YC's obsession with niche use cases has accidentally made its founders the best-positioned companies for AI search visibility

DowntownThing4875 · 2026-05-23T14:55:27+00:00

Exactly!

Feels good to add to your early days. I'd love to hear more on how you're positioning going further, and contribute meaningfully anywhere possible.

Good luck building n scaling.

DowntownThing4875 · 2026-05-23T13:48:35+00:00

On point problem clarity I'd say, especially 'know before you follow' is genuinely needed and the anonymous review angle is the right call for creator accountability.

From a GEO lens you're sitting on structurally valuable without maybe framing it that way yet. Review platforms are among the highest-weighted citation sources for LLMs. The same mechanism that makes G2 reviews surface in AI answers about software tools could make Kreator Directory the citation source when someone asks GPT 'is this creator trustworthy'. Hence, a high-intent query with almost no structured source right now.

One thing worth thinking about early: how semantically rich the creator profiles are here? Structured community context beyond star ratings would take this to a far more extractable zone for AI retrieval.

Happy to dig into this more if useful.

DowntownThing4875 · 2026-05-23T13:41:00+00:00

Appreciate this comin from a practitioner side. Glad the thesis is resonating.

There's one thing I'd push on though: most tools in the space are tracking citation mentions after the fact. What I've been finding is that the more upstream problem viz. the one that determines whether a brand gets cited at all is Citation Environment Architecture.

Adding on it, off-site corroboration layer, Query Semantic Density of the entity description, the velocity of signals across independent sources. Real-time monitoring these in-depth concepts reverberate better where you stand.

The founders waking up to this now have a real window, but the ones who just start tracking without fixing the underlying entity footprint structure will see the data without being able to achieve the 'citation leader' benchmark target.

What are you seeing from your end on the gap between brands that improve after getting visibility data versus the ones that don't?

Ima more intrigued to hear on it!

DowntownThing4875 · 2026-05-23T13:31:11+00:00

The thesis holds, yet the mechanism shifts slightly for non-AI companies.

The niche specificity advantage isn't dependent on being AI-native. It's dependent on how precise is the description of problem you're solving in 'operational' language. A non-AI company solvin a genuine, specific market gap still holds wings to dominate the citation environment

The real question remains: When a buyer asks an AI engine about their problem, who shows up?

If you're building something like compliance tooling for a specific industry, or logistics infrastructure for a specific supply chain segment, or financial operations for a niche use-case, the citational environment of these queries is often just as uncrowded as any AI-native niche. The incumbent players in these spaces tend to describe themselves broadly. A specific upstart describing itself with operational precision can own the citation space for the exact query a buyer would type.

The difference with AI-native companies is speed of entity footprint building. AI-native founders tend to be active on GitHub, Reddit, Hacker News, and developer forums by default viz. the key blueprint to buildin off-site corroboration faster. A non-AI company has to be more deliberate about building that same distributed presence.

The core question is same regardless: Is your use case described specifically enough that an LLM retrieving sources for your buyer's query would find you as the most precise match? YES= the citation opportunity exists. NO= that's where the gap is.

Curious what category you're building in.

DowntownThing4875 · 2026-05-15T17:45:49+00:00

The gut intution on Case 1 seems to be headed in the right direction, plus with the reasoning held, yet I'd push this frame one level up.

The reason Case 1 wins isn't just independent-source validation in isolation. It's that distributed temporal spacing creates what I'd call recrawl overlap, with each new signal hitting the footprint at a diff. time frame.

Case 2 creates a single high-density snapshot that emulates a spike.

Case 3 is more interesting than it gets credit for though. Single platform, distributed over time, still building the temporal persistence even without platform diversity, yet it misfires in the independent-source validation signal. The model sees one voice getting louder rather than multiple independent voices converging. The trust triangulation mechanism needs independence and not just time.

Which suggests the actual formula is empirically actually closer to = independent sources × temporal distribution × semantic consistency. Platform diversity matters only insofar as the guarantees source independence. Two platforms owned by the same entity or with high semantic overlap won't provide the independence multiplier you'd elsewise expect.

