SEO used to answer one question: can someone find your page.
AI search adds a second, harder one: when someone asks ChatGPT, Perplexity, or Gemini for a recommendation, does the system know enough about your product to actually name it.
That gap is widening fast. Understanding that gap is now central to AI visibility, especially for businesses competing to be mentioned in generated answers.
Research from GEO analytics firm Brandlight found that the overlap between top Google results and the sources AI systems actually cite has fallen from 70% to under 20%.
Ranking well in Google no longer means an AI system will mention you at all. It’s tracking a different signal.
That signal is generative engine optimization, or GEO. The term comes from a 2024 ACM SIGKDD paper by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande, researchers from Princeton, Georgia Tech, and IIT Delhi.
Their GEO-bench tested nine optimization tactics across 10,000 real queries to see what actually changes whether a source gets cited in a generated answer. Some tactics moved the needle. Most didn’t.
For SaaS and fintech companies specifically, this matters more than most industries.
Recent market data puts finance, legal, and SaaS at roughly 55% of all LLM-driven search sessions, because these are exactly the complex, multi-stakeholder purchase decisions people now offload to AI before they’ll even open a comparison page. If your product category is one where buyers ask “what’s the best tool for X” before they Google it, GEO isn’t optional.
Here’s how to actually build for it, without pretending it’s a checklist you finish once.
Start With Queries Your Product Actually Deserves to Win
The common mistake is asking “how do I get ChatGPT to mention us.” That’s backwards.
The better question is: what would someone need to be asking for our product to be the obviously correct answer?
Call this query mapping. It works in layers of specificity, and each layer changes who’s competing for the spot.
Take a hypothetical example: a mid-market invoicing tool for freelance agencies.
- Broad: “best invoicing software”
- Narrower: “invoicing software for agencies”
- Specific: “invoicing tool that handles retainer billing and multi-currency clients for a 5-person agency”
At the broad layer, you’re competing against every invoicing brand with name recognition, whether or not it’s actually a good fit. At the specific layer, fit matters more than fame.
A smaller, more specialized tool can beat a category leader here, because the AI system has more to work with when deciding who solves this particular problem.
This is worth sitting with: GEO performance isn’t one score. A product can be a strong answer to a narrow, well-defined problem and nearly invisible for the broad category term. Both are true at once, and they require different work to fix.
A workable starting point is 15 to 20 real queries that map to actual buying situations, not keyword variations.
Structure them around:
- who’s asking,
- what problem they have,
- what constraint they’re working with (budget, integration, compliance, team size), and
- what category they’d search under.
Don’t try to cover every possible phrasing. Cover the actual decisions your customers are making.
Make Your Positioning Legible to a System That Can’t Infer
Once you know which queries you should win, check whether your own site actually explains why.
Try reading your homepage and asking: could a machine accurately summarize who this is for and why they’d pick it?
Most SaaS sites fail this quietly, using lines like “the modern platform for scaling your business” that a human politely nods at and a language model has nothing to extract from.
Compare that to something like: “Built for finance teams at Series A to C startups who need SOC 2-ready expense approval without hiring a controller.”
The second version hands over five usable facts: who it’s for (finance teams, specific company stage), the problem (compliance overhead without dedicated headcount), and the mechanism (automated, audit-ready approval flow). An AI system synthesizing an answer can actually use that. It can’t use “modern platform.”
A simple formula to check your own pages against:
Who it’s for → the problem they have → how you solve it → what changes for them → why that claim is credible.
You don’t need that exact sentence on every page. You need those five things to be consistently extractable, whether a system is reading your homepage, your docs, a review site, or a comparison article that mentions you.
Generic language is the enemy here, not because it sounds bad, but because it’s informationally empty. “Trusted by teams worldwide” and “industry-leading” show up on thousands of competing pages and tell a retrieval system nothing that distinguishes you from any of them.
Feed the System Evidence, Not More Adjectives
This is where the Aggarwal et al. findings get specific enough to actually apply. Their top three tactics, cite sources, add quotations, add statistics, produced a 30 to 40% improvement on their word-count visibility metric and 15 to 30% on their subjective quality metric.
Keyword repetition wasn’t on that list. It didn’t move the number.
In their own words, the researchers found these methods “achieved a relative improvement of 30-40% on the Position-Adjusted Word Count metric and 15-30% on the Subjective Impression metric,” and attributed the gains to added credibility and content richness rather than surface-level optimization.
Translated for a SaaS content page: don’t just claim your product reduces reconciliation errors. Cite the number.
- Don’t just say experts recommend automating approval flows. Quote one, with attribution.
- Don’t just assert your onboarding is fast. Give the median time and how you measured it.
The effect isn’t uniform across every topic. Some domains respond better to hard statistics, others to expert quotation and authoritative framing.
