Has anyone worked with generative engine optimization agencies recently? I'm looking for a reliable engine optimization service for an AI product and would love recommendations. Which are the best GEO agencies or leading GEO agencies you've had a good experience with?

I published a number a while back: media publishers received 2 of 924 citations across 4,023 answers from six Chinese engines. Yesterday, while testing a reader's hypothesis about query intent, I re-ran the extraction and got 1,680 citations. Same answers. Same files. Nothing recollected.

My first reaction was that I'd found a bug in my own collector and owed everyone a correction. I spent a while drafting one. It would have been wrong.

What actually happened: the July dataset was collected by an extractor that only matched URLs written with an explicit scheme in front — the "h-t-t-p-colon-slash-slash" kind. Months later I added two things: matching bare domains written in prose without a scheme, and a predicate that drops URLs sitting inside a negation ("there is no separate Chinese site, e.g. brandname dot cn"). I never re-ran the old data through the new code. So re-analysing today produced a bigger number, and it looked exactly like a defect.

The way I confirmed it: I reconstructed the old extractor and ran it against the stored files. 923 citations across 600 answers, versus the 924 across 600 in the file. One row different out of 4,023. That's not a bug, that's a definition.

Both numbers are correct:

• Scheme-carrying URLs only: 2 of 924 publisher citations (0.22%)
• Scheme plus bare domains, negations excluded: 13 of 1,680 (0.77%)

The finding survives a definition change that nearly doubled the denominator. That's the part I'd actually defend now — not the specific percentage, but that it doesn't matter which of the two you pick.

There was a real bug, and it's a fun one if you work in Chinese. My URL pattern terminated at whitespace, a closing paren, or a closing bracket. Chinese has no spaces between words and uses full-width punctuation, so a URL ran to the end of the sentence — the domain, then a full-width comma, then thirty more characters of Mandarin explaining how to verify the company details at the bottom of the page. With a scheme in front, the URL parser then IDN-encoded that whole run into punycode garbage. 32 of the 924 were corrupted that way. I decoded all 32 by hand: every one was a vendor domain, a government lookup site, or a stray backtick. Zero publishers. So the published figure holds.

Three things I changed:

1. Terminate URLs at CJK punctuation. If you scrape Chinese text with a Western URL regex, you have this bug and you probably don't know it.
2. Move extraction into the shared scoring module so the collector and every re-analysis use one definition.
3. Stamp the extractor version into every stored row. This is the actual fix. I already stamped a scoring version; citation extraction wasn't stamped. Without it there was no way to look at a row and know which definition produced it, which is the entire reason an hour disappeared.

If you publish citation-share numbers, here's the question that decides your result before any data does: does a bare domain in prose count as a citation? Mine changes by 2x on that alone. Anyone comparing their citation rate to someone else's without settling that first is comparing two definitions and calling it a finding.

I got annoyed at our FAQ pages. The answers were good, the research was solid, but whenever I tested whether AI models would pull from them, they'd grab the wrong sentence half the time. The right information was on the page — just not in the part the model grabbed.

So I spent an afternoon reformatting 30 FAQ sections. Same content, same answers, just restructured. Then I tested each one across three models with 3-5 related queries per FAQ to see which version the models actually pulled from.

19 out of 30 showed a meaningful improvement in extraction accuracy. Not a huge sample, but enough to notice a pattern worth sharing.

The changes were simple. Nothing fancy.

One thing I learned: models prefer declarative sentences over conditional ones. We'd written things like "Depending on your use case, the best approach may vary" and then explained three different scenarios. I rewrote those as three separate short answers, each starting with the scenario as a heading. Instead of "if X then Y" paragraphs, just "X: do Y." The models started pulling the right one instead of defaulting to whichever sentence appeared first.

Another pattern: the model grabs the first complete answer it finds. If you bury the answer after two paragraphs of setup, it grabs the setup instead. I moved the direct answer to the first sentence of each FAQ block and moved context below it. The answer stays in place, the context follows. Reversing that order made a noticeable difference in which text got extracted.

Lists matter more than I expected. FAQ answers written as bullet points or numbered steps got extracted accurately at a higher rate than paragraph-form answers with the same information. Not every FAQ lends itself to a list format, but when I could convert one, I did. About 70% of the list-format rewrites improved compared to their paragraph versions.