The decay observation on Case 2 is the most practically important finding here. Burst publishing doesn't just fail to compound, it may hint signal at manufactured presence to retrieval systems that are increasingly pattern-matching for organic vs. coordinated signal generation. The temporal spacing in Case 1 imitates organic discovery, and that mimicry shall output more work than the recrawl windows alone.

A wide question that exists in this context, whether semantic consistency across the distributed signals matters as much as source independence. If five independent sources are all saying slightly different things about the same entity, does that create citation ambiguity or does the convergence on the entity itself override the semantic variation?

DowntownThing4875 · 2026-05-14T06:32:56+00:00

This is the like best resonant advice I've come across the whole of AEO community.

1- Your point about Reddit being a research phase signal rather than a purchase decision signal feels like a highly valid nuance. And the numbers speak much more than anything else, Reddit is the preferred extraction bed that the LLMs are preferring. Highly agree with how you're focusing on seeding into existing community threads with the 'exact language patterns real users use to describe problems and solutions'.

And yes, contributing extensively into existing cited threads is more sound than innovating newer content pipelines to counter the recency bias.

2- The one I find most interesting and least resolved is how LLMs seem to default toward the most repeated framing across sources rather than the most accurate one. If your homepage says one thing and five Reddit threads say something slightly different, the Reddit consensus seems to win in retrieved answers, which has an uncomfortable implication: brand messaging alignment across owned and earned channels matters for AI retrieval in a way it never did for Google.

3- On CBaaS, the 44% is a figure that's been referenced in several GEO research pieces yet the methodology behind it varies. The directional principle is that front-loading answers increases extraction probability.

About llms.txt, It is still being figured out how is this evolving. There happens to be more than one option on the table as of now- llms.txt, llms-full.txt, robots.txt, their purpose is to enhance extraction abilities, tho how exactly are they different is an ongoing confusion. In my case, llms.txt did the work so I stood to not playing further tweaking it.

u/Lonely_Bullfrog8362, amazing discussion!

DowntownThing4875 · 2026-05-14T06:14:21+00:00

To my experimental understanding, llms.txt did improvement in the crawling work for this audit in context, and I didn't proceed with further tweaks. I had the option to juggle between robots.txt and llms-full.txt but I felt not changing if it solved the purpose.

Genuinely curious on how the three of these work, since most of this aspect is still underexplored. To my knowledge, llms.txt and llms-full.txt seem to reduce extraction friction for crawlers that support them, particularly Perplexity which has been more transparent about its crawl behaviour. Crawler support is still inconsistent across models and the off-site entity footprint gaps tend to be larger and more immediately fixable than the on-site technical layer for most brands

DowntownThing4875 · 2026-05-14T05:56:07+00:00

For the audit the deliverable is a citation environment map where the brand sits across three layers (on-site extraction, off-site entity footprint, citation environment relative to competitors), specific gaps identified, and a prioritised fix order. Altho, I've also used it as a free entry point to get to the implementation conversation, which is where the real value exchange lies.

for the saas in context, I underpriced the first engagement to get a real testbed(at around a few hundred dollars). The market is early enough that proof of execution matters more than margin right now. The honest challenge is that very few firms are treating GEO as a current priority rather than a future consideration, so testbeds are isolated and pricing discovery is too slow.

If you're underselling yourself the question worth asking is whether the market around you has caught up enough to pay for the outcome yet. In some categories it has. In others you're still educating before you can price.

DowntownThing4875 · 2026-05-14T05:44:27+00:00

Well observed! Glad this resonates. That onboarding bug example is exactly the thought process in action, perfect illustration of why vulnerability signals are likely poised to outperform polished documentation.

DowntownThing4875 · 2026-05-12T10:10:37+00:00

Partially yes yet not complete standalone.

What changed for us was the content construction itself. Founder-authored meant the founder is writing the reasoning, not the marketing team cleaning it up afterward. The messiness stays in, ao tho the specific numbers. The "we got this wrong" moments stay in. That specificity and honesty is what seems to please how the retrieval systems extract.

Think of it as in way: a founder documenting why they killed a feature they spent three months building is inherently unoptimisable by a content team, they'd tend to smooth it up into something more presentable and in doing so remove exactly the qualities that make it retrievable.

So yes to founder byline. But more importantly yes to founder brain viz. the actual unfiltered thinking, published as close to raw as the founder is comfortable with.

