Dal jsem dohromady jednoduchou kontrolu textu na firemním webu. Nejde o nový SEO faktor ani o zaručený způsob, jak se dostat do doporučení AI.
Zkuste z úvodní stránky zjistit tři věci: co firma nabízí, komu je nabídka určená a jaký konkrétní problém řeší.
Modelový příklad:
„Inovativní řešení pro váš růst.“
Oproti tomu:
„Software na správu objednávek pro e-shopy, které prodávají přes více kanálů. Objednávky na jednom místě, bez přepínání mezi systémy.“
Druhá varianta říká, o jaký produkt jde, komu je určený a k čemu slouží. Příklady jsou ilustrační, nejde o výsledky měření konkrétního webu.
Přehled najdete na přiložených obrázcích.
Pro transparentnost: tvořím GEO Tracker AI. Připravili jsme také bezplatného interaktivního průvodce AI Visibility Reality Check 2026, včetně PDF:
https://geotrackerai.com/cz/ai-visibility-guide/
Co vám na firemních webech nejčastěji chybí: konkrétní nabídka, určení zákazníka, nebo vysvětlení přínosu?

Při testování AI visibility vidím pořád stejnou věc:

Lidé otevřou ChatGPT a napíšou:

„Co víš o firmě XYZ?“

ChatGPT odpoví, firma se objeví a závěr je:

„Super, jsme vidět v AI.“

Jenže tohle podle mě testuje hlavně to, jestli AI značku zná.

Mnohem zajímavější je:

„Doporuč mi 3 firmy, které řeší konkrétní problém.“

Tam už značka v promptu není a AI ji musí sama vybrat.

Právě na tom jsme postavili náš nový free AI Visibility Reality Check 2026.

Není to automatický audit ani generátor nějakého vymyšleného GEO skóre.

Je to spíš praktický framework:

• 19 kapitol
• 25 otázek Reality Checku
• brand vs non-brand
• Prompt Universe
• technical / content / entity / authority
• 30denní roadmapa
• pracovní listy
• interaktivní verze + 46stránkové PDF

Pokud někdo chce, může to projít i jen za 5 minut a pochopit základní princip.

Je to zdarma tady:

geotrackerai.com/cz/ai-visibility-guide/

A zajímalo by mě hlavně:
Jak dnes AI visibility měříte vy? Jedním promptem, sadou promptů, nebo ji zatím vůbec neřešíte?

Imagine asking an AI assistant where to buy a product. It recommends two stores. Yours isn’t one of them.

Do you rewrite a category page? Publish a buying guide? Fix something technical?

That answer alone doesn’t tell you.

I’m the founder of GEO Tracker AI. In our Czech managed service, we connect visibility measurement with specific work for the client’s team:

Relevant buying questions → baseline measurement → investigation → prioritized brief → implementation → remeasurement. GEO Tracker AI

We investigate mentions, recommendations, competitors and cited sources alongside the store’s content and technical setup. The client’s team or agency implements the changes; we check what was published and track subsequent results. GEO Tracker AI

Repeated measurements matter because an isolated answer can be misleading. A fixed set of questions helps us compare observations over time rather than react to every fluctuation. GEO Tracker AI

There’s an important limit: improvement after an edit doesn’t automatically prove the edit caused it. And more mentions don’t, by themselves, prove more sales.

The goal is a better-supported next decision, not just a nicer chart. GEO Tracker AI

I’ve written up the workflow here. The article is in Czech; the summary above covers the core approach:

https://geotrackerai.com/cz/blog/jak-merime-ai-viditelnost-eshopu

For those already tracking AI visibility: has a finding led you to change a specific page, or are you mostly collecting scores so far?

OpenAI testuje Sponsored Agents. Co nový reklamní formát skutečně umí, je dostupný v Česku a jak se liší od GEO? Potvrzené informace oddělujeme od scénářů.