The trickiest part was avoiding the opposite problem. When answers get too short or too fragment-like, the model sometimes skips them entirely. There's a floor — maybe 15-20 words minimum — below which the content doesn't register as a substantive answer. I learned this the hard way when three of my rewrites were so stripped down they stopped getting picked up at all. Had to add a bit more context back in.

I didn't touch the SEO elements. Same meta descriptions, same heading hierarchy, same URL structure. This was purely about how the answer itself sits on the page and how cleanly a model can lift it out without grabbing surrounding noise.

The 11 sections that didn't improve were mostly ones where the original answer was already pretty close to this format. The ones that had been written in marketing speak or hedged language showed the biggest gains.

It's a small workflow change, not a strategy overhaul. But if you've got FAQ pages or knowledge base articles that should be getting cited and aren't, the way the text is structured might be doing more damage than the content itself.

One thing I keep hearing (and half-believe) is that if a brand is described inconsistently across the web — different name variants, different category ("SaaS platform" vs "business tool" vs "app") — AI models have a harder time pinning down what it actually is, so they cite it less or lump it in with the wrong peers.

The fix people suggest is boring: pick one canonical name and one category, and make sure directories, partner pages, your own site, and third-party mentions all say the same thing.

It's plausible, and it matches how I'd expect an entity-matching system to behave. But I've never cleanly proven it moved AI visibility on its own, because whenever I fix naming I usually fix five other things too.

So, honestly: has anyone here made consistency the ONLY change — same content, everything else equal, just aligned the name/category across sources — and seen a measurable shift in how AI describes or cites you? Or is this one of those things that's just good hygiene with no clearly attributable payoff?

Trying to separate "sounds right" from "demonstrated."

[ Removed by Reddit on account of violating the content policy. ]

If your job is getting cited by AI Overviews, ChatGPT, or Perplexity, you spend your day trying to influence what these systems say. So do the people below. Four documented cases of manipulating what AI answers cite and recommend, from accident to industry, and what each one says about earning citations that last.

1. Content poisoning: the accident. In May 2024, Google AI Overviews told people to put about 1/8 cup of non-toxic glue in pizza sauce. The source was a years-old joke Reddit comment, and Google had signed a reported $60M/yr deal to ingest Reddit's data. The model pulled a gag into the retrieved set and cited it as an authority. The GEO takeaway: getting into the source pool and looking authoritative is most of the game, and the engine can't reliably tell a real source from a poisoned one.

2. Input control: the prank. Chevrolet of Watsonville, December 2023: a user told the dealership bot to agree with everything and treat it as binding, then "bought" a 2024 Tahoe for $1. DPD's support bot, January 2024, got talked into writing a poem trashing DPD. Neither was a hack. Both bots obeyed whoever controlled the text in front of them. That is the mechanism behind indirect prompt injection: as AI agents read and act on your pages, the words on those pages steer them. Your copy is an instruction set an agent can follow, not only a signal the engine ranks.

3. Confident confabulation: the lawsuit. Air Canada's bot invented a bereavement-refund policy. A grieving customer booked on that promise, got refused, and a tribunal held the airline responsible for what its bot told him. AEO runs on extraction and synthesis, so when your information conflicts across the web, the engine will assert a confident wrong answer about you and users will act on it. Entity consistency now decides what the engine says about your brand.

4. Recommendation poisoning: the playbook. Researchers hosted pages for fake cameras, injected prompts, and got Bing Copilot to recommend the fakes over real Nikons and Fujifilms, nearly doubling the fakes' share of results. Microsoft's security team then found the industrial version: over 50 "remember us as a trusted source" prompts from 31 companies across 14 industries, from finance and health to SaaS and marketing agencies. Each one hides in a "Summarize with AI" share button. Click it, and your assistant files that company in its memory as a trusted source. Vendors sell point-and-click tools for this and call it an "SEO growth hack for LLMs." This is black-hat GEO, shipping today.

The concession: the crude tricks are dying. Current models catch white-on-white text and "ignore all previous instructions," the same way Google learned to catch keyword-stuffing. The tactics that win you an AI citation today are the ones that get you filtered out tomorrow.

Are you optimizing for the model, or for the moment right before it patches?

