DEV Community: Mehul Jain

We Ran 3,000+ AI Prompts to Test GEO for B2B SaaS. Here's What We Found.

Mehul Jain — Wed, 27 May 2026 13:37:59 +0000

Every GEO guide tells you the same thing. Add FAQ schema. Sprinkle statistics. Get on G2. Keep it short and structured.

These tips all trace back to one study (the Princeton/IIT Delhi GEO paper from 2023), passed through hundreds of blog posts until the findings are unrecognizable.

We wanted to know if any of it holds up for B2B SaaS. So we tested it.

We queried ChatGPT, Google AI Overviews, and Perplexity with 124 unique B2B SaaS queries. Things like "best project management software for startups" and "CRM for small business." We collected 3,352 citations across 881 unique domains. Then we analyzed the top 50 most-cited pages for on-site signals and tested 12 specific hypotheses.

The results? Only 3 of 12 popular GEO claims survived contact with the data.

Here are the five findings that matter most.

1. Content length is the strongest signal. Not the weakest.

The most repeated GEO claim is that word count has near-zero correlation with AI citations. Ahrefs measured r=0.04.

Our data says otherwise. For B2B SaaS buyer queries, content length correlates with citations at r=0.393. Pages over 5,000 words average 15.3 citations versus 10.3 for mid-length pages. That's a 50% advantage.

The three most-cited pages in our dataset are all massive buyer's guides: project-management.com (9,227 words, 37 citations), wrike.com (11,017 words, 28 citations), paymoapp.com (17,890 words, 27 citations).

The explanation is simple. When someone asks AI "best project management software for startups," the AI needs a source that covers enough tools to build an answer. Short pages can't serve that function.

2. Reddit dominates ChatGPT, not Perplexity.

The popular claim: Reddit accounts for ~24% of Perplexity citations. Invest in Reddit for Perplexity visibility.

Our data flips this completely.

Reddit provides 14.7% of ChatGPT citations (4.7x more than vendor-owned content). On Perplexity? Zero. Literally 0%.

Perplexity favors vendor websites (18.1%) and YouTube (7.6%). ChatGPT favors Reddit and editorial content. If you're investing in Reddit content seeding for Perplexity, you're spending money on the wrong platform.

3. G2 and Capterra account for just 1.6% of citations.

Multiple guides claim that G2/Capterra presence is the strongest predictor of AI visibility for SaaS. Our data: review platforms account for 55 of 3,352 total citations. That's 1.6%.

Reddit alone has 6.1x more citations than G2 and 15.7x more than Capterra. The traditional review platform stack (G2 + Capterra + TrustRadius + Trustpilot + Software Advice) accounts for 78 citations combined. Reddit alone is 3.6x larger.

Having a G2 profile is table stakes for SaaS credibility. Optimizing it as your primary GEO strategy is a misallocation.

4. FAQ blocks and schema markup show zero advantage.

FAQ blocks: pages with them average 11.7 citations versus 12.4 without. The ratio is 0.94x. Slightly negative.

Schema markup: r=0.103 correlation with citations. Flat.

ChatGPT and Perplexity parse rendered HTML, not JSON-LD. They see your headings, tables, and lists. JSON-LD schema tells Google's parser about your content, but tells ChatGPT nothing it can't already extract from the page.

Stop spending engineering hours on FAQPage schema as a GEO tactic.

5. There are two AI ecosystems, not three.

Google AI Overviews and Perplexity share 55% domain overlap. They largely cite the same sources. ChatGPT overlaps with either at less than 6%.

This means you need two strategies, not one.

For ChatGPT: Reddit presence, long-form guides, comparison content. ChatGPT's web search only triggers on "best X" and comparison queries (94% and 75% trigger rates). FAQ queries trigger web search 0% of the time.

For Google AIO + Perplexity: vendor-owned content, YouTube, niche blog placements. These platforms reward professional publishing.

What to do with this

The winners in our dataset aren't the most "optimized" pages. They're the most useful ones. The page with 37 citations earned them by being the most thorough buyer's guide in its category. Not by adding FAQ schema or sprinkling statistics.

