GEO for SaaS: The Practitioner’s Playbook

A SaaS buyer opens ChatGPT on a Tuesday night and types, “best tool for X workflow, under $200 a month, runs on AWS”. By the time your BDR calls them on Friday, the shortlist is locked. You never appeared in the answer. You never knew the conversation happened.

This is the uncomfortable reality I keep running into with SaaS marketing teams. According to EMGI’s own SaaS AI Citation Gap Report, 44% of SaaS brands that rank on page one of Google are completely invisible to ChatGPT on their most important commercial queries. Not ranked low. Not cited with caveats. Entirely absent. The buyer built a shortlist, and you were not on it.

I built EMGI because classic SEO, the link building craft, still prints money for SaaS, but the shape of the buyer journey has shifted upstream of the Google SERP. A chunk of the consideration layer has moved into large language models, and most of the industry is still arguing about whether this is real. I think the argument is over.

This guide is a practitioner’s playbook. No theory, no “ever-evolving landscape” filler. A seven-layer GEO model, live client data, one honest pushback on the “GEO is just SEO” line from the old guard, and a specific proof stack of SaaS brands I have helped make visible inside AI answers. By the end, you will know what to prioritise and what to ignore.

What is GEO for SaaS, actually?

GEO, or Generative Engine Optimisation, is the practice of making your brand findable, citable, and preferred inside AI-generated answers. For SaaS, this means ChatGPT, Google AI Overviews, Perplexity, Gemini, and Claude. Roughly 800 million weekly active ChatGPT users ran prompts in early 2026 (OpenAI, 2026). Your buyers are inside that number.

The reason the framing matters: SEO ranks a page inside a list of blue links. GEO gets your entity cited inside a generated answer. The unit of visibility changes. The measurement changes. The tactics overlap but do not collapse into the same thing.

The five AI surfaces that matter for SaaS in 2026

I tell clients to prioritise these in order, based on where their buyers actually go:

1. ChatGPT search. Highest-volume surface. Pulls from Bing index plus OpenAI’s trust-weighted sources. Buyers use it for shortlisting.
2. Google AI Overviews. Shows up above the classic SERP on commercial queries. Draws from the same authority pool as the top ten results.
3. Perplexity. Skewed technical and research-heavy. Developers, VPs of engineering, security buyers.
4. Gemini. Embedded inside Google Workspace. Rising fast inside enterprise.
5. Claude. Enterprise-skewed. Quieter surface, but arguably the best model for long-context B2B work.

Why “SEO 2.0” is a lazy framing

I keep hearing practitioners call GEO “just SEO with a shinier name”. It is not. The index is different (Bing, Google, Common Crawl mixed together). The ranking substrate is different (citation worthiness, not link graph weight alone). The user behaviour is different (one natural-language prompt, not a keyword plus scrolling). Treating it as a tactical extension underestimates the problem and overestimates your current visibility.

My working prediction: buyers will run multi-model workflows. They will open ChatGPT for the first shortlist, cross-check in Perplexity, then pass the final two or three options through Claude for a deeper read. Google does not disappear in that flow. It gets used as the verification layer, the place buyers confirm whether a cited brand is real, reviewed, and findable. That is why this is genuinely SEO 2.0, not SEO replaced. Google still matters because it is where the trust check happens. The SEO foundation extends into AI surfaces, it does not get replaced by them.

I will push back on this properly later in this article. For now, the practical definition I use with clients is Search Everywhere Optimisation. It is not a term we invented, but it is exactly why we prioritise this approach. Every surface where a buyer might ask a commercial question, optimised together, measured together. Not SEO, not GEO in isolation. Both, with the proportions set by where your specific buyer actually spends time.

Why are most SaaS brands invisible in AI search?

The 44% invisibility number from our SaaS AI Citation Gap Report surprised me when we first ran it. These are brands ranking on page one of Google for their target commercial terms. The SEO work was done. The authority was there. And yet on a ChatGPT prompt like “best project management tool for remote engineering teams”, they did not appear. At all.

Three structural reasons SaaS is more exposed than other categories.

