AI Search Optimization and SEO for Apps and SaaS
We run SEO and AI search optimization for app and SaaS companies. That means technical SEO, content that answers the questions buyers ask, structured data, and the brand mentions that get you cited in Google AI Overviews, ChatGPT search, Perplexity and Gemini. You get a site that ranks, and a brand that gets named when buyers ask an AI.
What you get with SEO and AI search from ApsteQ
Search is now three layers: classic Google results, AI answers on top of them, and standalone assistants such as ChatGPT and Perplexity. One piece of work serves all three, so we plan it as one system.
Technical SEO fixes
Crawl and index audit, sitemap, canonicals, internal links, page speed, and a robots.txt that lets search and AI crawlers in. We hand your developers a ranked fix list, or we ship the fixes when we build your site.
Question-led content
A topic map built from the questions buyers ask, then pages that answer each one in the first paragraph, with sourced facts and a clear next step. Comparison, pricing and how-to pages come first because they sit closest to a decision.
Structured data
Organization, Article, Service, SoftwareApplication and BreadcrumbList markup that matches what the page shows, validated before launch. It helps machines read the page. It is not a ranking trick.
AI crawler access and llms.txt
Bot-by-bot robots.txt rules for OAI-SearchBot, PerplexityBot and Google, a clean sitemap, and an llms.txt file with your key facts and canonical pages.
Entity and mention building
A consistent brand description across your site, store listings, profiles and directories, plus a plan for the review sites, communities and publications AI answers tend to quote.
YouTube and Reddit presence
Video answers for the questions you want to own, and honest, useful participation in the subreddits your buyers read. We write the plan and the drafts; you or your team post under real accounts.
How an SEO and AI search engagement runs
We work in a fixed order, because fixing content before the site can be crawled wastes the writing.
Baseline audit
We check crawl and index status, Search Console data, your top landing pages, and what ChatGPT, Perplexity, Gemini and Google AI Overviews say today for 30 to 50 buyer questions in your category. You see who gets named instead of you.
Fix the foundation
Crawl rules, indexing problems, sitemap, structured data and page speed. These are one-off fixes with the fastest payback.
Map the questions
We group buyer questions by stage (what is it, which one, how much, how do I switch) and pick the ones where you can win.
Publish answer pages
Each page opens with a direct answer, adds sourced detail, and links to the next step. We refresh the pages that already rank before adding new ones.
Build mentions
Video answers on YouTube, useful replies on Reddit, listings on the review and comparison sites in your category, and a consistent entity description everywhere.
Measure and repeat
Each month we re-run the same question set across the AI engines, check impressions and clicks in Search Console, and track sign-ups or installs that start from organic and AI referrals.
What is AI search optimization, and how is it different from SEO?
AI search optimization, also called generative engine optimization (GEO) or answer engine optimization (AEO), is the work of making your content easy for AI systems to find, understand and quote. Classic SEO aims to rank a page in a list of links. AI search optimization aims to get your brand named or linked inside a generated answer. The two overlap heavily: both need a crawlable site, pages that answer real questions, and signals that you are a trusted source. The difference is the unit of competition. In classic search it is the page. In an AI answer it is a passage, plus the brand attached to it. So the practical work adds three things to SEO: self-contained answer passages, consistent brand facts across the web, and a presence on the sites AI systems like to cite. We treat SEO as the foundation and AI search as the layer on top.
The definitions are covered in our glossary entries on answer engine optimization and generative engine optimization. In practice we do not run them as separate projects, because the same page usually has to serve both.
The shift matters because user behavior is changing. Pew Research Center tracked the browsing of 900 U.S. adults and found that users clicked a traditional result in 8% of visits when an AI summary appeared, against 15% when it did not. When the answer is shown on the results page, being inside that answer becomes part of the job.
Academic work points the same way. The Princeton-led paper that named the field reports that GEO methods can boost visibility by up to 40% in generative engine responses in its test setup. It is a research benchmark, not a promise for any one site, but it supports the direction: how content is written changes whether it gets used.
How do you get cited by ChatGPT?
To get cited by ChatGPT search, let OpenAI's search crawler reach your site, then publish pages worth quoting. OpenAI says OAI-SearchBot is the crawler that surfaces websites in ChatGPT's search features, so allow it in robots.txt and keep the pages you want cited indexable. After that, ChatGPT chooses sources the way a careful researcher would: pages that answer the question directly, state facts clearly, and come from a brand that other sites also mention. Open each page with a short, complete answer. Back claims with named sources. Keep your company facts identical across your site, store listings and profiles. No one can guarantee a citation, and anyone who promises one is guessing, because answers vary between runs and between users. What you can control is access, clarity and reputation, and those three are where we spend the time.
