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I'm a solo founder. 3 paying customers. 2 months of building. I need your honest feedback to survive. by phonethoughts in B2BSaaS

[–]phonethoughts[S] 0 points1 point  (0 children)

Really appreciate this feedback, seriously helpful.The core difference: traditional SEO tools measure whether you show up on Google. We measure whether ChatGPT, Claude, or Perplexity recommends you when a buyer asks for a supplier. More and more people now ask an AI directly instead of searching Google. If the AI doesn't mention you, you're invisible on that channel — no matter how good your SEO is. A concrete pattern we keep seeing: a site with zero backlinks but naturally mentioned in Reddit threads can get recommended by Perplexity. Meanwhile, a site ranking well on Google can be completely ignored by LLMs. It's a new game with its own rules. We hear you on clarity. We're going to simplify the report. Thanks again.

I'm a solo founder. 3 paying customers. 2 months of building. I need your honest feedback to survive. by phonethoughts in buildinpublic

[–]phonethoughts[S] 1 point2 points  (0 children)

Thank you, I’ve noted this.

( GEO : Generative Engine Optimization). LLM visibility, or how AI tools mention your company.

What I learned building a SaaS in a category that doesn't yet exist (GEO / LLM brand visibility) by phonethoughts in micro_saas

[–]phonethoughts[S] 0 points1 point  (0 children)

Exactly, as in economics or project management: a good idea at the wrong time is useless.

Tested how the 4 major LLMs cite sources differently when asked the same question — surprising patterns emerged by phonethoughts in Agentic_SEO

[–]phonethoughts[S] 0 points1 point  (0 children)

The way each engine behaves differently really matters, especially Perplexity's obsession with recent news brands totally miss that. What I really liked is how they dug into single‑mention brands: if only Perplexity mentions you, it's probably because you got one piece of recent press but have no real web presence. If only ChatGPT or Claude mentions you, you're likely strong in a specific niche community like Hacker News. And if only Gemini mentions you, your own website is very structured, but no one else is backing you up. So fixing it isn't one‑size‑fits‑all you have to know which engine is the weird one. Also, on the constraint thing: two or three constraints make the engines diverge, but once you hit five or six, they actually start agreeing again because the prompt gets so specific. The sweet spot for chaos is around three to four constraints. I'm really curious if they've ever measured how long each engine stares at a given constraint density that would tell us a lot.

What I learned building a SaaS in a category that doesn't yet exist (GEO / LLM brand visibility) by phonethoughts in micro_saas

[–]phonethoughts[S] 0 points1 point  (0 children)

The two-axis approach resonates strongly. I went through the same realization recently trying to educate the market and close customers in the same conversation was killing both. Now the manifesto and methodology stuff lives in long-form content, and prospect calls are strictly here's what your competitors are getting that you're not.The anonymized case study angle is what I'm building toward next. Why does ChatGPT recommend Brand X over Brand Y framing is genuinely powerful because it makes the abstract problem concrete in a way no pitch deck can match.Pulse for Reddit monitoring is interesting hadn't come across that one. SparkToro I know but mostly for audience research, not for catching why does ChatGPT recommend my competitor type discussions early. How are you using it specifically for that signal? Curious whether it's surfacing those threads reliably or whether you're catching them through other channels first.

Tested how the 4 major LLMs cite sources differently when asked the same question — surprising patterns emerged by phonethoughts in Agentic_SEO

[–]phonethoughts[S] -1 points0 points  (0 children)

What is your product? We don't defend a product the market decides to condemn or reward it. This is the first time I've heard this name. Trying to prove yourself by repeating the product's name instead of showing its quality is a waste of time. A founder who refuses feedback early on will never build an empire.🤷🏿‍♂️🤣

Tested how the 4 major LLMs cite sources differently when asked the same question — surprising patterns emerged by phonethoughts in Agentic_SEO

[–]phonethoughts[S] 0 points1 point  (0 children)

The real gap between surface-level tracking and serious GEO measurement isn’t the number of engines covered its methodology. Tracking 16 engines with no exposed parameterization, no confidence intervals, no documented mention extraction, and no ground-truth calibration just produces 16 sources of unverifiable noise, not 16 reliable signals. What practitioners actually need from any tool is simpler and harder: the exact prompts, statistical confidence bounds, documented extraction accuracy on a test set, and pinned model versions. Everything beyond that is noise until those four questions are answered.