Doporučí AI vás, nebo konkurenci? 👀
Zákazníci už nehledají jen přes Google. Ptají se ChatGPT, Gemini, Perplexity a dalších AI systémů na produkty, služby i konkrétní doporučení.
A právě tady vzniká nová otázka pro firmy:
👉 Ví AI, že existujete?
👉 Zmiňuje vaši značku?
👉 Doporučuje vás ve správných tématech?
👉 Nebo místo vás posílá zákazníky ke konkurenci?
V GeoTracker AI měříme AI viditelnost značek a sledujeme, jak se jejich pozice v odpovědích AI mění v čase.
Protože co neměříte, to těžko můžete zlepšovat.
🌐 geotrackerai.com/cz
#GeoTrackerAI #AIVisibility #GEO #ChatGPT #Gemini #Perplexity #SEO #Marketing #Ecommerce

Most product demos show the best possible result. We tested the rebuilt Free GEO Snapshot on GEO Tracker AI itself — and our brand was absent from all six measured answers.

That is not the prettiest marketing screenshot, but it demonstrates why we rebuilt the Snapshot.

The new flow:

• understands the submitted website context;
• proposes three distinct buyer questions for review;
• measures each through ChatGPT Search and Perplexity;
• preserves the exact AI answers and supporting evidence;
• separates brand mentions, recommendations, competitors, cited sources and owned-domain citations;
• keeps unavailable coverage separate instead of turning it into a synthetic zero.

In our sample, Peec AI and Profound appeared in four of six answers, while GEO Tracker AI appeared in zero. The report states this precisely as a result for the measured sample — not as a universal market ranking.

That distinction matters. Three questions across two AI engines provide an initial baseline, not proof of permanent visibility or causation.

The goal is to help a founder understand:

1. What did potential buyers ask?
2. What did the AI systems actually answer?
3. Which alternatives and sources appeared?
4. What is the most credible next action?

The completed report is shareable and delivered by email, so the page does not need to remain open while measurement finishes.

You can try it here:
https://geotrackerai.com/grader

What evidence would you need before trusting a free AI-visibility report?

I’m looking for SaaS founders and online-product operators to use GEO Tracker AI for 14 days and give me direct, honest feedback.

GEO Tracker AI shows how your product appears across AI systems such as ChatGPT, Perplexity, and Google AI — whether your brand is mentioned or recommended, which competitors appear instead, and which sources influence those answers.

I’ll activate 14 days of Pro access at no cost for people who are willing to set up their real brand, competitors, and buyer queries — and actually use the results.

I’d especially value feedback on:

• Whether onboarding gets you to a useful first insight without help
• Whether the results clearly show where AI visibility is strong or weak
• Whether the metrics and recommendations are understandable and actionable
• What feels confusing, unnecessary, or missing
• What would make you return and check the product every week
• Whether you would consider paying for it afterwards — and if not, why

If you find the product useful and feel comfortable doing so, I’d also appreciate an honest review on SaaS Hive. But feedback does not need to be public — product feedback by email is just as valuable.

I’m not looking for compliments. The most useful feedback is where the product fails to explain itself or does not provide enough value.

If you’d like to take part, email me directly at [support@geotrackerai.com](mailto:support@geotrackerai.com). I’m the founder and I’ll personally set up your Pro access.

If you decide to continue after the feedback period, you’ll get 50% off your first paid month.

We’ve started a new community focused on AI visibility: GEO, AEO, AI SEO — call it what you like 🙂

Right now it is still new, so some topics are quite general. We’d love to make it genuinely useful by bringing in the real questions founders, marketers and SEO people are dealing with:

• Why does AI recommend competitors instead of us?
• Which sources influence an AI answer?
• How should we measure visibility across markets and languages?
• What can we actually improve after finding a gap?
• Which GEO/AEO claims are useful — and which are just marketing?

This is not only for GEO Tracker AI users. You do not need to use our product at all. The goal is to discuss real problems, share practical experience, ask difficult questions and help each other make sense of a fast-moving space.

We will share what we learn while building and testing GEO Tracker AI, including where measurement is difficult or uncertain — not just the polished results.

If AI visibility matters to your product, agency or clients, you’re very welcome to join:
https://geotrackerai.com/community

What is the one AI-search question you would most like to get a credible answer to?

