Your AI reputation is already being built. As a marketer, that keeps me up at night.

We show up. But why?

A few weeks ago I searched "Cloudflare partners Benelux" on Google. Two Point O appeared in the AI overview, described as a partner with a strong focus on the Benelux region, helping companies transition to cloud architecture.

Honestly, my first reaction was: great. My second reaction was: why? What content, what signals, what sources led Google's AI to describe us that way? And more importantly: could I replicate that for our Contentful practice? For Sanity? For the other platforms we work with?

That's the question most existing GEO tools struggle to answer. They tell you whether you are in the answer. They do not tell you what put you there or what to change to get there for a different query.

What Palmata is trying to add

This is where a tool like Palmata fits in. Launched by Contentful, but built for any organisation thinking seriously about AI discovery, it goes beyond the visibility layer that tools like Promptwatch and Rankshift cover well.

Where those tools ask "are we showing up", Palmata tries to answer "why are we being represented this way, what is driving it, and what do we prioritise first." It combines brand research, competitor analysis and simulated impact modelling so you can see the effect of a change before you commit time and budget to it.

It also ships with an MCP server, which means its research can feed directly into the content workflows your team already uses. For a marketer, that is the detail that actually matters. Insight without workflow integration is just a dashboard you check once and forget.

Is Palmata the final answer? I don't know yet. But the question it is asking is the right one.

Why content architecture is not a technical detail

Here is what I have learned working at a digital agency focused on composable architecture: the brands that show up well in AI-generated answers are not necessarily the ones with the biggest content budgets. They are the ones whose content is structured clearly enough for an AI system to interpret it accurately.

Answer engines do not crawl your website the way Google does. They are trained on large datasets and retrieve additional context at query time, pulling from sources they consider authoritative and consistent. When a model tries to describe what Two Point O does, it's looking for clear, consistent signals across our website, third-party mentions, industry directories, and what competitors say about the category.

Vague or inconsistent content creates noise. If your homepage says one thing, your product pages say another, and your blog uses a third set of terminology, an answer engine has no reliable signal to anchor a description of your brand. It will average out the confusion into something generic, defer to a competitor, or leave you out entirely.

This is the technical reason why composable architecture is not just a delivery advantage. It's increasingly a discoverability advantage too. Clean content modelling, consistent taxonomy, structured data, explicit relationships between content types. These are not just good CMS hygiene but they are the foundations that make your content legible to AI systems. Whether you are running Contentful, Sanity, or any other headless platform, the principle is the same.

Three questions worth asking now

As a marketer, I have started asking these questions about Two Point O itself. But they apply to any organisation thinking seriously about AI discovery:

1. 1

   How is your brand currently described by answer engines? Not what you say about yourself, but what ChatGPT, Gemini and Perplexity say when a prospect asks about your category.

2. 2

   Does your content give answer engines enough to work with? Thin pages, inconsistent messaging, and missing evidence are as much of a problem for AI discovery as they are for traditional search.

3. 3

   Is your content architecture set up to support this? Structured, modular content is easier for answer engines to interpret accurately. If your content lives in a monolith or a legacy CMS, that is a real disadvantage, one that gets harder to fix the longer you wait.

What this means for us at Two Point O

We help organisations build content architectures that are ready for where digital is going, not just where it has been. The Cloudflare AI overview result was a good reminder that the work we do on structure, clarity and consistency has consequences beyond the channels we can directly measure.

Palmata gives us a new lens for that conversation. Not as a tool we are selling, but as a way of making the connection between content architecture decisions and AI discovery performance visible and actionable.

GEO is not a replacement for SEO. It's an additional layer, and one that rewards the same thing good digital work has always rewarded: clarity, consistency, and content that is built to be understood. That's something we think about every day at Two Point O, and something I am increasingly curious to explore with the people we work with.