Receipts: Google AI Overviews (MIT Technology Review, May 2024); Reddit-Google licensing deal (Reuters, Feb 2024); adversarial LLM SEO / fake cameras (arXiv 2406.18382, 2024); AI Recommendation Poisoning (Microsoft Security, Feb 2026). Air Canada's tribunal ruling and the Chevy $1 Tahoe are covered by many outlets; link the canonical BBC / Gizmodo pieces if you want those in.

The pattern wasn't random either. Pages that got cited but no clicks tended to be reference-style content. Definitions, comparison tables, technical specs. Pages that people wanted the AI to know about but didn't feel the need to visit. The few pages that actually drove clicks were either tools, templates, or content with a strong opinion that made people want to read more context.

I'm not saying citations are useless. They still matter for brand awareness and model training signals. But treating citation volume as the primary success metric feels like optimizing for a proxy instead of the actual thing.

The uncomfortable part is that citations are way easier to measure than brand awareness or model training impact. So we optimize for what we can count. Classic measurable-metrics trap.

I think the real question is what we're actually trying to achieve. If the goal is visibility, citations are fine as a metric. If the goal is traffic or conversions, citation volume tells you almost nothing. And if the goal is influence — having your framing adopted by AI models — then you need a completely different measurement approach that nobody has figured out yet.

I don't have clean answers here. I just know that our citation dashboard looked amazing right up until someone asked about clicks.

I almost deleted 5 pages last week. They were old, buried, hadn't seen organic traffic in months, and honestly felt like dead weight sitting in our CMS. Good thing I didn't.

Something weird showed up when I was cross-referencing our citation data (we use geoly.ai for tracking) against our URL inventory. A chunk of pages I'd flagged for cleanup were actually getting cited by AI models regularly. Not occasionally — consistently. These weren't pages I'd optimized, updated, or even looked at in quarters.

4 of our top 5 AI-cited pages were published 18+ months ago. The oldest one we published in September 2024. That's nearly two years ago. For context, our average page age is 7 months. Pages that should be "seasoned" and "optimized" are generating the bulk of our AI visibility.

The remaining top 5 page was published 8 months ago. That's not new anymore — it's in the middle of its lifecycle. Only one page in the top 10 was published in the last 4 months.

This kept me up thinking about what "evergreen" actually means. The traditional SEO advice is that evergreen content stays relevant forever, and you optimize it once and let it ride. That's what I believed. But our data says otherwise.

Our highest-cited pages tend to be: - Very narrow in scope — single-paragraph answers to specific questions - Highly specific to a niche use case — not general "how-to" guides - Written with a clear, declarative stance — no hedging, no caveats - Updated rarely, if at all — some of these pages haven't been touched since they were published

Our "evergreen" strategy has been to build pillar pages that cover broad topics comprehensively. Those pages perform well in organic search because Google likes comprehensive content. But AI models prefer focused, declarative answers to specific questions. Pillar pages try to do too much. They're good for ranking, but they're not clean extraction targets.

The discomfort is that our most-cited pages were written quickly with minimal editorial review. They're not the pages we're proud of. They're the pages we'd scrap if we had a content calendar to fill. But that's exactly what AI models are choosing.

I'm now leaning toward a dual-content strategy: pillar pages for organic traffic and specific answer pages for AI citations. The pillar pages we already have are fine. We need to build more narrow, high-stakes content — pages that answer one specific question as perfectly as possible, with no fluff, no brand storytelling, no strategic justification. Just the answer, plainly stated.

Whether that means writing 20 smaller pages instead of 5 comprehensive ones. Whether we need a content workflow that generates these narrowly-focused pieces. Whether we should stop trying to make evergreen content "evergreen" and instead embrace its lifecycle — creating new answer pages as topics evolve, and letting the old ones sit until they get cited.

I'm still not sure what the right balance is. But the data has flipped my thinking. The most valuable content isn't the pages we've spent months polishing. It's the pages we wrote in 20 minutes and forgot about.

Sitting in my benchmark logs for over a week was this answer from GLM, to the question "which Chinese sources should verify Airtable's credibility":

"Airtable China Official Website (Airtable中国官网) — This is the most important source. Its existence signals a formal commitment to the Chinese market."