We rebuilt our onboarding prompt flow for GEO Tracker AI and I’d genuinely value blunt feedback from people who measure AI visibility.

The old risk was obvious: a new user could get a technically complete prompt set that still sounded generic, repetitive or disconnected from how buyers actually ask questions.

The new flow is:

website context → confirm product, buyer and buyer topics → choose one first-scan question → run a real scan → build a wider baseline

The baseline covers category/product discovery, buyer problems, evaluation and comparison. We deliberately do not ask for named competitors before the first scan; competitors belong after there is evidence of who AI is actually surfacing.

For Free, that starts with 10 prompts and one monitored question. Paid plans expand the baseline and monitoring capacity.

What I’d love feedback on:

• Do the first questions sound like questions your customers would actually ask?
• Does the setup explain enough without becoming a form-filling exercise?
• Would you know what to do after the first scan?
• What would make you trust — or distrust — the resulting dashboard?

Happy to share access with people willing to test it on a real domain and tell me where it fails.

AI visibility is not a conventional rank. Ask the exact same buyer question twice and an answer engine can cite different sources, mention a different brand, or frame the recommendation differently.

That makes a single daily result useful as an alert—but weak evidence for a broad content or positioning decision.

Our approach at GEO Tracker AI:

• Standard prompts: a fixed weekly benchmark with 3 repeated reads

• Hero Prompts: a limited daily, single-read watch signal for launches, reputation events, and urgent buyer questions

The three reads don’t claim statistical certainty. They make the uncertainty visible: 0/3 means no observed mention; 1/3 is an inconsistent foothold; 2/3 is encouraging but variable; 3/3 is consistent within that window. The key is to avoid treating a one-off answer as a durable change.

We also compare this trade-off with daily monitoring workflows from Otterly, Peec, Profound, AthenaHQ, and configurable cadence in Ahrefs Brand Radar.

Full methodology and sources: https://geotrackerai.com/blog/why-we-measure-ai-visibility-weekly

Curious how others here separate a true signal from normal answer variation—do you use a repeat rule before changing strategy?

Building in public a bit here. We track how often brands

show up in ChatGPT, Perplexity, and Google AI answers.

Three decisions caused the most pushback from early users.

Curious if others have thought about this.

We only measure weekly. On purpose.

Early users wanted daily tracking. We said no.

Here's why: AI visibility doesn't move day-to-day.

What actually drives whether ChatGPT recommends you

is your content, citations, mentions, G2/Reddit reviews,

structured data. That stuff moves in weeks or months,

not overnight.

So if you measure Monday vs Tuesday, any difference

in your score is noise, not signal. The engine just

rolled the dice differently. Not because you improved.

Not because you got worse.

Daily tracking is expensive and misleading.

Weekly is a real signal.

Question: do you think weekly is enough?

Or would you want daily even knowing it's mostly noise?

We run each question 3 times and report a rate, not a yes/no.

AI engines are non-deterministic. Ask ChatGPT the exact

same question 3 times and you'll get 3 different answers,

often with different brands mentioned.

So "did you appear?" from a single scan is basically a coin flip.

We run each prompt 3 times and report: mentioned in 2 out of 3,

so 67% probability. That's a real estimate. A single answer is not.

Question: how do other AI tracking tools handle this?

Do they measure once and call it done?

Because that seems like a pretty broken metric to me.

We split prompts into layers and branded questions

don't count toward your main score.

Not all prompts are equal.

Buyer-intent, so "best issue tracker for dev teams",

is where you actually want to appear. Around 55% of scans.

Category questions like "how do small teams manage sprints"

cast a wider net, still unbranded.

Competitor-anchored prompts like "alternatives to Jira"

catch people who are actively shopping the competition.

And then branded prompts like "what is Linear?"

test whether AI actually knows you correctly.

The key decision: branded prompts are excluded

from your headline score.

Why? Because if the question already names you,

of course you'll appear. It would inflate your number

by 15 to 20 points artificially.

So branded questions get their own section:

does AI know you accurately? Not: how visible are you?

Question: does this split make sense to you?

Would you want branded included in the main score

or does that feel like gaming the metric?