Long consideration windows

Enterprise SaaS buying cycles commonly run 6 to 18 months. Mid-market is shorter, SMB shorter still, but the enterprise end of the market is the part that sets the shape. Buyers research upstream of the vendor website. If the first-touch question happens inside ChatGPT on a Tuesday night, and you are not cited, you do not enter the evaluation. By the time the buyer hits your homepage weeks later, they arrived because somebody else’s citation pulled them.

Gartner’s 2024 B2B buying research found that a typical purchase requires 14 or more buying touches before a decision is made (Gartner, 2024). AI lets you hack touch volume quickly. One buyer, one week, one research sprint, and you can generate many touches at once by appearing in AI answers across adjacent queries, controlling the Reddit narrative in the subreddit they check, and ranking for the target keywords they verify through Google. That is not three separate channels, it is one coordinated surface that stacks touches faster than outbound ever could.

Buying committees each use AI differently

In a typical SaaS purchase, you are not selling to a person, you are selling to a committee. The IC runs the first prompts, building a shortlist. The VP checks reputation signals and cross-references with Reddit. The CFO asks the model for risk flags. Finance asks about pricing. Each stakeholder runs different prompts on different surfaces. Winning in one does not cover the others.

Trust-critical categories over-index on AI shortlists

Fintech, healthtech, and developer tools all show heavier AI-first shortlisting in our data. These are categories where a wrong choice hurts, so buyers outsource the first filter to a model that “feels” unbiased. Here is the irony, and it is worth sitting with. People genuinely believe AI is more objective than a Google search. The reality is the opposite. A Google SERP shows you ten results and lets you pick. An LLM hands you three names inside a sentence and presents them as if the model reasoned its way to the answer. It did not. It surfaced whichever sources it has been trained to trust, weighted by citations, and wrapped the output in the tone of a confident human. Buyers who would scroll past an ad on Google now accept the first model output at face value. The perceived objectivity is higher, the actual objectivity is lower. Which brings us to the invisible shortlist problem.

The good news: this is solvable. Not overnight, not with one tactic, but systematically. The seven-layer model below is how I structure the work for our retainer clients.

The seven-layer GEO model for SaaS

Most GEO guides in circulation use six layers. I added a seventh (Distribution) after watching one specific pattern play out across EMGI’s client base: original research that lives quietly on a client blog does not get cited. The same research, amplified through podcasts, LinkedIn, guest posts, and expert quotes, gets picked up by LLMs inside weeks. Authority compounds when it is visible. Distribution is how that happens.

The layers, in working order:

1. Entity
2. Citation
3. Content
4. Technical
5. Freshness and searchability
6. Distribution
7. Measurement

Layer 1: Entity. Can an LLM tell who you are?

Entity recognition is the foundation. If an LLM cannot resolve your brand into a clear, disambiguated entity with a category, a product, a founding team, and a set of facts, it will not cite you. Wikidata, Crunchbase, LinkedIn Company pages, and Gartner Peer Insights are the canonical sources. In our CRM research (publishing soon), Gartner Peer Insights is the single strongest directory predictor of LLM visibility.

The entity sources most SaaS companies miss

• Wikidata. Not Wikipedia. Wikidata, the structured knowledge graph Wikipedia runs on. LLMs pull entity facts from it directly. Most SaaS brands do not have a clean Wikidata entry.
• Crunchbase. Funding, founders, product category. Often outdated. Update it.
• Gartner Peer Insights. Expensive to cultivate, disproportionate payoff. Our data shows it lifts ChatGPT citations more than any other single directory.
• G2, Capterra, Software Advice. Volume of reviews matters more than star rating. Apollo.io sits on 19 directories with 24,730 reviews and zero ChatGPT citations on CRM queries, because the category tagging says “sales engagement” and not “CRM”. Get the category right first.

Entity is a one-off sweep you do once and maintain quarterly. It is also where most SaaS teams leave money on the table. Our off-page SEO checklist covers the mechanics.

Layer 2: Citation. Who vouches for you in the sources LLMs trust?

Citation is where most of the SaaS GEO work happens. Semrush’s June 2025 analysis found that Reddit contributes roughly 40% of AI citation share across ChatGPT search answers (Semrush, 2025). That single stat should reshape how SaaS marketing teams think about earned media.

The citation layer is where link builders win by default. If you already have an earned-media practice, you are 80% of the way there. The other 20% is about which sources you target.

