https://preview.redd.it/o0pkbp47gdkh1.png?width=2752&format=png&auto=webp&s=02225aa1080fb3a291796ebb6b6f38d2c5fe2d3b

Been tracking AI answer visibility per-location for clients and wanted to share a pattern that keeps surprising people, because it breaks the assumption that "our AI visibility" is one thing.

The setup: same brand, same buyer-intent prompt, run against the same engine, but varying (a) the location signal and (b) the language of the prompt. Two findings that changed how we think about this:

1. The answer changes by location, not just the ranking, the whole cited-source set.
Ask an engine a category recommendation question as a user in one country vs another and you don't just get a reordered list, you get different brands surfaced and a different set of sources cited to justify them. The model is filtering its retrieval by the location it infers, so each region is effectively drawing from its own corpus of reviews, listings, and local pages. A brand that's the confident #1 answer in one market can be absent in another off the identical prompt. For any multi-location or multi-market brand that means a single national/global "visibility score" is basically meaningless, you're averaging over answers that don't resemble each other.

2. Language of the prompt is a separate variable from location, and it moves the answer independently.
This one caught us off guard. A client operating in Finland: we ran the category prompts in English, then ran the same intent in Finnish. Different answers. Not just translated, different brands cited and different sources pulled. Our read is that the Finnish-language query pulls from a different slice of the corpus (Finnish-language reviews, local pages, local forum/press content) than the English version of the "same" question does, even for a user in the same place. So "location" and "prompt language" are two separate levers, and if you only ever test in English you're blind to what your actual local-language buyers are seeing.

The practical takeaway: if you operate in more than one country or more than one language, you have to measure per-location and per-language, on native-language prompts, not a translated English set. The gaps show up in exactly the places an English-only audit can't see.

Disclosure per rule 5: this comes out of our own tool (sanbi.ai, we do per-location AI visibility tracking), so that's where the data's from, weigh it accordingly. But you can sanity-check the effect yourself for free, ask ChatGPT or Perplexity a category question with different location context, then ask it once in English and once in the local language, and watch the cited sources change.

Curious if others tracking this see the language effect too, or whether it's stronger in some languages than others. My hunch is it's biggest in markets with a rich native-language web (Finnish, Japanese, German) and smaller where the local audience mostly consumes English content, but I only have a handful of markets to go on.

https://preview.redd.it/f7kh1mz58dkh1.png?width=2752&format=png&auto=webp&s=aed3551687b1cebc477e1f2f78b794b8e5513aca

Been tracking AI answer visibility per-location for clients and wanted to share a pattern that keeps surprising people, because it breaks the assumption that "our AI visibility" is one thing.

The setup: same brand, same buyer-intent prompt, run against the same engine, but varying (a) the location signal and (b) the language of the prompt. Two findings that changed how we think about this:

1. The answer changes by location, not just the ranking, the whole cited-source set.
Ask an engine a category recommendation question as a user in one country vs another and you don't just get a reordered list, you get different brands surfaced and a different set of sources cited to justify them. The model is filtering its retrieval by the location it infers, so each region is effectively drawing from its own corpus of reviews, listings, and local pages. A brand that's the confident #1 answer in one market can be absent in another off the identical prompt. For any multi-location or multi-market brand that means a single national/global "visibility score" is basically meaningless, you're averaging over answers that don't resemble each other.

2. Language of the prompt is a separate variable from location, and it moves the answer independently.
This one caught us off guard. A client operating in Finland: we ran the category prompts in English, then ran the same intent in Finnish. Different answers. Not just translated, different brands cited and different sources pulled. Our read is that the Finnish-language query pulls from a different slice of the corpus (Finnish-language reviews, local pages, local forum/press content) than the English version of the "same" question does, even for a user in the same place. So "location" and "prompt language" are two separate levers, and if you only ever test in English you're blind to what your actual local-language buyers are seeing.

The practical takeaway: if you operate in more than one country or more than one language, you have to measure per-location and per-language, on native-language prompts, not a translated English set. The gaps show up in exactly the places an English-only audit can't see.

Disclosure per rule 5: this comes out of our own tool (sanbi.ai , we do per-location AI visibility tracking), so that's where the data's from, weigh it accordingly. But you can sanity-check the effect yourself for free, ask ChatGPT or Perplexity a category question with different location context, then ask it once in English and once in the local language, and watch the cited sources change.

