findable 2.0: Helping Agency Clients Rank #1 on ChatGPT, Google, and Claude

Generative AI is reshaping how people find information. More and more users are skipping search engines entirely and going straight to ChatGPT, Claude, or Gemini — treating whatever the AI says as the final word. This means whether a brand gets mentioned or recommended by these AI models is becoming a new discipline in marketing. findable 2.0 is built precisely to fill that gap.

What Is findable 2.0

findable 2.0 is an AI visibility optimization tool built for marketing agencies. It launched on Product Hunt under three categories: Marketing, SEO, and Artificial Intelligence. Its core promise is bold and straightforward — help agency clients rank first on ChatGPT, Claude, Gemini, and traditional Google.

In other words, findable takes the logic of traditional SEO (Search Engine Optimization) and extends it to what's now being called GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization). Rather than serving individual brands directly, it targets the people who serve brands — marketing agencies — helping them manage and improve the AI visibility of multiple clients at scale.

Why Agencies Need This Kind of Tool

Traditional search is built around rankings and clicks: users search, see a list of results, and choose what to click. In an AI conversation, users typically receive a single synthesized answer — and the model decides which brands to mention and which sources to cite. If a brand isn't part of that answer, it's effectively invisible at this new entry point.

For agencies, this creates a two-sided pressure. On one hand, clients are starting to ask questions that simply didn't exist before: "Where do we rank in ChatGPT?" On the other, there's no mature methodology yet for measuring or optimizing AI visibility. Tools like findable create value by turning this vague demand into something trackable, deliverable, and reportable to clients. That's exactly why it targets "agency clients" rather than end brands directly — agencies have a persistent, structural need to optimize across multiple clients and produce reports.

It's worth noting that different AI products use fundamentally different information retrieval mechanisms, which directly affects how transferable any optimization strategy can be. ChatGPT (especially with browsing enabled) and Perplexity pull from live search indexes, so traditional content authority and backlink strategies still carry some weight. Claude in default mode relies on its training cutoff, meaning it's slower to recognize emerging brands. Gemini is deeply integrated with Google's search infrastructure, so its performance is closely tied to how well a brand is indexed by Google. This heterogeneity means "one strategy fits all models" is technically very challenging. If agencies want to deliver cross-model visibility reports to clients, they need tools that can probe each platform independently and attribute results separately — not just produce a single blended score.

The Industry Shift from SEO to GEO

The emergence of findable 2.0 is itself a signal of a paradigm shift in search marketing. For the past two decades, SEO revolved around keywords, backlinks, page structure, and rankings. As AI becomes the primary information gateway, the optimization target shifts from "search engine crawlers" to "the training and retrieval mechanisms of large language models."

This raises a new set of questions: How does a model decide which brand to mention? How do you increase the probability of being cited? How do you optimize simultaneously across multiple models — ChatGPT, Claude, Gemini — each with its own preferences? findable attempts to provide a unified dashboard for all of this. That said, it's worth staying grounded: the "recommendation logic" of AI models is fundamentally a black box, and vendors update their systems frequently. Any tool claiming to reliably deliver a "#1" position deserves healthy skepticism. Ranking at the top is more likely an ongoing, dynamic process than a one-time fix.

The core difference between GEO and traditional SEO lies in the nature of what's being optimized. Traditional SEO targets rule-based crawler algorithms — Google ranks content based on relatively transparent signals (link authority, page speed, keyword density, etc.) and publishes algorithm update documentation. GEO, by contrast, deals with the "emergent" knowledge representations of large language models: these models don't crawl pages in real time. Their knowledge comes from the statistical distribution of training data, plus some models have RAG (Retrieval-Augmented Generation) capabilities that let them pull from external indexes at inference time. This means a brand's "presence" in a model depends both on how it was described in training data — and how often — and on what real-time retrieval sources the model calls during reasoning. AEO (Answer Engine Optimization) is another common label for this space, with a slightly stronger focus on voice assistants and AI Q&A scenarios; the two concepts overlap heavily and no industry consensus exists yet. Currently, the GEO space lacks anything like Google Search Console as an official measurement tool. Brand exposure data can only be estimated through probe-style queries from third-party tools — which is precisely the gap that findable and similar products are trying to fill.

Market Performance and Positioning

findable 2.0 received 23 upvotes and 1 comment on Product Hunt, ranking 15th on its launch day. By the numbers, this is a modest launch — no viral breakout — but the space it's entering, AI visibility optimization, is heating up fast. One of the makers is Peter M. Buch. The fact that this is a 2.0 release signals that it's not a raw proof-of-concept but a more mature product that's already gone through iteration.

For marketers anxious about how their clients can remain visible in the age of AI search, findable offers a reasonable starting point. Before committing, though, it's worth evaluating three things carefully: How comprehensive is its model coverage? Are the optimization recommendations actionable? And is the reporting data transparent and credible enough? In a field where the methodology is still being written, a tool's real effectiveness has to be tested against actual projects.

Summary

findable 2.0 represents a new category that's still taking shape: shifting the focus of marketing optimization from traditional search engines to generative AI. It serves agencies, covers major large language models, and centers on visibility improvement — responding to a real and increasingly urgent market need. Whether its "rank #1" promise can fully deliver depends on how the AI search ecosystem evolves and how technically deep the product actually is. For anyone tracking AI marketing trends, it's at least a compelling case study in how the GEO space is beginning to take practical form.