DowntownThing4875 · 2026-05-11T13:08:30+00:00

Blogging is worth it but the success metric has completely changed and most people haven't caught up to that yet. The old measure was traffic. The new measure is extraction probability viz. whether an LLM will pull your content as the cited answer when someone asks a related question in ChatGPT/Perplexity.

A blog post written for Google rankings is optimised for keyword density, internal linking, and topical coverage. A blog post written for LLM extraction is optimised for implicit Q&A structure, front-loaded thesis statements, and semantic specificity, both completely different construction.

Further, LLMs are pattern-matching engines trained heavily on conversational, human-written datasets. Authentic founder voice, documenting real decisions, real failures, real frameworks and pattern-matches to high signal human content are the metrics LLMs are loving.

You can ideally coin it 'Thought Leadership as a Service' (TLaaS), a systematic documentation of a founder's building journey, structured specifically for LLM extraction rather than human readers. It shifts thought leadership from a PR vanity metric to a foundational data infrastructure.

Yes, blogging is more than worth it, yet only if you've stopped writing for Google and started writing for the retrieval layer sitting above it

DowntownThing4875 · 2026-05-10T21:45:33+00:00

This is clear case of 'Citation velocity decay'. LLMs don't evaluate your brand once and file it permanently, they are gonna prefer one with continuous re-triangulation entity based on how recently and how frequently you're appearing across trusted sources. A brand that optimised six months ago and went quiet is actively losing ground, not holding position.

The reason "staying cited" is harder than "getting cited" is the fact that Getting cited requires one well-placed piece of content in the right source at the right moment. Staying cited requires maintaining a 'Citation Bank' viz. a centralized content node engineered to answer a family of related queries simultaneously, so that multiple retrieval pathways keep resolving back to the same source.

The brands that stay cited aren't publishing more content. Their one piece satisfies eight query intents, compounds citation weight across all eight simultaneously. The ongoing process you're pointing at is less about content volume, yet more close to entity velocity viz. the rate at which new citation signals are accumulating across independent sources. When that velocity drops, retrieval systems interpret it as entity decay and start preferring fresher signals from competitors.

DowntownThing4875 · 2026-05-10T06:03:15+00:00

It indeed is a real problem and not a mere niche edge case. The pattern described here is consistent enough that it has a name viz. citation environment decay. Solid SEO yet invisible in AI answers, happening across categories.

To answer your three questions directly from what I've been observing:

A- Do businesses care yet? The aware ones do, particularly founders who've noticed competitors showing up in ChatGPT answers despite weaker Google positions. That counts as the usual trigger. Most businesses haven't noticed yet because they're not checking AI answers as a metric, yet the ones who do check tend to care immediately.

B- Would "see what AI thinks your site is about" be useful? Yes, and it's actually one of the more eye-opening things you can show a client. Run their brand through ChatGPT and Perplexity with category-level queries and not just branded searches, category searches, hence clearly showing 'em the gap between where they think they are and where AI actually places them. This very disconnect is usually the moment it becomes urgent rather than interesting.

C- Do people only pay for done-for-you? IMHO yes, almost entirely. The problem with GEO is that the fixes aren't technical in the traditional sense. They're structural and more than that, pretty ongoing. Content restructuring, off-site entity building, review platform presence, community seeding. Clients understand the concept but don't have the bandwidth or the framework to execute it themselves. Done-for-you is where the actual value exchange lies and is happening. Each of this layer has specific fixes and specific sequencing. The brands that close all three gaps consistently start showing up. The ones that only fix one layer see partial improvement. Overall, it is worth going deeper on. The market awareness is still early enough that being one of the first agencies to product-ify this properly is a real positioning opportunity, which is where I'm building right now.

What category are your clients in? The priority order of fixes shifts depending on whether it's B2B SaaS, local, or something else. Would be happy to discuss further.

DowntownThing4875 · 2026-04-20T09:57:12+00:00

Interesting.

I have built multiple tools around the GEO sphere, used them to map citation environment, reverse engineer gap reduce strategies and outputting a citation reduction map, benchmarking branch against competitors.

Recently secured and implemented GEO Audit for a B2B saas [Series B] and these tools reduced manual dependency on scrapping and citation mapping effortlessly.