Curious if others tracking this see the language effect too, or whether it's stronger in some languages than others. My hunch is it's biggest in markets with a rich native-language web (Finnish, Japanese, German) and smaller where the local audience mostly consumes English content, but I only have a handful of markets to go on.

not sure if this belongs here or if I'm overthinking it, so tell me if it's noise. it's about underwriting and monitoring franchise / QSR / service tenants, so figured it's at least CRE-adjacent.

context: a growing share of "where should I eat / who should I call / is this place any good" now happens inside AI answers (ChatGPT, Google's AI Overviews, Perplexity, Siri) instead of ten blue links. for the local retail and service tenants a lot of us underwrite, that's a real slice of top-of-funnel discovery. and here's the thing that surprised me: it varies wildly per unit, even inside the same brand.

why it varies is kind of mundane. different engines pull local data from different places:

• ChatGPT leans on Yelp + Bing local
• Google's AI Overviews pull from Google Business Profile + Maps reviews
• Siri routes through ChatGPT / Apple Maps
• Perplexity crawls the web and cites sources

so two units of the same franchise can be described completely differently depending on which of those listings is claimed, current, and well-reviewed at that specific address. the brand can be strong nationally and a specific box can still be functionally invisible or described badly.

where I think this actually matters for us:

1. tenant health / sales durability. for a percentage-rent deal or any tenant where you care whether the unit keeps its sales, per-location review sentiment and AI-answer visibility is a leading indicator that's cheaper to check than a sales audit. a unit whose local discovery is quietly degrading is a unit whose foot traffic may follow.
2. underwriting a franchise tenant. everyone checks the franchisor's financials and the guarantor. fewer people check whether THAT specific location has a claimed, healthy local footprint, or whether it's a dead Yelp/GBP listing that AI models skip. costs ten minutes and tells you something about the operator.
3. brand-level contagion risk if you hold several units of one brand. AI models don't cleanly separate one bad location's reviews from the brand. a few chronically bad units in a system can drag how AI answers generic "is [brand] worth it" questions, which theoretically softens demand across the brand you're exposed to. this one I'm least sure about, would push back on myself here.

the check itself is dumb-simple and free: ask ChatGPT and pull up Google's AI Overview for "best [category] near [address/neighborhood]" and "is [brand] at [location] good," and look at whether your tenant shows up, and how it's described. then glance at the GBP/Yelp review recency for that unit. degrading review recency + absence from the AI answer is at least a yellow flag worth a question to the operator.

honest disclosure: this overlaps with my day job (I work at a company that tracks this stuff per location), so I'm obviously biased toward thinking it matters. which is exactly why I'm posting here instead of assuming, this sub is the right crowd to tell me if it's real signal or if I've got a hammer looking for nails.

so: does anyone actually factor local-discovery health into tenant underwriting or asset monitoring yet? or is it too early / too soft to bother, and NOI and guarantor strength are still the only things that move the needle? genuinely want the pushback.

Most of us here know that standard SEO is bleeding traffic to AI engines like Perplexity, Claude, ChatGPT, and Gemini. If a user asks "best CRM for startups" and your tool or your client is not in the output, you do not exist to that buyer.

The problem is that tracking this manually is impossible because prompt outputs change every hour. Answer Engine Optimization (AEO) agencies are currently charging $3k to $5k a month just to run these checks and write basic content.

I wanted to automate this entire loop for myself and other founders. So I built Sanbi AI. The goal was to make a tool where a single operator could manage their entire AI visibility pipeline in 30 minutes a week, starting at $50 a month.

Here is the architecture of the Growth module we just launched:

• Prompt Monitoring: The system runs buying intent queries into the 4 major AI engines on a strict schedule to account for response variance.
• Source Extraction: It pulls every source URL the AI cited to build its answer (blogs, Reddit threads, news).
• Citation Pipeline: It cross references where your competitors got cited and you did not, dropping these missed opportunities into a pipeline board.
• Automated Drafts: You click a missed citation, and the tool uses AI to draft a context aware reply or post to help you get cited on that exact source domain.

Everything flows through an SSR proxy, and scores are computed live based on citation count, recency, and platform authority.

The core thesis is that operators armed with the right automation can bypass expensive agencies entirely.