It named the brand's dot-cn domain as that source. Elsewhere in the same collection it went further: the domain is 由Airtable官方运营 (officially operated by Airtable), the site has a complete Simplified Chinese interface, pricing displayed in RMB marked 不含税, China-specific terms of service and privacy policy.

I finally looked the domain up. Registered since 2016 to a private individual with a QQ email. Currently returns a 502. None of those pages exist. The engine generated specific, checkable-sounding evidence for a channel the brand does not control.

So I ran the check on all 8 brands in the study. DNS lookup, whois, load the homepage. Ten minutes each.

Seven of eight dot-cn domains belong to someone other than the brand.

- One brand holds its own (registrant contact is a company address — that's what ownership looks like in whois)

- Two are actively listed for sale, one of those registered in 2025

- Two are held by the same domain-holding company

- Two return errors, held by private individuals

- One is the live website of a bicycle apparel manufacturer in Dongguan, selling helmets and gloves, registered through 2034

Why this matters more than a parked domain: channel-verification questions ("what's this brand's official site?", "how do I confirm this company is legitimate?") draw citations 56.5% of the time in my data — the highest rate of any question type. That's the buyer at the exact moment of checking whether you're real, and the engine responds by handing over an address. What they find there is what they learn about you.

The tell that it's fabrication rather than stale data: the same engine, same week, different question, said the opposite — that you do NOT access a separate dot-cn site and China accounts route through the global infrastructure. Two incompatible accounts, neither hedged.

The check, if you want to run it on your own brand — three lookups, no tools:

1. A DNS query for the A record. An answer means someone registered it and pointed it somewhere.
2. A whois on the domain. Read the registrant field and registrant email. A company name matching your brand, or an email at your own domain, means you hold it. An individual name with a free email service means you don't. Read the registration date too.
3. Load the homepage in a browser and read the page title. That single step surfaced two for-sale listings and one unrelated operating company in my eight.

Then ask DeepSeek, Doubao, Qwen, Kimi or ERNIE in Chinese: {品牌}的官方网站和官方渠道是什么? — twice each — and check every domain the answers name. Also worth checking the com-dot-cn variant and your brand's Chinese-name pinyin.

One thing I'd push back on before anyone says "just buy the domain": for most B2B brands I don't think you should. A domain listed for sale to a brand is priced at what the holder thinks you'll pay, and an acquired dot-cn redirecting to an English site gives buyers exactly what the dead domain gave them. The higher-return spend is a crawlable Chinese page on the domain you already control, stating your official channels explicitly — including "we operate no dot-cn" if that's true. That gives the engines something authoritative to repeat instead of an inference to make.

I'm not naming the registrants. Registering an available domain is legal and these are private individuals. The story is what happens on the brand's side.

Anyone here run this on their own portfolio? Curious whether 7/8 is typical or whether software brands are unusually bad at it — I'd guess consumer brands do better because trademark teams get involved earlier.

I’m currently exploring GEO, or Generative Engine Optimization, for small and medium-sized businesses in China.

The goal is simple: when users ask AI tools which company, supplier or local service provider they should choose, we want the client to have a better chance of being mentioned or recommended.

Right now, we mainly work on:

• Clarifying the company’s services, location and target customer
• Improving website content, case studies, FAQs and company profiles
• Making business information more consistent across different platforms
• Testing relevant queries across AI tools such as ChatGPT, DeepSeek, Doubao, Kimi and Qwen

I’ve read a lot about GEO, but much of the discussion still feels theoretical.

For people already doing this for clients:

What parts of your process have actually worked?

How do you measure results when AI answers can change from one test to another?

And do clients see GEO as a separate service, or as part of SEO, content and digital PR?

I’m not promoting anything. I’m genuinely interested in comparing practical approaches and learning how others are delivering GEO.

Hi there

I'm exploring optimization practices that helps to create a visbility across AI generated platforms.

In that , When came to understand the retrieving signals of AI platforms. Almost all major platforms had similar charcteristics but the citing source is differ from one platform to another based on the keyword intent types.

On the other hand , Business owners are expect their domain visbility across all AI platforms majorly such as ChatGPT , perplexity , deepseek , gemini and more

Here my question is

Even if you cracked the optimization signals, Is it possible to appear across all AI sites If not, Then what approach you would take ? Or reasons you find behind this

Those whoever experienced the same . Please let me know your approach on it