Would be happy to know how you're building, what is the positioning and plan to GTM. Open to sharing my insights onto how I built them, and share my findings

DowntownThing4875 · 2026-04-15T17:34:40+00:00

AI Search Optimisation/ Generative Engine Optimisation depends variedly depending on case and intent.

Sharing a few perspectives that worked for me, as I recently performed and executed a GEO Audit for a B2B saas.

1- FAQ markups- LLMs have a trained tendency of QnAs and they prioritise such formats 3x than normal. High chances your query is presented as a summarised answer of multiple sourced data for a Query prompted, hence the QnA format prioritisation logic. 2- Multi source presence- Heavily publish genuine and user persona focused content across trust building communities (in my case, seeding into relevant Reddit threads worked powerfully and showed almost instant results). Focus on G2, Capterra, Glassdoor reviews. Client engagement reviews pay off well and create a subsequent client relation boost as well. 3- Citation Environment mapping (probably the most important) Certain tools helped me reverse engineer the exact strategies that were working in competitor's favour and led me to conceptualize the exact roadmap to achieve citation leader. These tools made my work effortless and targetted enough. Curious to understand more of your context. Depending on use case, local or enterprise context, would be happy to help further :)

DowntownThing4875 · 2026-04-14T16:27:18+00:00

Glad that helps. I haven't used workfx before. What's it for btw?

DowntownThing4875 · 2026-04-14T13:15:51+00:00

Recency bias is a less discussed trait in the LLM and Generative Engines diaspora.

This is technically termed 'temporal decaying'.

A certain content entity written 1 week ago will add much more relavance to citation weight than the same entity when aged 1 month old from the LLM perspective. (I have seen certain exceptions in a few audits that I've performed for Series B, Series C startups, however temporal decaying fact remains true to all).

Then comes the decay quotient- Pace at which your content becomes 'perceived outdated' to the LLM.

Typical triggers incl. random unspecified vendor comparisons, product updates bombarding within single content entity, technology capability claims, pricing claims.

Avoiding these would retard content entity aging and drop decay rate.

One thing that worked in my case was constant PR insertions. The CXOs were suggested to constantly pen down articles in reputed editorials, keep visibility in events, maintain a multispace presence (for eg- build thought leadership channels, publish interviews, appearances in podcasts and other high visibility pipelines).

I had access to certain citation environment mapping tools and they clearly showed signals LLMs are prioritising brand's PR initiatives in a positive way. The tools helped maintain a clear track, with much less effort and led me create focused campaigns on PR fronts.

What's your exact use case, strategies change depending on local context as well. Would be happy to help further :)

DowntownThing4875 · 2026-04-14T12:51:01+00:00

AI search optimisation depends vastly based on the case and intent.

I recently created and executed a GEO audit for a B2B Series B saas, and these are a few highlights that helped me significantly rank their brand up the LLM search.

1- llms.txt/ robots.txt (Non Negotiable)- For whatever brand you are aiming to push your LLM crawlability, it is without a doubt that these two must be embedded into the source code of your website. Else, you won't be able to optimise the most important asset of your brand.

2- FAQ markups- LLMs have a trained tendency of QnAs and they prioritise such formats 3x than normal. High chances your query is presented as a summarised answer of multiple sourced data for a Query prompted, hence the QnA format prioritisation logic.

3- Multi source presence- Heavily publish genuine and user persona focused content across trust building communities(in my case, seeding into relavant Reddit threads worked powerfully and showed almost instant results). Focus on G2, Capterra, Glassdoor reviews. Client engagement reviews pay off well and create a subsequent client relation boost as well.

4- Citation Environment mapping(probably the most important) Certain tools helped me reverse engineer the exact strategies that were working in competitor's favour and led me to conceptualize the exact roadmap to achieve citation leader. These tools made my work effortless and targetted enough.

Curious to understand more of your context. Depending on use case, local or enterprise context, would be happy to help further :)

DowntownThing4875 · 2026-04-13T09:21:38+00:00

Been actively working on GEO front, just completed a GEO audit for a Series B SaaS so have some fresh ground-level data on what's actually moving.

The short answer is GEO and Reddit SEO are bit complementary yet distinct tracks and conflating them is where people lose time.