The sources LLMs over-cite for SaaS

• Reddit threads in relevant subreddits. r/SaaS, r/sales, r/devops, vertical-specific subs. Not spam, genuine practitioner answers.
• Niche industry publications. Often lower DR than you expect, but topical authority is high.
• High-DR SaaS review sites. G2, Software Reviews, Crozdesk, TrustRadius. Our data shows Software Reviews and Crozdesk punch above their weight.
• Expert podcasts and YouTube. Both are citation surfaces in their own right. ChatGPT and Gemini pull transcripts. A 40-minute podcast mention becomes a durable entity association that a backlink alone cannot match.
• Medium and similar open publication platforms. High domain authority, low editorial friction. LLMs trust them disproportionately given how easy they are to publish on.
• Parasite placements. Forbes, Entrepreneur, trade publications. Charles Floate has been publishing detailed LinkedIn breakdowns on how parasite SEO still works in 2026, despite Google’s repeated attempts to crack down on the tactic. His posts are worth reading end to end. The short version: a well-placed article on a parasite host still outranks and out-cites most owned content, because the model reads host authority, not author motive.

There is a second layer to this most SaaS teams miss. A parasite placement is not just a backlink. It is a brand mention, an entity association, and a perceived-authority signal stacked on top of the link value. An LLM reading Forbes does not just pass link equity back to your domain. It reads “X is the kind of company Forbes writes about”, and that semantic weight sticks. Treat every earned citation as three signals at once: link, entity, authority.

In our web scraping SaaS client engagement, we focused on DR 65+ sites as the anchor quality filter. Long-tail queries started ranking inside AIO after the link work, even though those queries were not the direct targets. That is the whole-site authority distribution effect. It is not a “one link equals one page ranks” story, it is a systemic authority lift that LLMs then read as a signal.

I wrote a full piece on why high-authority backlinks still matter for anyone wanting the deeper argument.

Layer 3: Content. Are your pages shaped for passage extraction?

LLMs cite passages, not pages. Your content has to be structured so that a 40 to 80 word chunk can be lifted cleanly into an answer with the citation pointing back at you. This is a writing craft problem, not a technical problem, and most SaaS teams over-engineer it.

The pattern I use on every piece of priority content:

1. Question as the heading (or a direct rephrasing of how a buyer would ask it).
2. Direct answer in the opening sentence, 20 words or fewer.
3. Evidence in the next sentence, ideally with a named data point and a source.
4. One concrete example, short, specific, SaaS-relevant.

If you look at this article, every H2 follows that shape. It is not accidental. It is how the passage-extraction game is played.

Original data is the shortcut for smaller brands

Here is the single most important tactical observation I have made in the last 12 months. LLMs love citing data. Specifically, they love citing data that no other source carries, on topics the model has been asked about. Our SaaS AI Citation Gap Report is the clearest example I have. Minimal distribution. No PR push. A small email list and two LinkedIn posts. It was picked up inside ChatGPT within a week of publishing. We are not Ahrefs. We are not Search Engine Journal. The semantic value landed because the data was original and useful.

The flywheel effect is what makes this valuable long after the first citation. The report has already brought us new backlinks from writers citing our stats inside their own articles. Those backlinks pass authority back to the domain, which lifts every other page, which increases the chance of further AI citations on unrelated prompts. One piece of original research compounds into ongoing authority, ongoing citations, and ongoing pipeline. This is the flywheel in practice. Not theory.

This is the opening for smaller SaaS brands. You cannot outspend HubSpot on authority. You can publish something HubSpot has not published.

Comparison pages are the highest-ROI content format

A disproportionate share of the citation wins we track sit on comparison queries (“X vs Y”, “alternative to X”). Build those pages, seed the anchor corpus on G2, Software Advice, and Slashdot, and you set up a flywheel where the model cites your own comparison page as a source for adjacent comparison queries. Semantic positioning is what makes this work. Linear gets cited as “the PLG-friendly alternative to Jira”. Basecamp gets cited as “the async-first option for distributed teams”. The phrase is not a tagline, it is a semantic handle the model latches onto because it solves the buyer’s query in three words. If your comparison content gives the model that handle, you get cited on adjacent prompts you never explicitly targeted.

Layer 4: Technical. What crawlers can reach you, and what structure do they see?