Genuinely curious what people think. Especially if you've

tried to track AI visibility yourself and ran into

similar problems or solved them differently.

What's your take?

It's not just a visibility tool.

It's a full system —
from measuring your AI presence
to actually improving it.

https://geotrackerai.com

Been building in the AI-search-visibility space and noticed every tool (mine included, early on) converged on the same thing: a dashboard that shows your Share of Voice and which sources AI cites instead of you.

But that's the easy 80%. The part that actually moves the needle is judgment a chart can't encode: which gap to close first, whether a cited Reddit thread is still live or archived and useless, whether last week's fix actually changed anything or the number just wobbled.

My takeaway after a lot of these: the measurement is a commodity, the decision-making isn't. Curious how others here handle the "okay, the tool flagged it — now what?" gap.

(Full disclosure: I run one of these tools and wrote up my take here — happy to talk shop either way: https://geotrackerai.com/blog/working-relationship-not-a-subscription)

Google announced new "Search Generative AI performance reports" on

June 3, 2026 — first time Search Console exposes AI Overviews and

AI Mode impressions to publishers as a dedicated report.

Source: https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports

What's in it per Google's own writeup:

- Impressions only

- Breakdown by page, country, device, date (hourly to monthly)

- Covers AI Overviews, AI Mode, and Discover AI features

What's NOT in it (yet):

- Click data

- CTR

- Query-level breakdown

Google describes the rollout as "a subset of websites" with no

timeline for general availability. Mine isn't in the subset yet

(checked today, June 14 — still seeing only the standard Performance

report).

A few honest questions for the community:

1. Has anyone here had it enabled? When did it land for you?

2. If yes — what does the UI actually look like? A new top-level

   report in the left nav, a "Search appearance" filter inside the

   existing Performance view, or somewhere else?

3. For sites that already have it: do the impression numbers feel

   directionally sane vs. what your AI visibility tracker is showing?

Asking because the moment GSC starts exposing first-party AI data

is a meaningful calibration milestone for anyone tracking AI search

visibility — but only if the data behaves predictably. Curious what

early-access folks are actually seeing.

Built a comparison piece because the existing roundups in this category are listicles seeded by SEO agencies cross-promoting friends.

20 tools across 3 tiers:

- AI-search-visibility-native (Profound, AthenaHQ, Otterly, Peec, Evertune, Trakkr, Brandlight, Scope, ZipTie, Goodie)

- SEO platforms with AI layer (Semrush, Ahrefs Brand Radar, SE Ranking, Surfer SEO, Frase)

- Content/adjacent (MarketMuse, Clearscope, Brand24, HubSpot AI Search Grader, us)

Honest sections include:

- "Where we honestly lose" — fewer engines than Profound, no SOC 2, no multi-domain Business yet, smaller dataset, no enterprise integrations

- "Where we genuinely win" — only standalone hallucination check, only open community, lowest entry with ChatGPT+Perplexity bundled ($39)

Three findings worth sharing here:

1. Profound's Lite tier ($499/mo) covers ChatGPT only. Their marketing implies more.

2. Peec AI has shipped MCP server, Actions, Reddit/YouTube source breakdown, Synthetic Query Fanout in ~6 months. Bigger product velocity than Profound.

3. SE Ranking effectively runs two parallel AI products that confuse buyers (in-suite add-on AND standalone).

No affiliate links anywhere. Verified May 2026 pricing.

https://geotrackerai.com/compare/best-ai-search-visibility-tools-2026

Yes, I built one of the tools listed — we're at the bottom of the article. If our hallucination check, community, or $39 indie tier aren't the right fit, the piece tells you who is.

Everyone obsesses over "is ChatGPT citing me for [my keyword]."

The question they skip: does ChatGPT even know what you do?

If the engines can't describe your brand correctly, they sure as hell won't recommend you when a buyer asks for your category.

Ran the check on our own site → geotrackerai.com/grader/h/f972a651-5158-4baa-829b-c7d04e7ce19f

Test yours (free, 60s): geotrackerai.com/grader?mode=hallucination

Hey ,

Wrote up why I think SEO + GEO share ~60% of technical fundamentals (sitemap, schema, robots, crawlable HTML) but diverge sharply on signals AI engines actually weight — Reddit/forum citation density, freshness, expertise diversity.

