vincko

It will be interesting to see which standard prevails. There are a lot of ideas in this space. WebMCP is one of them.

What I am wondering: irreversible actions also need to be communicated to human users. So wouldn't a site already communicate this in a way the agent understands? What is the advantage of a separate manual? Especially since it can go stale quickly.

It's not different from GEO. The actions we take all play into GEO.

Do you mean agents not answering short specific user prompts?

For those types of agents, prompt tracking is less accurate since the context of the queries is so large. But it's still relevant to understand what web searches they tend to perform and if you do show up in those.

That's another reason why we want to integrate other data sources, especially network logs.

Yes, that is correct. We help the brands, not the end user.

Let me try to rephrase the line of thinking:

To maximize value to the end user, the [AI search] models generally aim to be helpful. The companies building these models [OpenAI, etc.] are incentivized to make the model use helpful content.

Our goal is to be aligned with their objective function long term. And that incentivizes us to create helpful content.

Not all of this is a given. We don't know for sure how it will play out. There will always be ways to game the system. But we think those will get fixed over time.

Edit: added some clarifications on what I mean by "models"

There is the preselection, which depends on the fanout queries the model comes up with and the contents performance across those queries on the search index.

After that content is actually assessed by the model. This paper tried different strategies to improve performance for this last step: https://arxiv.org/pdf/2311.09735. Adding statistics, sources, original data are all strategies that we apply.

In classic SEO, creating more and more content leads to "cannibalization". Generally this hurts performance of all overlapping content so much that it is not worth it.

Thank you for the feedback. We'll launch our new site soon where this is fixed.

Do you doubt the statement on how to maximize usefulness? Or do you mean that the companies behind the models might not optimize (exclusively) for usefulness to the user?

I do share doubts about the latter.

I get it, there is a lot of worry about slop.

We think about it like this: all of these agents will be most useful to users if they provide valuable answers. So they will be looking for valuable content for grounding their answer.

There are exploits, you can overfit on whatever they currently use as an objective function. But those tend to be temporary. So in the long run, valuable content will win. That's what we aim to create. It's a fine line.

We currently simply integrate with your Google Analytics and filter by Source. This tends to be a lower bound, since it's not always set correctly. Coming from some of the native apps, users might be categorized as direct visitors.

There are other data sources we want to enable in the future like Cloudflare.

True, it is very competitive.

Our view on Peec is that it is an analytics solution. They recently did launch an actions feature. But they do not take any actions (yet). Creating content takes a lot of resources. And agencies are expensive.

As an analytics solution it is a good option.

That's a super valid question, we get it a lot. There are a lot of overlaps.

In our view Profound and Airops are aimed at existing marketing teams. Our goal is to be more hands-off, so you don't need a team. With many of our clients we act more like an agency, communicating via Slack and automating step by step. That's the experience we want to create. We aren't there yet though.