The technical layer has the smallest single-lift of the seven, but it is a floor you cannot skip. Get this wrong and everything else compounds less. Get it right and the work moves to citation, content, and distribution.

Honest caveat first. I have seen plenty of sites rank perfectly well with mediocre technical SEO. Core Web Vitals are important but rarely decisive. Schema helps but is not a silver bullet. The hygiene list below matters less than content and citations on most engagements. Two exceptions, both non-negotiable.

The technical stack that matters for GEO

• Schema. Organization, Product, FAQ, HowTo, BreadcrumbList. Priority order. The first three are non-negotiable.
• robots.txt posture. Explicitly allow GPTBot, ClaudeBot, PerplexityBot, Google-Extended unless you have a strategic reason to block. Most SaaS teams block by accident.
• JavaScript rendering. This is the one technical bar that actually kills you in AI search. LLM crawlers are meaningfully worse at JS than Googlebot. A lot of the “vibe-coded” websites I see built on modern JS frameworks have not been optimised for rendering at all, and their pricing, features, and comparison copy exist only in the client-side bundle. If a crawler cannot read the passage, it cannot cite the passage. Full stop. Server-render or pre-render the commercially important content or accept that you are invisible to most AI surfaces. I cannot overstate this.
• Internal linking. The other non-negotiable. The same principles that pass link equity pass entity context. LLMs map sites through your internal link graph, and the graph tells the model which pages are important and how they relate. Getting internal linking sorted properly is worth a tonne of good backlinks. I have seen sites with mediocre off-page profiles outrank heavier competitors on the strength of internal linking alone. If you do one technical thing this quarter, audit the link graph.
• llms.txt. Overrated. No hard evidence LLMs respect it meaningfully. We run one on emgigroup.com because it takes an hour and cannot hurt. Position it as covering bases, not as a silver bullet.

I am going to keep pushing back on the llms.txt hype. Every few weeks somebody publishes a post claiming it is the next frontier. I have not seen the data. Until I do, treat it as a hygiene item.

Layer 5: Freshness and searchability. Is your content still indexable and current?

Freshness is massively important, and most SaaS teams underweight it. Here is the test I run before any content engagement. Open the SERP for the target keyword. Look at when the top-ranking pieces were last updated. If the top three are all fresh inside six months, your 2023 cornerstone page is not going to compete, no matter how good the original writing was. LLMs and Google both weight recency heavily on commercial queries, and the gap between a fresh piece and a stale piece is not 10%. It is closer to the page being read versus the page being ignored.

Updating existing content with fresh stats, new screenshots, and actual 2026 examples makes it more extractable. That is the word I want readers to hold onto. Extractable. An LLM pulling a passage wants a statistic with a recent date attached and a source it can cite. Give it that and your odds of citation go up meaningfully.

The conclusion I have reached after running this across dozens of client blogs: it is better to maintain a blog with fewer pages and genuinely fresh content than to keep publishing new pieces while older ones rot. Most SaaS teams have the ratio backwards. They publish four new posts a month and update zero. Flip that. Content updates are just as important as new content, arguably more so, because a refreshed page already has link equity, internal link graph position, and search history working for it.

The second-order problem is searchability. Content that is fresh but poorly structured for extraction still does not get cited. Content that is well-structured but 18 months stale gets overtaken by a newer, better-structured competitor piece. Fresh plus extractable is the target.

The working cadence I use with clients:

• Cornerstone pages. Review every 60 to 90 days. Update stats, refresh examples, add new comparisons.
• Listicles and “best of” pages. Touch every 30 days minimum. These rot fastest.
• Product docs. Update on every release. LLMs read doc sites as authoritative source.
• Blog posts with commercial intent. Annual refresh at minimum, ideally 6-monthly.
• “Last updated” stamps. Render them on the page, include them in schema.

The searchability side is simpler than it sounds. Can a crawler reach the passage? Is the heading a question? Is the answer in the first 40 words? Does the passage have a source? If the answer to any of those is no, you have a searchability problem dressed up as a freshness problem.

Layer 6: Distribution. How does your authority become visible?

This is the layer most GEO guides miss, and it is the layer I think is becoming decisive. Authority is not just “what domains link to you”. Authority is what the LLM has seen your brand associated with, across every surface it has crawled. Distribution is the mechanism that makes that association happen.