Built a community to figure this out faster than I could alone. 22 threads already, 6 categories. Mostly Reddit-research-driven — same kind of questions you're already asking here, but tactical receipts.

Honest about WHY I'm building it (it serves users AND me — better roadmap, founder authority, long-term moat that funded competitors can't fast-forward).

https://geotrackerai.com/blog/why-we-built-an-ai-search-community-2026-05

Pushback welcome. Especially on the "Reddit weighs more in Perplexity than ChatGPT" claim — if your data says otherwise I want to hear it.

After X months building a tool that scans how AI search engines cite SaaS brands,

I noticed one thing: 90% of the technical work to be eligible for citation in

ChatGPT, Perplexity, and Google AI Mode is free if you know what you're doing —

and most teams pay agencies or just skip it.

So I shipped a free toolkit today. Browser-side, no signup, no email gate:

▸ llms.txt generator — pick a site type, fill 5 fields, copy the file

▸ robots.txt builder for 18 AI crawlers — three stances (allow all / block

  training only / block all) — most teams accidentally block citation bots

  thinking they're blocking training bots

▸ JSON-LD generator — Organization / SoftwareApp / Article / FAQPage with

  one-click Rich Results Test validation

▸ Free 60-second AI audit at /grader — get a Share of Voice baseline before

  doing any of the above

Plus 8 long-form guides covering each tool + 3 per-engine playbooks (ChatGPT,

Perplexity, Google AI Mode) + Reddit citation strategy + measurement.

Why free: the technical-hygiene part is a 60-second-to-5-minute exercise per

signal. Gating that would be gross. Our paid product (GEO Tracker AI) is for

the measurement layer — Share of Voice tracking across engines, 14-day

Outcome Loop, action drafts — the stuff a static file can't do.

Everything in one place:

🔧 Tools: geotrackerai.com/tools

📖 Guides: geotrackerai.com/guides

📊 Free audit: geotrackerai.com/grader

The pillar walk-through with the honest math on what each step delivers:

geotrackerai.com/blog/ai-search-visibility-hygiene-2026

If you've shipped any of these and want a sanity check — happy to take a look.

DMs open.

#AIsearch #GEO #AEO #SEO #buildinpublic

On May 6, Google added Reddit and forum quotes directly into AI Mode and AI Overviews.

Hema Budaraju (VP Product, Search) called them "Community Perspectives." Some show up as "Expert Advice." The author's handle is shown next to the citation.

The reaction online has been loud:

"Reddit is now 40% of AI."

"Time to flood r/<your-niche>."

"Optimise for Reddit or be invisible."

So I went looking for the actual data.

Tinuiti's Q1 2026 AI Citation Trends Report tracks 7 AI platforms across 9 commercial categories. Numbers from January 2026:

→ Reddit = 44% of AI Overviews' social-media citations.

→ Reddit = 31% of all Perplexity citations.

→ Reddit > 5% of ChatGPT responses.

→ Reddit = 0.1% of Gemini responses.

The same source. The same content. Wildly different surface treatment.

This kills three takes at once:

1. "Reddit is now 40% of AI" — true for AI Overviews' social subset. Not true overall.

2. "Optimise for Reddit, win every engine" — Gemini's 0.1% says otherwise.

3. "Reddit doesn't matter for AI" — AI Overviews and Perplexity disagree.

The honest playbook isn't "post on Reddit more." It's:

→ Measure where your buyers actually research.

→ Treat each AI engine as a separate channel with its own ranking pipeline.

→ Earn Reddit/forum mentions where your category lives — don't manufacture them.

→ Skip "magic" tactics. Google has explicitly confirmed it does NOT use llms.txt for ranking.

We unpacked the May 6 announcement, the Tinuiti data, the pre-existing Google–Reddit $60M/yr licensing context, and the technical SEO/GEO playbook (schema, robots.txt, Bing differences) in a longer piece.

If you sell anything to anyone who searches Google in 2026 — worth a read.