I am curious how the builders and marketers here are handling AI search visibility right now. Are you building your own scrapers, ignoring it until the dust settles, or paying for enterprise tracking?

- Sanbi AI

Context: I'm building Sanbi AI, an AEO and AI search visibility platform (flair discloses). The reason I started it: a year ago I watched a small agency owner pay an SEO/AEO consultancy $4k/mo for what was basically a dashboard, a monthly report, and some content drafts. Same month, that same person was using Claude to write proposals, ChatGPT to draft client emails, and Perplexity to research competitors. The disconnect was wild. They were happily self sufficient on hard knowledge work but outsourcing the part that's mostly tracking and templated output.

So I built Sanbi AI around the assumption that an SMB owner with AI tools and roughly 30 minutes a week can do the work an AEO agency charges four figures a month for, if the tool surfaces the right opportunities and drafts the first pass. Entry plan is $50/mo against an agency rate of $3 to $5k. Just launched the Growth module, which pipelines citation opportunities (the URLs where AI engines are recommending your competitor instead of you across ChatGPT, Claude, Gemini, and Perplexity), drafts the outreach and content, and lets the owner work the pipeline themselves. Early beta feedback is encouraging but it's still beta.

The strategic question I keep going back and forth on:

Across marketing categories generally (SEO, AEO, content, PPC management, social), how much of the agency retainer market do you actually think gets eaten by "owner plus AI tool plus 30 min/week" in the next 2 to 3 years?

Three positions I've heard from people I respect:

1. Most of it. Agencies were always selling time and templated outputs. AI collapses both. The retainer model below roughly $10k/mo basically goes away for anything that isn't deep strategy or relationship based.
2. Almost none of it. Owners say they want to DIY but won't actually do 30 min/week consistently. Agencies survive because they're a forcing function as much as a service. DIY tools have been around for years (Ahrefs, SEMrush, HubSpot) and agencies still grew.
3. Splits by category and owner type. Technical and measurable work (rank tracking, audits, AEO monitoring, reporting) gets eaten fast. Creative and relationship work (brand strategy, PR, partnerships) doesn't. And it splits by founder. Operators who already use AI daily will DIY. Owners who don't use AI at all keep paying agencies.

I'm betting on position 3, which is why I priced Sanbi AI for self serve SMBs instead of building another enterprise dashboard. But I'd like a reality check from people who actually run agencies, sell to SMB owners, or have watched this play out in other categories.

For agency owners specifically: are you already seeing churn from clients saying "we're going to try this in house with AI for a while"? And for SMB owners: have you actually pulled work back in house, or is it still mostly aspirational?

Genuinely curious where I'm wrong on this.

I was building a product that uses LLMs to draft responses for forums and blogs where clients are cited, with the goal of increasing visibility. I was testing the system with real client data.

The AI generated a response citing what looked like a perfectly normal Reddit thread about automotive sensors.

I clicked the generated link to verify it. Instead of a technical discussion, I was instantly redirected to a completely unrelated and highly inappropriate subreddit.

After a brief moment of panic, I investigated what actually happened. The AI correctly generated the base URL and the text slug for the automotive post. However, it completely hallucinated the unique string of characters (the ID) at the very end of the URL.

Because of how Reddit routing works, the platform ignored the text slug and redirected the browser based solely on that hallucinated ID. By pure chance, that random ID belonged to an active, very unprofessional post.

If this had gone to production and a client clicked that link, it would have been catastrophic for trust and business.

This made me realize that just checking for a 404 status code is not a viable validation strategy. A link can resolve successfully and still be entirely wrong.

To fix this, I built a specific verification layer that tests and classifies every URL before a human sees it.

The classifier sorts the links into specific buckets:

• verified: The link exists and matches the intended context.
• unverifiable: Blocked by paywalls or forum logins.
• hallucinated: Dead IDs or 404s.
• suspicious: The link works, but there is a redirect mismatch. This is the crucial category that catches the exact error I experienced.

TL;DR: An LLM hallucinated the unique ID of a Reddit URL, which caused a redirect to a bizarre and inappropriate page despite having a clean URL slug. If you are building AI tools that generate links, you must build robust verification systems that check for redirect mismatches, not just broken links.