The distribution surfaces that compound

• Podcasts. Niche industry shows, not top 10 charts. Transcripts get indexed. Expert quotes compound.
• YouTube. Underrated as a GEO surface. ChatGPT and Gemini both cite YouTube transcripts. Long-form interviews, product walkthroughs, debates.
• Substack and Medium. Newsletters with a consistent cadence build entity association fast. LLMs trust them disproportionately.
• LinkedIn. Dense with buying-committee signal. Comment threads get indexed.
• Parasite placements. Forbes, Fast Company, trade publications. Pay-to-play mostly. Still useful.
• Expert quotes in journalists’ articles. Help a B2B Writer, Qwoted, Featured.com, direct outreach to trade journalists. Each quote is a compounding citation.
• Journalist relationships. Long-term, not transactional. Two good relationships outperform 20 pitches.

I run EMGI’s distribution personally because I do not trust it to a junior. The cost of a missed relationship compounds for years. The cost of a good relationship does the same in reverse.

Our Prospeo engagement is a clean example. We paired targeted link building on head-term comparisons with distributed PR on niche email-finder reviews and podcasts. The earned citations went from under 10 monthly to over 60 inside 8 months. ChatGPT started citing Prospeo at rank 3 on “Hunter.io alternatives” prompts. Distribution did the heavy lifting that authority alone would not.

Layer 7: Measurement. If you cannot see it, you cannot improve it.

Measurement is where most SaaS teams give up or overspend. The AI visibility tools market is loud, expensive, and largely unnecessary if you have an engineer who can spend a weekend with an API. Semrush, Ahrefs, Profound, Peec AI, Otterly, Athena all offer AI visibility tracking. Most of them wrap the OpenAI or Anthropic APIs with a dashboard on top.

What to measure

1. Citation frequency. On your priority prompts, how often does your brand appear, as a percentage of runs?
2. Share of voice on target keywords. Of all brands cited on a prompt, what share is yours? Track this at the keyword level, not the domain level, because share of voice fluctuates heavily by query. Your brand might hold 40% share on “best X for Y” and 0% share on “X alternatives”, and those two numbers move independently.
3. AI-referral traffic. GA4 segment for ChatGPT, Perplexity, Copilot referrers. Still small as a share of total organic, but growing fast and disproportionately high intent. A ChatGPT referral converts at a meaningfully higher rate than a cold organic click, because the buyer has already been pre-qualified by the model’s shortlist.
4. Branded query lift. When AI citations rise, branded search typically follows inside 60 to 90 days. This is the second-order evidence that AI visibility is working, even before referral traffic shows up meaningfully in analytics.

Share of voice is the metric I care about most. It collapses citation presence and competitive pressure into one number per keyword. If your share of voice climbs on the 20 buyer prompts you care about, everything else (traffic, pipeline, branded search) follows. If it does not climb, you are running the wrong tactics.

How to measure, without a tool vendor

At EMGI we built our own measurement stack on top of the DataForSEO APIs. I will not go deep on the implementation here, but the short version is: we run the priority prompts on a weekly cadence, log the cited brands, track share of voice by keyword, and flag changes. It gives us the same output as a $1,500 per month SaaS tool for a fraction of the cost. If you have an engineer who can spend a weekend with an API, you do not need a vendor. I cover the tool-building approach in more depth in our ChatGPT citations playbook and do a full category review in our AI visibility tools guide.

Under $1M ARR, build, defer, or ask your agency. Over $1M ARR, a tool is fine, but know what you are paying for.

“GEO is just good SEO”. Let me push back properly.

Danny Sullivan, Google’s search liaison, has repeatedly said that “good SEO is good GEO”. The line gets quoted a lot, usually by old-guard SEOs who find it reassuring. I want to push back on it carefully, because the line is half right and the other half is doing real damage.

Where the “GEO equals SEO” argument is correct

Authority still wins at the macro level. There is a huge correlation between what ranks well in Google and what gets cited in AI answers. Our own CRM research (publishing soon) shows an AIO-to-ChatGPT citation correlation of 0.94 across the category. That is enormous. The two systems draw from the same underlying authority pool. If your SEO is genuinely strong, you will pick up a meaningful share of GEO visibility for free.