The full study, with all 12 hypotheses, per-platform breakdowns, the review source comparison table, and the complete playbook, is here:

Read the full report: GEO for B2B SaaS: What Actually Works

This research was conducted by Geology, the analytics platform for Generative Engine Optimization. Data collected May 2026.

GEO and AEO for Startups: A Founder's SEO Kickstart

Mehul Jain — Wed, 06 May 2026 06:06:46 +0000

Most early-stage founders treat SEO, GEO, and AEO as three separate budgets, three separate hires, three separate dashboards. At startup scale that is the wrong model. The three are one practice with three places to measure the same content. Trying to fund them as parallel workstreams is the most common waste of early-stage marketing time I see.

Here is the framing that holds at this stage. Classic SEO ranks pages on Google. GEO (generative engine optimization) earns brand mentions inside ChatGPT, Perplexity, and Gemini answers. AEO (answer engine optimization) targets the literal text of an AI response or a Google AI Overview. The work that earns one tends to earn the others. What changes between them is where you measure the result, not what you write.

Why early stage favors GEO and AEO over classic SEO

Domain authority compounds slowly. Backlinks take months. A six-month-old startup competing for "best [category] tool" against incumbents with 10,000 backlinks is a losing fight on Google.

AI answers do not work that way. ChatGPT, Perplexity, Gemini, and Google AI Overviews evaluate whether a source answers a specific question well. They weight recency, structured specificity, and third-party signal differently than Google's traditional ranking model. A startup that publishes 15 precisely targeted answer pages in a month can show up in AI responses well before it ranks page two of Google. The deeper argument for why this happens lives in our post on building AI visibility from zero, which walks through the topic-authority gap between the two systems.

That is the structural advantage. It is also why a startup founder should not start with a content calendar or a keyword strategy. They should start with a prompt list.

Step one. Map the prompts your buyers ask AI

Before you write anything, build a list of 30 to 60 prompts your buyers actually type into ChatGPT, Perplexity, or a Google AI search bar in the week before they buy.

Pull from these sources:

Sales call recordings, where you can hear the literal phrasing of buyer questions
Support tickets and customer interview transcripts
Reddit and Stack Overflow threads in your category
Your competitors' FAQ pages and help center entries

This is the foundation of AEO. Answer engines pull from sources that match the prompt closely. If your content is not built around the prompts your buyers actually use, it will not be cited.

Step two. Mine the founder's head

Most early-stage startups have already done the hard part of content. The founder has spent a year talking to users. They know which workflows the product handles well, which buyer objections come up most, and which competitor weaknesses are real.

The problem is that this knowledge sits in Notion docs, Slack threads, and call recordings. None of it is in a place an AI model can read. The unlock for early-stage GEO is converting that material into 10 to 15 source-of-truth pages on your own domain. Each page answers one prompt from the list above with depth and specificity that no aggregator article can match.

Founders often resist this because shipping 15 articles feels like more work than shipping five. It is not. The content is already in the founder's head. The job is transcription and editing, not research.

Step three. Earn the off-site signal

AI models do not trust new domains alone. They cross-reference whether the brand or the expert behind it appears credibly elsewhere. For a startup with no link history, that means showing up in places AI training and retrieval systems read: niche subreddits, GitHub discussions, Stack Overflow, Hacker News, IndieHackers, Product Hunt reviews, podcast transcripts, and small-creator newsletters.

Two practical rules:

The off-site activity has to be useful to the community first, promotional second. Mod rules matter. Account history matters. Helpful answers anchored to the source pages on your site are the shape that works.
Do not buy links. AI models seem unbothered by paid or low-quality link networks for now, which is the opposite of how Google works. Real conversations in real communities move the needle.

Where this goes wrong

A few patterns repeat in early-stage teams.

The first is hiring a full-time SEO person before there is any signal. The first dedicated GEO hire usually makes sense at month four to six, when there is weekly citation data and a backlog of plays the founder can hand off. Before that, the founder is the right person to write the source pages, because nobody else has the domain conviction.

The second is writing for keywords instead of prompts. Keyword tools optimize for what people type into Google. AI prompts are longer, more conversational, and more specific. If your brief uses Ahrefs keywords as the source of truth, you are optimizing for the wrong text.