I was building a product that uses LLMs to draft responses for forums and blogs where clients are cited, with the goal of increasing visibility. I was testing the system with real client data.

The AI generated a response citing what looked like a perfectly normal Reddit thread about automotive sensors.

I clicked the generated link to verify it. Instead of a technical discussion, I was instantly redirected to a completely unrelated and highly inappropriate subreddit.

After a brief moment of panic, I investigated what actually happened. The AI correctly generated the base URL and the text slug for the automotive post. However, it completely hallucinated the unique string of characters (the ID) at the very end of the URL.

Because of how Reddit routing works, the platform ignored the text slug and redirected the browser based solely on that hallucinated ID. By pure chance, that random ID belonged to an active, very unprofessional post.

If this had gone to production and a client clicked that link, it would have been catastrophic for trust and business.

This made me realize that just checking for a 404 status code is not a viable validation strategy. A link can resolve successfully and still be entirely wrong.

To fix this, I built a specific verification layer that tests and classifies every URL before a human sees it.

https://preview.redd.it/c79egro7155h1.png?width=716&format=png&auto=webp&s=d51a4c0b6a10693b149cc35f018692cd775336f3

As you can see in the attached image, the classifier sorts the links into specific buckets:

• verified: The link exists and matches the intended context.
• unverifiable: Blocked by paywalls or forum logins.
• hallucinated: Dead IDs or 404s.
• suspicious: The link works, but there is a redirect mismatch. This is the crucial category that catches the exact error I experienced.

TL;DR: An LLM hallucinated the unique ID of a Reddit URL, which caused a redirect to a bizarre and inappropriate page despite having a clean URL slug. If you are building AI tools that generate links, you must build robust verification systems that check for redirect mismatches, not just broken links.

I was building a product that uses LLMs to draft responses for forums and blogs where clients are cited, with the goal of increasing visibility. I was testing the system with real client data.

The AI generated a response citing what looked like a perfectly normal Reddit thread about automotive sensors.

I clicked the generated link to verify it. Instead of a technical discussion, I was instantly redirected to a hardcore NSFW subreddit.

After a brief moment of panic, I investigated what actually happened. The AI correctly generated the base URL and the text slug for the automotive post. However, it completely hallucinated the unique string of characters (the ID) at the very end of the URL.

Because of how Reddit routing works, the platform ignored the text slug and redirected the browser based solely on that hallucinated ID. By pure chance, that random ID belonged to an active NSFW post.

If this had gone to production and a client clicked that link, it would have been catastrophic for trust and business.

This made me realize that just checking for a 404 status code is not a viable validation strategy. A link can resolve successfully and still be entirely wrong.

To fix this, I built a specific verification layer that tests and classifies every URL before a human sees it.

classifier results

As you can see in the attached image, the classifier sorts the links into specific buckets:

• verified: The link exists and matches the intended context.
• unverifiable: Blocked by paywalls or forum logins.
• hallucinated: Dead IDs or 404s.
• suspicious: The link works, but there is a redirect mismatch. This is the crucial category that catches the NSFW error.

TL;DR: An LLM hallucinated the unique ID of a Reddit URL, which caused a redirect to an NSFW page despite having a clean URL slug. If you are building AI tools that generate links, you must build robust verification systems that check for redirect mismatches, not just broken links.

Hey everyone,

I'm currently refining my LinkedIn cold outreach strategy and wanted to get a reality check from other founders and sales folks who are doing this daily.

There's a classic YC outreach template (see the attached image) that is often held up as the gold standard for cold messaging. It follows a very distinct, multi-paragraph formula:

• The Hook: A highly personalized reference (e.g., referencing a recent blog post or company milestone).
• The Value Prop & Social Proof: A quick intro, what the product does, and dropping notable customer names.
• Team Credibility: Highlighting past big-tech or relevant industry experience.
• The CTA: A direct ask for a demo, proposing a specific day, and dropping a Calendly link.

While this structure is obviously proven for cold email, I’m curious how well it translates to LinkedIn InMails or connection request DMs today.

My questions for the group:

1. Do you still follow this YC-style structure? Is a multi-paragraph message with background and social proof too heavy for LinkedIn, or does the deep personalization still win out?
2. Or do you keep it simple? Do you prefer a much shorter, casual approach—something that just starts with a simple "Hi John Doe, saw you're leading [Team]..." and asks a quick, low-friction question to start a conversation?
3. The Calendly drop: Are you including your scheduling link in the very first cold message like in the template, or do you wait until they actually reply?