On access, OpenAI documents three separate bots. OAI-SearchBot is used to surface websites in ChatGPT search, GPTBot relates to training, and ChatGPT-User acts on a person's request. Each robots.txt setting is independent, so you can allow search visibility and still opt out of training if you want. The same page says it can take about 24 hours for a robots.txt change to take effect for search.
The mistake we see most often is a blanket block on all AI bots, added after a security scare. It also removes you from ChatGPT search. Decide per bot, write it down, and review it each quarter.
Perplexity follows the same logic. PerplexityBot is designed to surface and link websites in Perplexity search results, and Perplexity recommends allowing it in robots.txt to appear there.
How do you show up in Google AI Overviews and AI Mode?
You show up in Google AI Overviews and AI Mode by being a page Google can already index and show with a snippet. Google's own documentation says there are no additional requirements and no special optimizations for these features, and no special schema.org markup to add. That is good news, because ordinary SEO done well is the entry ticket, with no extra tricks to learn. Make sure Googlebot can crawl the page, that it is not blocked by noindex or a restrictive snippet setting, and that internal links point to it. Then cover the topic completely, because Google's AI features run several related searches across subtopics to build an answer, and a page that covers the sub-questions has more ways to be picked. We use structured data to describe the page accurately, never to chase a feature.
These points come straight from Google's AI features documentation, which describes the process as issuing multiple related searches across subtopics and data sources. In practice that rewards topic clusters: one strong page per question, linked to its neighbors, rather than one long page that touches everything.
On structured data, Google's rule is that you should not mark up content that is not visible to readers, and the markup must be a true representation of the page. One update is worth knowing: Google has announced that FAQ rich results no longer appear in Search. We still mark up visible FAQs because other systems read them, but we no longer promise a rich result.
Which search surface uses which signals?
Each engine has its own crawler and its own way of choosing sources. This table shows what you control on each, based on the vendors' own documentation.
| Surface | Crawler or access control | What we do |
|---|---|---|
| Google Search | Googlebot, noindex and snippet controls | Technical SEO, topic clusters, internal links, page experience |
| Google AI Overviews and AI Mode | Same as Search. The page must be indexed and snippet-eligible. | Complete topic coverage, clear answer passages, accurate structured data |
| ChatGPT search | OAI-SearchBot (search), GPTBot (training), ChatGPT-User (user requests) | Allow OAI-SearchBot, answer-first pages, brand facts consistent across the web |
| Perplexity | PerplexityBot (search), Perplexity-User (user requests) | Allow PerplexityBot, sourced pages with named references |
| Gemini | Google systems, so Search access rules apply | Same foundation as Google Search, plus YouTube video answers |
| Off-site sources AI quotes | Not a crawler setting. Earned through reputation. | Review and comparison listings, YouTube, Reddit, press and directory mentions |
What does SEO for SaaS and apps actually involve?
SEO for SaaS and apps means getting the right buyers to find your product pages, comparison pages and answers in search, then turning that traffic into sign-ups or installs. It starts with technical health, so Google can crawl and index every page that matters. It continues with a keyword and question map tied to buying stages, not to volume alone. The pages that earn the most are usually the ones closest to a decision: alternatives, comparisons, pricing explainers, integrations and use-case pages. Blog posts that answer top-of-funnel questions feed those pages through internal links. For apps there is a second surface, the store listing, which we cover in App Store Optimization. The measure that counts is not rankings or traffic. It is how many sign-ups, installs and paying users start from organic and AI referrals.
Publish the decision pages first and let the blog support them. If you are an app, connect the web side to the store side: the same keywords should shape your site and your App Store listing. Our guides on app marketing for SaaS and app content marketing go deeper on the content side.
Do llms.txt files and schema markup help AI visibility?
Both are cheap to add and low risk, but neither is a switch that makes AI systems cite you. An llms.txt file is a proposed standard: a Markdown file at your site root that gives AI agents a short summary of your site and links to your key pages. Its own site describes it as a proposal, and it does not say search engines have adopted it. Google says no special schema.org markup is needed for its AI features. So treat both as hygiene, not strategy. We add llms.txt because it takes an hour and helps agents that read it on demand. We add structured data because it removes ambiguity about who you are and what a page is. The real gains come from content, crawl access and reputation, which is where we put most of the effort.
The format is documented at llmstxt.org, which was proposed by Jeremy Howard. It asks for an H1 with the site name, a short summary, and sections of links. We keep ours factual: what the company does, the pages that state it, and the data we are willing to be quoted on.