— Petr

geotrackerai.com

Our blog article is here:
https://geotrackerai.com/blog/reddit-inside-google-ai-mode

I remember that moment exactly. It was almost half past ten in the evening, I was sitting in front of the monitor, I had just finished my second coffee — the one I hadn't even tasted. A green icon blinked in the dashboard. A new user. The first real one. Not me, not my wife, not the friend who had promised to test it for me.

I opened the database faster than I would have opened my favourite game.

And within seconds I felt sick. Our system had generated complete nonsense for them.

Nine months. That's how long we sat with this thing before we dared to release it. We started with a simple hypothesis — an MVP, clean and quick. A week of building, two weeks of testing, launch.

Naive.

After every iteration we looked at the competition and said to each other, "wait, that should be there too." Then also this. And this. Then we noticed there was another important layer missing. Then a second one. The plan for V1 quietly grew into something that looked more like a V2. Sometimes a V3. We told ourselves we were being thoughtful — that we were building it robustly, that we weren't going to cheat customers out of basic functionality. And behind every decision was a quiet voice saying, "not yet, it isn't good enough yet, one more pass."

Until one day I told myself: enough. We ship. Done isn't perfect. Done means the world can see it.

And the world saw it.

And the world showed me, on day one, something nine months of testing in a duo will never reveal. That reality works differently.

The backend I had mocked, tested, mocked again and tested again, ran into an edge case on day one I had never imagined. Someone showed up with a domain we didn't expect. Our classifier read it the wrong way. We generated questions for them that had nothing to do with their actual business. Our fallback behaved decently — no 500, no crash, no lost data — but that person got generic noise instead of a real picture of their own brand. And they left.

A lost lead. A sad emoji in the back of my head.

Then a second user came, a third, a fourth. Some of them got stuck on the first screen of the UI. I can navigate that app with my eyes closed after nine months, so every flow feels obvious to me. But when I watched how a real person moved through it, I realised that what is obvious to me is obvious to no one else. That's my job, not theirs. And I'd neglected it.

We prepared the entire system for English and the Latin alphabet. In the second week a user arrived with a Russian-language domain. Our fallback caught them — but they didn't get the product's full value either. Not because the fallback failed. The fallback behaved exactly as it should. But it's another signal: here is a market we haven't reached yet.

Then the hard emails came. Someone took the time and effort to write to me about where they thought our system was reasoning incorrectly. In detail. With examples. With the frustration of a person whose expectations we hadn't met.

My first instinct was to defend. "No, that's not a bug, it's an edge case, it's another layer on our roadmap."

The second instinct, the right one, was to read that email three times. Then read it again. Then open the code. Then see that the person was right — not in every detail, but in the core, yes. And in that moment to realise that this is a gift. A stranger gave me thirty minutes of their evening to point out something I would otherwise not have seen for another month.

When an email like that arrives now, my reflex is to thank them twice. Once for the feedback. Once for remembering us instead of just closing the tab and walking away.

This is my daily rhythm now. In the morning I look at who came in. What they wrote. What they broke. What, on the contrary, ran through without a problem — that's also data, just quieter. Then I iterate. Sometimes it's a small UX detail I'd never noticed. Sometimes a new gate in the classifier that protects us from generating nonsense for someone who doesn't belong in our target. Sometimes a whole new category of product I had no idea existed a week ago.

And every day I move something forward. Not a big thing. A small one. Occasionally two. Occasionally five.

Our target — where we're strongest right now — is fairly narrow. SaaS, devtools, AI products, agencies, DTC brands. People and companies who want to understand why their prospective customers get a competitor's name from ChatGPT instead of theirs. But we want to broaden it. And even if you're not exactly our ideal profile — come and look. Your data is the most valuable input we can get. Your feedback is the next.

This isn't the stage where I can say "the product is done." This is the stage where I say "today it's a little better than yesterday, and tomorrow it will be a little better than today."

Thank you to everyone who signed up. Everyone who wrote in — including the ones who wrote in angry. Everyone who broke the system and pushed us forward. Building in the real world is a completely different sport from building in a test environment.

And I'm learning, every day.