There’s no single template that works everywhere, which is really just an argument for testing your own category rather than copying someone else’s playbook wholesale.
Don’t Publish Content That Already Exists Ten Times Over
If your competitor’s comparison page and your comparison page explain the same three differences in the same order, you haven’t added anything a retrieval system needs. This is the information gain problem. And it’s not new to GEO, but it gets more expensive to ignore as AI makes generic explainer content nearly free to produce.
Before publishing, it’s worth asking directly: what will someone know after reading this that they didn’t know after reading the five competing pages already ranking for this topic?
Useful sources of real information gain:
- your own usage data,
- a customer research finding nobody else has published,
- a documented edge case your support team handles weekly,
- an original comparison you ran yourself instead of summarizing vendor marketing pages.
These are hard for competitors to reproduce quickly, which is exactly why they’re worth the effort.
One Section Most GEO Guides Skip: Content Decay
AI systems appear to weight recency when choosing which sources to pull from.
A thorough guide published two years ago, with no updates since, will lose ground to a shallower one published last month.
This is a bigger deal for fintech content specifically.
Compliance frameworks get updated. Interest rate environments shift.
A page explaining KYC requirements or payment processing regulations that hasn’t been touched since last year is a liability, not an asset. And it’s the kind of thing an AI system is reasonably cautious about citing.
Practically, this means treating your highest-intent pages as living documents.
- Add a visible last-updated date.
- Revisit core statistics on a schedule rather than whenever someone happens to notice they’re stale.
This costs less than writing new pages and often does more for visibility than another net-new article would.
Get Other Sources Saying the Same Thing About You
Your own site can explain your positioning. It can’t independently corroborate it. That’s what off-page signals are for, and it’s a different job than traditional link building.
The distinction that matters: a backlink says “this page exists.”
A contextual mention says something more specific. Compare “Jordan Ellis contributed research to this piece” against “fintech compliance consultant Jordan Ellis, who has advised on SOC 2 audits for over 30 SaaS companies, explains why automated approval logs matter for auditors.”
The second version builds an association a retrieval system can actually connect: person, expertise, specific claim, specific audience.
This shows up through customer reviews, industry publications, podcast appearances, comparison sites, partner content, and community discussion, not through volume of links but through consistency of the same core claims appearing in credible, independent places.
A citation you can see in an AI answer tells you the system used a source. It doesn’t tell you why, and it can change between model versions without warning.
Treat this as building an accurate, consistent public record, not gaming a specific model’s current behavior.
Run Your Own Visibility Check
You don’t need a GEO platform subscription to find out where you stand. Take the query list you built earlier and run each one through the AI systems your buyers actually use.
For each query, log:
- Did your product show up at all
- Was it recommended, or just mentioned in passing
- Was the description of your product accurate
- Who else showed up, and how often
- What evidence, if any, seemed to support the answer
Patterns tend to show up fast. Some products appear reliably for narrow, specific queries and vanish for broad category ones.
That’s usually a fame or reach problem.
Others appear constantly but get described inaccurately. That’s a positioning problem, and it needs a different fix. Conflating the two wastes effort on the wrong lever.
Citations Aren’t the Finish Line
Getting mentioned by an AI system isn’t end the goal. You need to have your product recommended by AI crawlers or LLMs.
The sequence that actually matters is:
- understanding your buyer’s real query,
- backing your positioning with evidence,
- earning independent confirmation of that positioning, and
- only then showing up as a recommendation instead of a footnote.
An AI citation means a system pulled information from you. A recommendation means it decided you were the answer.
For a SaaS or fintech brand competing for consideration in an AI-mediated buying process, that second outcome is the only one worth measuring toward.
FAQs
Q#1. What’s the first thing to fix if our product never shows up in AI answers?
Start with your single highest-intent page, usually a comparison, pricing, or “best for X” page. Check whether it clearly states who it’s for, what problem it solves, and why that claim is credible. Add a direct, specific answer near the top, then supporting evidence. Fix that one page first before spreading effort across the whole site.
Q#2. Does this replace our existing SEO work?
No. Fast sites, clean structure, and solid on-page SEO are still what makes content crawlable and retrievable in the first place. GEO is what happens after a system retrieves you: whether it trusts what it finds enough to use it. You need both.
Q#3. How often should we update cornerstone content for this to matter?
There’s no universal number, but anything touching pricing, compliance, or product capability should be reviewed at least quarterly, with a visible last-updated date. Content that changes rarely, like foundational explainers, can go longer between refreshes as long as the core facts still hold.
Q#4. Is AEO the same thing as GEO?
Answer engine optimization originally referred to optimizing for voice search assistants. In practice, most voice queries now route through the same generative retrieval systems as chat-based AI search, so the two have largely merged. If you’re optimizing for how AI systems synthesize and cite information, you’re doing GEO regardless of which term your team uses internally.