Would love to hear what styles, lengths, and CTAs are actually converting for you right now. Thanks!

https://preview.redd.it/z33kn1sjdy4h1.jpg?width=984&format=pjpg&auto=webp&s=a2c87de6a9f9bb2e08794a5048d11dc6229f1b5d

Hey everyone,

I'm currently refining my LinkedIn cold outreach strategy and wanted to get a reality check from other founders and sales folks who are doing this daily.

There's a classic YC outreach template (see the attached image) that is often held up as the gold standard for cold messaging. It follows a very distinct, multi-paragraph formula:

• The Hook: A highly personalized reference (e.g., referencing a recent blog post or company milestone).
• The Value Prop & Social Proof: A quick intro, what the product does, and dropping notable customer names.
• Team Credibility: Highlighting past big-tech or relevant industry experience.
• The CTA: A direct ask for a demo, proposing a specific day, and dropping a Calendly link.

While this structure is obviously proven for cold email, I’m curious how well it translates to LinkedIn InMails or connection request DMs today.

My questions for the group:

1. Do you still follow this YC-style structure? Is a multi-paragraph message with background and social proof too heavy for LinkedIn, or does the deep personalization still win out?
2. Or do you keep it simple? Do you prefer a much shorter, casual approach—something that just starts with a simple "Hi John Doe, saw you're leading [Team]..." and asks a quick, low-friction question to start a conversation?
3. The Calendly drop: Are you including your scheduling link in the very first cold message like in the template, or do you wait until they actually reply?

Would love to hear what styles, lengths, and CTAs are actually converting for you right now. Thanks!

https://preview.redd.it/z33kn1sjdy4h1.jpg?width=984&format=pjpg&auto=webp&s=a2c87de6a9f9bb2e08794a5048d11dc6229f1b5d

For the folks who don't have time, here's why:
Google doesn't exist there. Neither does Reddit. ChatGPT is blocked. Gemini is blocked. Your backlinks don't count. Your press releases don't count. Your PDFs don't count. The crawlers reading your site have names you've never heard of. And the Chinese models answer from training data, not live search, so the content you publish today might not show up for months.
Different internet. Different rules. Different playbook.

Want me to get a bit more technical? Here's the full breakdown.
We ran a brand visibility audit across 6 Chinese tech hubs (Shanghai, Beijing, Shenzhen, Hangzhou, Guangzhou) for a US B2B client. Same prompts, two sets of models: Western LLMs vs. Qwen (Alibaba) and DeepSeek.
The results weren't just different in degree. They were structurally different.

1. Different training corpus, different internet. Qwen and DeepSeek weren't trained on Reddit, Hacker News, or English trade press. They learned from Baidu-indexed content, Zhihu technical columns, eet-china -com, 21ic-com, elecfans-com.

If you're not on those platforms, you don't exist in Chinese AI answers. Your Google ranking is irrelevant.

2. First-party content dominates. Western AEO leans on third-party authority like backlinks, reviews, analyst coverage. In Chinese LLMs, citation analysis tells a different story: your own domain gets cited more than any single trade publication.

Every product page with full specs in crawlable HTML. Every application note. Every design brief. That's the lever.

PDFs don't move the needle. Press releases don't either.

3. Crawlers you've never heard of are already on your site. Baiduspider. QwenBot. DeepSeekBot. ByteDance crawlers.
   Two questions: Is your CDN blocking them? (Many sit on Alibaba Cloud and Tencent ASNs that default firewalls flag.) And are you serving zh-CN localized pages?

A missing hreflang tag is the single most common reason Western brands underindex in Chinese AI.

4. Training cycles, not publishing cycles. Perplexity and Gemini do live web search. You can influence them this week. Qwen and DeepSeek answer mostly from training data.

That means your window to shape outputs is the next training cut, not your editorial calendar. Syndicate to the platforms they index now.

5. ChatGPT is blocked. Gemini is blocked. Measuring "AI visibility" with only Western models for a China strategy is measuring the wrong thing.
   If you sell B2B into China, your AEO strategy needs a separate Chinese LLM track.

It's not a translation problem. It's a different internet.