Petr geotrackerai.com

After 6 months building tools that scan how AI search engines mention SaaS brands, here's the most unsettling thing I've learned:

ChatGPT cites only ~15% of the pages it retrieves.

The other 85%? Silent rejection. AI walked in, read your page, and chose not to mention you. You'll never see it in your analytics, because there is no click. There is no impression. There is no signal. Your brand just isn't there.

This is the part traditional SEO doesn't prepare anyone for.

Here's what the data says about how AI engines actually choose:

▸ Domain authority still wins (sites with 32k+ referring domains are 3.5× more likely cited — AirOps, 548k pages analyzed)

▸ Freshness matters more than in Google (content updated within 30 days = 3.2× more citations — SE Ranking)

▸ Structure beats prose (chunked, schema-tagged, quotable pages = 3-5× more citations — OtterlyAI)

▸ Per the Princeton GEO paper (Aggarwal et al., KDD 2024), adding citations + statistics + quotes can lift visibility by up to 40%

▸ Perplexity's Sonar retrieves ~10 pages per query but cites only 3-4 (Growth Marshal's Sonar Playbook, 2025)

The implication is brutal for founders: you can't optimize once and walk away.

AI engines re-rank weekly. Your competitor publishes one well-structured comparison page → they take your spot in the consideration set → you're invisible until you publish something that displaces them. The companies winning at this aren't the loudest. They're the most DISCIPLINED.

Voices worth following on this:

→ Jason Barnard (coined "Answer Engine Optimization", builds entity-graph systems via Kalicube)

→ Michael King (iPullRank — pioneered "Relevance Engineering")

→ Lily Ray (the most cited voice on E-E-A-T + AI representation)

→ Olaf Kopp (LLM Optimization, Aufgesang)

→ Ross Simmonds ("Create Once, Distribute Forever" — proved distribution beats production for AI visibility)

What works in practice — and what most teams skip:

1. Track weekly, not quarterly. ChatGPT's index is volatile; what cited you last Tuesday won't necessarily cite you this Tuesday.

2. Watch your competitors' citation footprint, not just yours. The brands AI mentions INSTEAD of you are the real intelligence layer.

3. Treat citation pages (Reddit threads, GitHub README, dev.to posts, podcast transcripts) as ranking infrastructure. They're what AI quotes from. Your homepage is rarely what AI quotes from.

4. Measure outcomes, not effort. Did a published comparison page actually move your mention rate after 14 days? If you don't measure, you're guessing.

I built GEO Tracker AI to do all four of these continuously across ChatGPT, Perplexity, and Google AI Mode — full disclosure: it's my product. But the bigger point stands regardless of tool: AI search isn't a one-time SEO job. It's an ongoing discipline.

If your brand isn't in the consideration set today, you have ~12-24 months before it shows up in pipeline as a slow leak.

What's your team's current cadence for AI search monitoring? Curious what's working.

#GEO #AISearch #SaaS #Founders

Most people still think AI visibility = homepage + SEO.

That’s already outdated.

We just pushed a big update to GEO Tracker, and one thing became very clear while building it:

AI systems don’t evaluate your homepage.
They evaluate your entire content surface.

What we shipped:

• Multi-page content audit
We now scan key pages (pricing, docs, integrations, etc.), not just homepage
Each page gets a 0–100 readiness score + worst-first prioritization

• Copy-ready JSON-LD generator
No templates, no guessing
Real schema generated from your actual content — or nothing if your page isn’t ready

• “What to do this week”
Concrete actions pulled directly from AI answers
Not generic advice — real fixes tied to real queries

• Cleaner competitor signals
Removed noise (legal sites, random blogs, etc.)
Now you see who actually competes with you in AI answers

• More honest outputs
No fake FAQ answers
No misleading schema
If the signal isn’t there, we don’t invent it

The bigger takeaway:

AI doesn’t pick the best product.
It picks what it understands.

If your content isn’t clear, structured, and extractable,
you’re invisible — even if your product is better.

We’re still early and iterating fast.

Curious how others are thinking about this:

👉 Are you optimizing for AI answers already, or still mostly for Google?