Fundamentals still print. E-E-A-T, technical hygiene, entity salience, fast sites, good schema, earned links. None of that has changed. Anyone telling you to throw out the old playbook is either selling a new tool or has not been practising long enough to remember that fundamentals always win.

Where the argument fails

LLMs were not in common use two or three years ago. Claiming “nothing is different” in 2026 is naive. Three specific ways LLMs diverge from classic search:

• LLMs love citing data. Original statistics, survey results, experiment outcomes get cited disproportionately, even from lower-authority sites. This is the opening for smaller brands.
• LLMs read long-form content with semantic depth. A 3,000-word guide that covers topical relationships comprehensively can get cited above a thinner, better-linked page.
• LLMs pull context from articles that observe relationships between entities. Comparison pages, head-to-head reviews, “alternatives to X” content are semantic gold for the model.

Quick aside. You can spot an AI-written article that has been published without human oversight because it almost always drifts onto LLMs and AI search as the example topic. It is the tell. Ironic that LLMs love talking about how important LLM search is. Ha ha. Honest version for this piece: I am writing about AI search because I run an agency that works on AI search, not because the model steered me there.

I think a lot of the “GEO is just SEO” rhetoric is the old guard resisting change because the change threatens their existing expertise. If GEO is genuinely different, then the person who has spent 15 years building link-driven authority plays has to learn something new. That is uncomfortable. Saying “it is all just SEO” is easier than saying “the game has added a new surface with new rules on top of the old ones”.

The proof I keep coming back to

EMGI is not Ahrefs. We are not Search Engine Journal. We are a small specialist link building agency. When we published our SaaS AI Citation Gap Report, it was picked up by LLMs inside a week. Minimal distribution. No tier-one PR. The data was original and the semantic framing was clean. The model decided it was useful.

That would not have happened in 2023. Link graph weight would have pushed the citation to Ahrefs, Semrush, or HubSpot regardless of who had the better data. The model changed. “GEO is just SEO” misses that change.

Proof: how we make SaaS brands visible in AI answers

Theory is cheap. This section is the evidence I point clients at before they sign. First Page Sage reported 702% ROI on B2B SaaS SEO investments with a 7-month average breakeven in 2026 (First Page Sage, 2026). GEO sits inside that envelope. Here is what the work looks like in practice.

The web scraping SaaS case, DR 65+ and the authority distribution effect

Our primary SaaS case study is a web scraping SaaS client I took on when their head of marketing called me frustrated that their AIO presence was zero. They ranked well on Google for the head term but did not appear in the AI overview. Classic invisibility problem.

We focused on DR 65+ sites as the anchor quality filter. Over the engagement, the earned citations compounded in a specific way: long-tail queries started ranking inside AIO after the link work, even though those long-tail queries were never direct targets. The authority passed through the site internally and distributed across the whole URL set. This is the whole-site authority distribution effect. It is the clearest live evidence I have that GEO rewards systemic authority, not page-by-page tactics.

Final result: rank 1 inside Google AIO on the target head term, with related long-tail queries picking up AIO presence as a second-order effect. The full case study has the numbers.

The UK e-commerce experiment, 0 to 7,000 impressions in two months

This is an unpublished EMGI experiment I want to describe carefully because the client is in a UK e-commerce niche I cannot disclose. We launched a brand-new website on an exact-match domain, with no pre-existing authority. Semantic-depth content combined with targeted link building on the keywords that started to perform.

The detail worth sitting with: we were not targeting soft category-level prompts. The experiment was run on direct commercial-intent keywords with 1,000+ monthly search volume each. These are the terms where the buyer is already in purchase mode, not the informational end of the funnel. Inside two months the site moved from 0 to 7,000 monthly impressions and kept growing. It appeared inside Google AI Overviews and was cited inside ChatGPT on category-level prompts adjacent to the commercial targets. We were the only company under DR 70 to appear on those AI surfaces at all. Every other cited brand was significantly larger, more established, and sitting on years of accumulated authority.

This is the proof point that kills the “you need DR 80 to get AI citations” line. You do not. You need the right semantic signal plus the right targeted authority work on the exact keywords you want to win.

The SaaS AI Citation Gap Report, self-proof

I have referenced this twice already but it is worth separating out. Our research across 150 SaaS brands was picked up by LLMs within seven days. It became a cited source on multiple commercial AI prompts inside a month. The report lives on emgigroup.com, a specialist agency site, not a tier-one publication.