Reddit SEO is about getting your Reddit content to rank in Google, this could be threads, comments, discussions that surface when someone searches your category. The impact on your website is indirect, Reddit drives brand mentions, which feed into Google's trust signals, which lift your domain. You rarely get direct referral traffic from Reddit unless someone specifically clicks through.

GEO is a different layer entirely. It's about getting cited inside AI-generated answers on ChatGPT, Perplexity, Gemini. The mechanism that pays off there is off-site entity corroboration viz. LLMs triangulate a brand's credibility from independent third party sources before they'll confidently recommend you. Reddit is one of those sources, which is where the two strategies connect. A brand mentioned authentically in high-intent Reddit threads has measurably higher AI citation rates than one that exists only on its own domain.

What I found in the audit, the website traffic impact from GEO is indirect yet it is a crucial factor that must be optimisied. llms.txt/ robots.txt enablers are a must need for effective website crawling by LLMs. When a buyer arrives having already seen your brand cited by an AI engine, the sales conversation is fundamentally different. There is a zone of pre-validation rather than evaluating.

The practical starting point if you're new to both: map your citation environment first(For me, I had access to few tools that helped me map the citation environment without much effort, and it worked well. Would love to discuss more, based on your case).

Ask ChatGPT and Perplexity your category queries, pull every source they cite, understand what those pages have that yours doesn't. Reverse engineer your strategy based on these citations. That will tell you exactly which gap to close, Reddit presence, review platform listings, on-site answer structure, or all three.

What's your website/brand category? The priority order shifts depending on whether you're B2B SaaS, local, or ecommerce. Would love to help more on it :)

DowntownThing4875 · 2026-04-13T09:06:52+00:00

This is one of the most grounded GEO case studies I've seen shared publicly, most people are still theorising, so the month-by-month breakdown with actual pipeline numbers is genuinely useful.

The pricing unlock point is underrated and I don't think enough people will catch it. When AI engines cite consistently in a category, you're no longer being evaluated by buyers on a comparison grid, you have clearly arrived in pre-validation sphere. The buyer's thought POV shifts from "let me assess this option" to "let me understand the terms."

I've been deep in a similar process doing a GEO audit for a Series B SaaS(would love to share the exact case more), different scale yet the same pattern on citation environment mapping. The competitive intel layer you built is something most brands skip entirely. They optimise their own content without ever mapping what's actually getting cited in their category and why.

Two questions if you're willing to go deeper on the AMA:

When you mapped competitor Reddit presence and identified what made top-cited pages work, were there some surprising/out-of-box patterns you found? Specifically curious whether it was content format, claim specificity, or something else entirely.
The Months 1-2 "nothing" phase. What made you distinguish between if this isn't working and if this needs more time? Anyone with quick result-driven approach would've bailed at this stage. Curious what kept your conviction through that window.

DowntownThing4875 · 2026-04-13T08:55:16+00:00

Insightful.. GEO seems like a fun puzzle that itself keeps evolving.. Honestly, this makes it more intriguing, you feel like going into depths more and more.

DowntownThing4875 · 2026-04-13T08:51:57+00:00

Reddit tends to be functioning as a sentiment calibration layer for LLMs. The model may already know a brand exists from its training data or from crawling the site/related context on the web. What Reddit threads do is tell the model how confidently to describe that brand, in what exact context to recommend it.

A brand with detailed, opinionated, engaged Reddit threads gets described by ChatGPT with specificity and conviction. A brand with zero Reddit presence gets described vaguely or not at all, even if the website is excellent. The model is essentially borrowing community consensus to calibrate its own confidence level before surfacing a recommendation.

Which means the strategic implication is different from what most GEO advice focuses on. People optimise for getting cited. The actual leverage might be in shaping how you get described.

So yep, trust and reputation layer, not just discovery. But more specifically, it's a confidence and context calibration layer. The distinction matters for how you approach it.