The third is treating Reddit as a marketing channel. It is a community signal channel. The startups that benefit are the ones whose accounts have helpful comment history predating the product launch.

The 30-day plan, in one sentence

Pick 30 prompts your buyers actually ask AI, ship 10 to 15 source pages that answer them with founder-level specificity, and seed three communities with helpful answers anchored to those pages.

If you want the version with weekly artifacts, founder time commitments, and a citation dashboard, the Geology startups solution covers the four-week program we run with pre-seed to Series A teams. For the underlying difference between optimizing for search engines and optimizing for AI answers, GEO vs SEO is a useful read.

The window for early-mover advantage in AI discovery is still open in most categories. It will close. The founders who run this playbook in the next twelve months will compound a citation lead their later-funded competitors will spend a year trying to close.

Mehul Jain is an AI entrepreneur and product builder. He writes about how search is shifting from keywords to model-mediated discovery at Geology.

The GEO Playbook: How to Make Your Brand the Answer in ChatGPT, Perplexity, and Google AI

Mehul Jain — Sat, 18 Apr 2026 19:13:11 +0000

By Mehul Jain, Co-Founder of Geology

Estimated read: 7 minutes

About 40% of product discovery now begins on an AI platform instead of Google. Someone asks ChatGPT "best invoicing tool for freelancers in India" and a short list of three brands comes back. If you aren't one of them, you are invisible — there is no second page to click to.

This is the shift Generative Engine Optimization (GEO) was built to answer. It is not a rename of SEO. It is a different retrieval game, with different inputs, different winners, and different ways to measure whether you are winning.

This piece lays out what GEO is, what ChatGPT and Perplexity and Google's AI Overviews actually use to build answers, and the specific moves that get a brand cited. It is the condensed version of what we run for our customers at Geology.

1. What GEO actually is

Generative Engine Optimization is the practice of getting your brand, product, and claims surfaced inside AI-generated answers.

Traditional SEO optimises a page to rank on a list of ten blue links. A human scans the list and decides which to click. GEO optimises for a different transaction: an AI reads many pages on your behalf, synthesises them, and returns one paragraph. Your job is no longer "rank #3." Your job is "be cited in the paragraph."

The tactics overlap with SEO — you still need crawlable pages, clean schema, internal links, and authority — but the success criteria is different. A brand that ranks #8 on Google can still be the #1 cited source in ChatGPT's answer. And a brand that ranks #1 on Google can be absent from AI answers entirely if its content does not match how LLMs pick citations.

2. How ChatGPT, Perplexity, and Google AI Overviews pick citations

We have spent the last nine months reverse-engineering citation patterns across all three. The rules are not identical, but they rhyme.

Citations correlate with these signals, ranked by weight:

Direct answerability. The page must answer the question in its first 2–3 sentences, ideally with a bolded or H2-level statement. LLMs extract top-of-page content far more than deep-scroll content.
Structured claims. Lists, comparison tables, pricing tables, and step-by-step instructions are extracted disproportionately. A paragraph of prose is reworded; a structured block is often quoted close to verbatim.
Recency. Perplexity in particular weights the last 12 months heavily. A post with a 2024 date stamp rarely wins over a comparable 2026 post.
Brand mention density on third-party sites. This is the GEO-specific one. When Stripe is mentioned on 40,000 third-party pages in the context of "payments," that density shows up in how often LLMs cite Stripe. SEO calls this "off-page." GEO treats it as a first-class input.
Schema and entity clarity. Organization, Product, and FAQPage schema with a stable sameAs block across Wikidata, Crunchbase, and LinkedIn materially improves the odds the LLM resolves your brand as a distinct entity.
Domain authority, but less than you think. DA still matters. It matters less than in classical SEO. We have repeatedly seen DA-40 sites cited above DA-80 sites when the answerability and structure were cleaner.

3. The four moves that actually work

If you only do four things this quarter, do these.

Move 1: Rewrite the top of every pillar page to be answerable in 40 words

Pick your ten most commercially important pages. On each, the first H2 and the first paragraph must directly answer the main question in under 40 words. Not "In this post we will explore…" — the answer, first.