This matters because it closes the loop on the “GEO is just SEO” argument. If GEO were purely an authority play, the report would not have been cited. Ahrefs or Semrush would have been cited instead, for the same topic, by default. The semantic value of the data was what the model rewarded.

The invisible shortlist: EMGI clients winning on category-level ChatGPT prompts

This is the evidence I keep coming back to when someone asks me whether GEO “really works” for SaaS. We have a longer list of wins where clients rank on their own branded and “X vs Y” queries, but honestly, a client ranking for their own brand versus a competitor is not the impressive part. Anyone can do that. What matters is category-level visibility, where the buyer has not yet decided on a vendor and the model picks who to surface. Those are the wins below.

Two patterns stand out. First, alternatives and “best X” queries are the winnable surface for smaller SaaS brands. You do not need to win every comparison prompt. You need to be inside the three-to-five names the model surfaces when a buyer asks the category-level question. Second, Reddit quotes are load-bearing on these positions. The proxy client’s rank 3 is partly sustained by Reddit thread citations, confirming the Semrush 40% stat in live conditions.

If you want a sibling playbook on specifically how to get cited in ChatGPT, read our ChatGPT citations for SaaS guide next. For the Google AIO side, see the AI Overview optimisation playbook. For the tool landscape, the AI visibility tools guide.

What should a SaaS team actually do?

Editorial.Link’s 2025 survey (n=518) found that 56% of SaaS SEO teams outsource link building (Editorial.Link, 2025). Whether you run GEO in-house or work with an agency, the core activities are the same. I do not think in “week 1, week 2” steps because the work is not linear. It is five streams that run in parallel and keep running. Here is how I structure the first 90 days, and then the ongoing cadence.

Diagnostic

• Identify your top 20 buyer prompts. Middle and bottom-of-funnel only, not brand terms.
• Run them through ChatGPT and Google AIO. Log where you appear, where you do not, and which sources are cited instead.
• Score the sources cited against: can we earn a placement there?
• Benchmark share of voice by keyword so you have a baseline to measure against.

Entity

• Wikidata, Crunchbase, LinkedIn Company page. Update, clean, verify facts.
• G2, Capterra, Gartner Peer Insights, Software Reviews. Check category tagging. Apollo.io’s zero-citation problem was a category tag, not a review volume issue.
• Claim or correct any misattributed mentions.

Suite (technical and on-site)

• Audit JavaScript rendering on pricing, features, and comparison pages. Fix first.
• Review internal linking. Tighten topical groupings, strengthen hub pages, remove orphan URLs.
• Add Organization, Product, and FAQ schema where missing.

Passage rewrites

• Question headings. Direct answers in the first 40 words. Cited data. Concrete examples.
• Update older commercial pages with fresh 2026 stats and examples. Extractability is the goal.
• Compress passive voice, shorten paragraphs, get to the answer faster.

Citations

• Reddit: write genuine, high-value answers in your top relevant subreddits. Not spam.
• Pitch niche-publication guest posts on topics with commercial overlap.
• Book founder or CMO podcast appearances. Target niche shows, not top 10 charts.
• Start journalist relationships. Two good ones outperform 20 pitches.

This is ongoing work, not a project with an end date. You stack authority over time. Each earned citation, each refreshed page, each new entity association compounds. Your competitors are doing this right now, which means the gap either closes or widens every month. There is no steady state. If you want the managed version, that is what we do on our retainers.

Frequently asked questions

Closing thoughts

The reason I wrote this guide is simple. I keep meeting SaaS marketing leaders who are anxious about AI search, watching traffic dip on AIO-heavy queries, unsure whether to throw out the old playbook or double down. The answer is not either-or. The answer is Search Everywhere Optimisation: optimise for both surfaces, measure both, treat them as one channel with different shapes.

The seven-layer model (Entity, Citation, Content, Technical, Freshness, Distribution, Measurement) is how I structure the work. The proof is in the category-level ChatGPT wins we track for clients, in the web scraping case, in the UK e-com experiment, in our own citation gap report being picked up inside a week.

If you remember one thing from this piece: your buyers are already running the prompts. The shortlist is already being built. The only question is whether you are on it.