We practise this on apsteq.com itself. Our articles cite their sources, our guides include video explanations, and we publish an llms.txt. We do not claim a traffic number from it here, because the honest way to judge it is the way we judge client work: by running the same question set against the AI engines over time.
How much does SEO and AI search optimization cost?
The cost of SEO and AI search optimization depends on five things: how many pages need work, the state of your technical foundation, how competitive your category is, how much content has to be created rather than refreshed, and whether we also handle off-site work such as video and community presence. A small SaaS with a clean site and a clear niche needs far less than an app with thousands of pages and a crowded category. We do not publish a rate card, because a number without your audit behind it would be a guess, and the same site can need very different amounts of work. After the baseline audit we scope a fixed monthly plan against the work that will move your numbers first. SEO compounds, so we plan in quarters and review results against sign-ups and installs, not against traffic alone.
| Cost driver | Pushes effort up when | Pushes effort down when |
|---|---|---|
| Technical state | Pages are blocked, duplicated or slow to render | Site is already clean and indexed |
| Content gap | Few pages answer buyer questions | Good pages exist and need refreshing |
| Category competition | Large incumbents own the head terms | You can win specific, high-intent questions |
| Markets and languages | Several countries or languages | One market, one language |
| Off-site work | Video, community and listings are needed | You already have strong mentions |
| Speed to results | You need fast movement on many topics | You can phase work over quarters |
What we see across 300+ apps
Over the years we have worked on 300+ apps and brands, and the same patterns repeat in how they get found.
Most visibility problems are access problems. Before any content work, we usually find a page that is not indexed, a canonical pointing the wrong way, or a robots rule written years ago that blocks a crawler the company now wants. These are boring and fast to fix.
Founders write about themselves; buyers search for their problem. The pages that earn traffic are named after the question or the comparison, not the feature. A page titled with the buyer's words beats a page titled with the product's.
AI answers favor brands that are described the same way everywhere. When the site, the store listing, the LinkedIn page and the directory entry each say something different, the story is hard for a machine to repeat. Matching them is cheap and underrated.
Video and community carry more weight than most teams expect. A clear YouTube answer or an honest Reddit reply is often what an assistant ends up quoting, and it also reaches people directly. It only works when it is useful, so we never script fake threads or fake accounts.
Proof from our work
ApsteQ was founded in 2015. We have worked with 300+ apps and brands, driven 52M+ installs and managed $2.5M+ in ad spend.
EarningsCall: a finance app that was hard to find
EarningsCall had been live for two and a half years with almost no one finding it. In the foundation stage we rebuilt its store listings around the words investors actually search and set up search-friendly content on the website, so the app started appearing for earnings and investing searches instead of only its own name. In the campaign period it reached 3K+ monthly active users on 5K+ downloads, with 60% of downloads active monthly. That engagement also included paid and social work, so the result is not from search alone.
Read the EarningsCall case study →Frequently asked questions
What is the difference between GEO, AEO and SEO?
SEO is the base: making pages crawlable, relevant and trusted so they rank. AEO and GEO are names for the extra work of getting quoted inside AI answers. The terms overlap and vendors use them differently. We plan them as one program because the same page has to serve classic results and AI answers.
How long does SEO and AI search optimization take to work?
Technical fixes can show up within weeks once pages are recrawled. Content and authority take longer, so we plan in quarters. A robots.txt change for ChatGPT search can take about 24 hours to be picked up, according to OpenAI, but being cited depends on much more than access.
Can you guarantee we will be cited by ChatGPT or Gemini?
No. Nobody outside those companies controls what an AI engine cites, and answers vary between runs. We control access, page quality, structured data and reputation signals, then measure how often you appear for a fixed set of buyer questions and report the trend each month.
Do we need a separate strategy for apps and for SaaS?
The method is the same, the pages differ. SaaS leans on comparison, integration and use-case pages. Apps add the store listing as a second search surface, which we connect to the website through shared keywords. We cover the store side in our App Store Optimization service.
Should we block AI crawlers?
It depends on the bot. Blocking search crawlers such as OAI-SearchBot or PerplexityBot removes you from those answers. Blocking training crawlers such as GPTBot is a separate choice. OpenAI treats each setting as independent, so we set the rules bot by bot to match your goals.
What do you need from us to start?
Read access to Google Search Console and analytics, a list of your main competitors, and access to your site or a developer contact. We start with the baseline audit, and you see the gaps before you commit to a plan.
Related reading and tools
App Store Optimization
The other half of Get Found: keywords, metadata and conversion in the stores.
Get Found hub
How store search, web search and AI search fit together.
Answer engine optimization services
Answer engine optimization tools
Generative engine optimization tools
App marketing for SaaS
App content marketing
Free tool: ASO Score
See what AI says about your app today
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