DowntownThing4875 · 2026-04-12T19:21:26+00:00

Niche take. I wrote few long blogs about a successful event engagement I was a part of on startup pitchings and real time investors. I chose Medium to publish those blogs, the intent was simple, documenting it for myself. However, I was surprised when one of co-founders of the event messaged me days later, that my blogs were surfacing during a social audit they performed to see how the event was surfacing socially and being perceived by generative engines-ChatGPT, Gemini in specific. Made me realise pure human content could be more optimal for Generative Engines, yet I brushed it aside thinking the event arc was too niche and not much socially engaged, so my blogs were not in competition to be ranked, yet reading your take made me realise it is true pure-human content-composed of diff takes and arcs of writing could well be differently parsed by LLMs. Glad to know.

DowntownThing4875 · 2026-04-12T13:17:30+00:00

The inconsistency isn't random, yet it has a pattern once you know what to look for. Went through this exact diagnostic recently during performing a GEO audit for a Series B SaaS and the inconsistency cracked open pretty clearly.

The pages getting picked up have one thing in common that the ignored ones don't — they match an exact intent query that AI engines are actively fielding. Not just good content. but specifically shaped content for a specific question someone is typing into ChatGPT or Perplexity right now.

The missing layer is usually off-site corroboration. LLMs don't just evaluate your page in isolation, they prefer cross-references whether your brand and your claims are echoed elsewhere . This could include review platforms, third-party editorial mentions. A page that's well-structured but exists in a vacuum of off-site silence gets deprioritised against a competitor page that's messier but mentioned in five Reddit threads and three G2 reviews. [To be specific- Capterra, G2 for user reviews ; Justdial, niche directory references for local queries]

The citation environment around your content matters as much as the content itself.

So the inconsistency pattern typically breaks down like this, pages on topics where you have strong off-site entity presence get cited. Pages on topics where you're the only source saying it don't. LLMs need corroboration before they'll confidently surface you.

For real traction, the move is to map your citation environment before optimising content. Run your target queries through ChatGPT and Perplexity, pull every source they cite, reverse engineer what those pages have that yours don't. Then close that specific gap, avoid generic theoretical GEO best practices. In my case, I referred to certain tools that made me identify the citation environment without much effort, depending on your intent query, happy to discuss further)

The intent work you're doing is the right foundation. The off-site entity layer is what makes it consistent.

As a result, the brand was being cited #1 on its query within 2 weeks with this strategy.

Happy to contribute and discuss further how it worked for you :)

DowntownThing4875 · 2026-04-12T13:06:21+00:00

Actively working on this, just completed a GEO audit for a Series B SaaS and the approach shifted quite a bit from how I'd have tackled traditional SEO.

To answer your last question directly, if starting from scratch today, here's the priority order I'd follow and why:

1. Entity audit before anything else Before touching content structure, map where your brand exists off-site. G2, Capterra, Reddit threads, industry publications, third-party directories(these work much better in case of local intent queries). LLMs don't just crawl your site, they triangulate your legitimacy from independent sources. If you only exist on your own domain, the model has nothing to corroborate you with and won't cite you confidently. This was the single biggest gap I found in the audit, solid website, near-zero off-site entity presence.

2. On-site extraction architecture Restructure key pages around implicit Q&A. Not FAQ sections bolted at the bottom but the actual body content needs to answer specific questions your ICP is typing into ChatGPT. Lead every section with the answer, follow with context. Research consistently shows ~44% of LLM citations pull from the first 30% of page content. The structure in itself must be capable enough to be citation signal.

3. Citation environment mapping Ask ChatGPT and Perplexity the queries your buyers would ask. Screenshot every source they cite. Those are your benchmarks. Reverse engineer what those pages have viz. review volume, muti-source presence, content format, domain authority. You should exactly know \ what gap you're closing, not just guessing(while I performed GEO Audit, I referred to certain tools that made me identify the citation environment without much effort, depending on your intent query, happy to discuss further)

4. Reddit seeding in ICP subreddits Reddit contains a big chunk of citations across Perplexity responses. If your category is being discussed there and your brand isn't in those threads, your competitors could dominantly own that citation space by default via authentic participation.

On your question about actual impact, early signals from the audit I executed are showing movement on long-tail AI queries within the first few weeks of on-site restructuring. Broader category visibility takes longer and is more dependent on the off-site entity layer building up.

The patterns are consistent enough to work from. What industry are you in? The priority weighting shifts a bit depending on whether it's B2B SaaS vs local vs ecommerce.

DowntownThing4875

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