A new release from OpenAI, Anthropic or Google can absorb a product category overnight. You need pages that explain why a dedicated tool still wins for a specific job.

  3. Category names are still being written

    “AI SDR”, “agentic support” and “AI notetaker” did not exist as searches a few years ago. Whoever defines the term in answers tends to own it.

Prompts that decide which AI tool gets tried

AI buyers ask in long, specific prompts, often naming a model or a stack. These are the shapes we track, per engine, for your category.

  • Is there an AI tool that drafts SOC 2 policies from our existing docs?
  • Best AI agent for Zendesk that does not hallucinate refund policies
  • Cursor vs. other AI coding tools for a large monorepo
  • Which AI notetaker works with Microsoft Teams and keeps data in the EU?
  • Can I just use ChatGPT for this, or do I need a dedicated tool?
  • Open-source alternative to a hosted AI voice agent

The AI tool listings to beat

Your rivals in the answer are not only other startups. These four kinds of listings compete for the same searches, and each has a gap we build into.

There’s An AI For That and Futurepedia

Their gap
Directory entries are a line or two of description, often months out of date.
Your opening
We build use-case pages that show exactly what your product does for each job, current to this week’s release, so they outrank the directory and give engines a better source.

“Best AI tools for X” listicles and G2 roundups

Their gap
Roundups cover twenty tools with a paragraph each.
Your opening
Your own category and comparison pages answer the buyer’s actual question in depth, which is what wins the ranking and the citation for that use case.

Incumbents’ “alternatives to” pages

Their gap
Bigger rivals publish one-sided comparisons that include you.
Your opening
We publish fair, specific comparisons for every matchup, kept current as both products ship, so buyers and engines get the accurate version from you.

ChatGPT, Claude and Gemini themselves

Their gap
General models often answer “do I need a tool for this?” with themselves.
Your opening
We build clear “when a general model is not enough” pages for your use case, with the specifics that make the case for a dedicated product.

See who AI recommends instead of you.

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Where models learn about AI tools

AI engines lean on a small set of technical and community sources for this category. Each one needs different work.

  1. Hacker News

    Show HN threads and comment discussions are cited for developer and infrastructure tools. We help founders post and answer in a way that suits the community, and track which threads engines pick up.

  2. r/LocalLLaMA, r/ChatGPT and r/SaaS

    These subreddits are where people compare models and tools in public. We monitor mentions, answer questions with disclosure, and flag threads where your product is misdescribed.

  3. GitHub and Hugging Face

    READMEs, model cards and repos are read by crawlers and cited for open-source and developer products. We make them clear about what the product does, who it is for and how it compares.

  4. Your docs and changelog

    Docs and changelogs are where the freshest facts live. We structure them so crawlers can read them and engines can quote current features, pricing and supported models.

  5. Newsletters and AI roundups

    Ben’s Bites, The Rundown and similar newsletters are syndicated and cited. We find the ones that cover your category and pitch genuine news, not filler.

Search problems only AI products have

Three issues we see in almost every AI startup’s search presence.

  1. 01Models describe you from stale training data

    A model may know your product as it was at its training cutoff: old pricing, missing features, the wrong category. We publish clear, dated facts on crawlable pages, update the third-party sources engines retrieve, and check each engine’s description after every release.

  2. 02Your category name is still moving

    The same product is searched as an “AI SDR”, “AI sales agent” and “autonomous outbound”. We map which terms buyers and engines actually use, pick the one to lead with, and cover the variants on supporting pages.

  3. 03Thin launch content outranks your own site

    Directory listings and launch posts often rank above a young product site for its own name plus “review” or “pricing”. We build the pricing, security, comparison and review pages that should be the answer, and make them the best-linked pages on the site.

Find out which of these is costing you most.

Book a call and we will rank them by the revenue at stake on your own site.

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The AI launch cycle and the trust checks

AI search moves on a calendar set by model releases and launches, and buyers check a specific set of trust facts before they sign up.

Timing

  1. Model releases

    Each major release from OpenAI, Anthropic or Google changes what buyers ask and which tools get recommended. We review your category prompts after every one.

  2. Your own launches

    A Product Hunt launch or major release is a short window to set how you are described. Pages, docs and pricing are ready before launch day, not after.

  3. Conference season

    Developer and AI conferences bring waves of new tools and coverage. We plan comparison and category content ahead of them so you are in the roundups.

Rules and checks

Security reports
Buyers ask engines whether a tool has a SOC 2 report or similar. If you have one, we make sure the security page says so clearly and engines can find it.
Data use and training policies
“Does this tool train on my data?” is one of the most common prompts in the category. A plain-language data policy page answers it before a competitor’s does.
Model and hosting choices
Which models you use and where data is processed are now buying criteria. We put those facts where engines and procurement teams can find them.

What we ship for AI products, area by area

AI tools are judged on what they can do this week, so every vertical is built to stay current with your releases.

Guides on the jobs your product does, such as transcribing sales calls or drafting support replies, written from real usage and updated with each release. Directory blurbs and launch posts go stale within weeks; content that shows current capability is what engines cite for “how do I do X with AI”.

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Why Passionfruit for AI startups

For an AI startup, generative engine optimization is not a side channel. Your buyers ask models for recommendations more than anyone else does, so the answer inside ChatGPT or Claude is often your real first impression. We work with AI companies to make that answer accurate, current and favorable.

Our platform runs your category prompts across every major engine on a schedule and keeps each full response. That shows which tools are recommended, which sources are cited, and where a model still describes a version of your product you shipped months ago. Experts then ship the pages, docs fixes, listings and community work that change it.

Because AI categories move with each model release, the program is reviewed after every major launch, yours or the labs’. You see signups and pipeline from AI referrals next to organic search, so it is clear which prompts are bringing users.

What we measure

  • Signups and activated users that arrive from ChatGPT, Claude and Perplexity referrals
  • How often each engine recommends you for category and “tool for this job” prompts
  • Gaps between how each engine describes your product and what you ship today
  • Pipeline from comparison and alternatives pages
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Questions

ChatGPT still describes our old pricing. Can that be fixed?

Usually, over time. Engines that browse retrieve current pages, so clear, dated pricing and feature pages help quickly. Descriptions that come from training data change as models are retrained, which is why we also update the third-party sources they learn from.

How do we compete when OpenAI or Anthropic ships our feature?

By being specific about the job. We build pages that explain what a dedicated tool does that a general model does not for your use case, such as integrations, accuracy controls or workflow, so engines have a reason to recommend you anyway.

Should we bother with AI directories like There’s An AI For That?

Keep an accurate listing, then outrank it. Directory entries are short and go stale, so use-case pages on your own site, current to your latest release, can beat them for the searches that matter and become what engines cite instead.

Our category does not have a name yet. What do we target?

The problems buyers describe. We map the terms buyers and engines use, choose the one you want to lead with, and build a category page and use-case pages around it.

Do you work with open-source and developer AI products?

Yes. For those, GitHub, Hugging Face, Hacker News and your docs carry as much weight as your marketing site, and they are part of the program.

More SaaS and B2B segments

Get found and chosen in AI answers.

Book a call and we will show you where Google and AI answers leave you out today, and what we would ship first to get you in.