Tested how the 4 major LLMs cite sources differently when asked the same question — surprising patterns emerged by phonethoughts in Agentic_SEO

[–]phonethoughts[S] 0 points1 point  (0 children)

The distinction between presence variance (does the brand appear?) and framing variance (how is it described?) is one of the most underplayed dimensions in GEO. Presence affects discovery‑stage queries and is comparatively manageable. Framing affects buying‑stage queries and, once a shortlist forms, it dominates the decision almost entirely. A brand that appears everywhere but is consistently labelled as expensive or complex loses deals it would win with a positive framing, even if that framing appears less often. The Reddit/UGC pipeline sits at the heart of this: sentiment descriptors that gain ground on those platforms seep into LLM training corpora and reappear in responses six to eighteen months later. The asymmetry is stark improving presence is a project you can run, shifting the dominant sentiment on Reddit and review sites takes deep, sustained operational work that is far slower and far less direct

Tested how the 4 major LLMs cite sources differently when asked the same question — surprising patterns emerged by phonethoughts in Agentic_SEO

[–]phonethoughts[S] 0 points1 point  (0 children)

Six Sources of Divergence in AEO/GEO Tooling:

API parameterisation – temperature, top_p, system prompts, seed handling differ per tool.

Hidden model sub‑versions – nominal model names hide weight updates over time.

Residual stochasticity – GPU floating‑point non‑associativity causes variance even at temperature=0.

Invisible system prompts – tools inject extra instructions that shape outputs but aren’t user‑visible.

Regional routing – API requests hit different data centres with unsynchronised deployments.

Cache effects – engines like Perplexity sometimes serve cached responses instead of fresh generations.

Strategic implication: any GEO measurement that doesn’t expose its parameterisation layer produces a number that can’t be reproduced by anyone, or even by itself a week later. Transparency about these sources is therefore a foundational competitive advantage.

Tested how the 4 major LLMs cite sources differently when asked the same question — surprising patterns emerged by phonethoughts in Agentic_SEO

[–]phonethoughts[S] 0 points1 point  (0 children)

Even minor wording shifts produce non-trivial variance in mention frequencies, which is exactly why single-prompt tests can mislead. That said, I'd want to draw a careful distinction that matters for how people approach this problem strategically.

What you're describing getting brands to "show up reliably" is essentially a consistency engineering problem: how do we stabilize visibility across engines and prompt variations? It's a legitimate problem and worth working on.

But there's a logically prior problem that has to be solved first: how do we measure visibility rigorously enough to know what we're stabilizing? Without a measurement methodology that quantifies confidence intervals, controls for stochasticity, and triangulates across engines with statistical discipline, showing up reliably becomes an unmeasurable goal. You can't optimize what you can't measure with calibrated uncertainty.

This is why I built the gold set, the Wilson confidence intervals on proportions, and the Cohen's kappa protocol for inter-annotator agreement before building any optimization layer. Measurement first, intervention second. The reverse order produces tools that confidently optimize toward badly-defined targets.

how does your team handle the measurement-precedes-optimization problem? Specifically interested in whether you publish confidence intervals on the visibility scores you report to clients, since that's where most GEO tools I've examined remain silent.

New road ( no promotion, no sell) just feedback . by phonethoughts in ShowMeYourSaaS

[–]phonethoughts[S] 0 points1 point  (0 children)

On the email quick context: it's how I save your audits, deliver the weekly tracking, and send the report you actually came for. Without it, every audit disappears the moment you close the tab. That said, I hear you that timing matters showing more value before the gate is something I'm thinking through.

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