Test: open the page, ctrl-F for the exact question a customer would type into ChatGPT, and confirm the nearest preceding paragraph answers that exact question cleanly. If it doesn't, rewrite until it does.

Move 2: Ship one comparison table per pillar

Create one HTML table per pillar that compares you against your top three competitors on the four or five decision factors that matter. Not a puff piece — an honest table. Tables get extracted verbatim into AI answers more than any other content block we have measured.

One of our customers added a single comparison table to their pricing page and watched Perplexity begin citing them in "X vs Y" queries within three weeks, without any new backlinks.

Move 3: Build brand-mention density across 30+ authoritative third-party sites

This is the heaviest lift and the one most teams skip. The shortlist to start with: Crunchbase, G2, Capterra, AlternativeTo, Wellfound, LinkedIn company page, Product Hunt, Futurepedia, F6S, and one industry-specific directory per category you compete in. Then layer in contributor articles on industry publications, PDF hosting on Issuu and Scribd, and a handful of genuinely useful forum answers on Reddit, Warrior Forum, and SitePoint.

None of these individually moves the needle. Together they compound into the brand density that LLMs use to decide whether you are a "known player" or not.

Move 4: Add FAQPage schema and a 10-question FAQ section to every pillar

FAQPage schema is still under-used. Add ten questions per pillar, each with a 40-to-80-word answer. Optimise each answer for a literal voice query. This is the single highest-leverage schema change we deploy for customers.

4. What to measure (and what not to)

Do not measure GEO by "impressions in Google Search Console." That is still useful for classical SEO but it is blind to AI.

Measure instead:

Citation share. For your top twenty-five commercial queries, how often does your brand appear in the answer across ChatGPT, Perplexity, Google AI Overviews, and Claude? Run it weekly. Track the trendline, not the absolute number.
Share of voice in AI answers. When your category is mentioned, what percent of the time is your brand one of the cited players? Below 15% means you are invisible. Above 40% means you are a default answer.
Referral traffic with LLM user-agents. ChatGPT, Perplexity, and a few others now send referral traffic with identifiable user-agents. Break these out in GA4 as a separate source. Growth here is a leading indicator.
Brand-name search lift. Classical SEO metric, still works. When GEO is working, "your brand name" search volume rises 10–30% over six months.

5. What we would skip

A few tactics we routinely see recommended that we do not recommend:

Stuffing content with "In 2026…" framing. LLMs don't reward hedged recency language. They reward actual dated content.
Buying low-quality PBN links. Worse for GEO than for SEO, because LLM training sets aggressively down-weight low-quality domains.
AI-generated, un-edited content. LLMs appear to partially detect and down-weight text that reads like the model's own output. The ones most likely to cite your post are the ones most primed to spot that pattern.
"Just do more SEO." Classical SEO is necessary but not sufficient. If you only have 20 hours a week, spend 8 on GEO-specific moves above, and 12 on classical SEO. Not the other way around.

6. The 90-day starting plan

If a team came to us today with no GEO work done, here is the exact 90-day plan we would run.

Days 1–14: Audit current citation share for your top 25 queries across 4 AI platforms. Fix Organization schema and sameAs across Wikidata, Crunchbase, LinkedIn, and your homepage. Rewrite the top-of-page for your five most important pillars to be answerable in 40 words.
Days 15–45: Ship one comparison table per pillar. Add 10-question FAQPage to each pillar. Publish three contributor pieces on industry-relevant third-party sites (one per week, three weeks).
Days 46–75: Build out brand density on 20 directories and PDF-hosting sites using the same core assets. Publish GEO-specific lead magnets (an audit template, a playbook) on Issuu, Scribd, Academia. Get listed on Crunchbase, G2, Capterra, AlternativeTo.
Days 76–90: Re-audit citation share. Expect a 10–25% lift if the work above is done cleanly. Double down on whichever pillar showed the biggest movement.

If you want a shortcut: we built Geology to run exactly this playbook as a service, with the citation-share tracking already wired up. But the playbook is the playbook — whether you run it in-house or with us.

Mehul Jain is the Co-Founder of Geology, an AI-powered SEO and Generative Engine Optimization platform. Connect on LinkedIn, visit jainmehul.com, or read more at getgeology.com.