B2B customers may use GPT. But not necessarily ChatGPT.
Why API-based measurement for AI Visibility might be more important in B2B than it seems at first glance.
When we talk about AI Visibility, there is an assumption that initially sounds completely logical: If I want to know what my customers see in ChatGPT, I have to open ChatGPT, enter my relevant prompts, and then measure which brands appear in the responses. That is exactly why some AI Visibility approaches rely on browser automation or so-called UI scraping. In other words, you simply simulate users as they use chatbots. The APIs of AI models are usable keys that can likewise be used to obtain responses. Here, you can send a prompt directly via an API and receive a response from the respective model. However, the responses are not the same as those you would receive via a browser chat with, for example, ChatGPT. That is why many declare them unusable, since they do not reproduce the responses that you would normally receive as a user.
However, the longer I engage with the topic, the more I question this distinction, at least in the B2B space. Because it is based on an equation that we should question: We equate the interface of a model provider with the interface of the user. Just because someone uses a GPT model does not necessarily mean that this person is sitting on chatgpt.com. And especially in larger companies, this difference could become quite decisive for AI Visibility.
An employee, let us call him Franz, at an industrial corporation could just as easily enter his question into a company-owned AI Assistant. Behind it, there may be an AI Gateway that routes the request to different models or providers. Perhaps it ends up with a model from OpenAI, perhaps Anthropic, perhaps Google, perhaps it is routed differently depending on the task. To the employee, it looks like a single chat window. Technically, however, the interaction takes place via APIs.
This is precisely where the debate about API versus UI scraping becomes much more interesting to me. Because API-based AI Visibility measurement in B2B may not be an inferior copy of reality at all. It could be measuring a second reality that we have so far underestimated.
We confuse ChatGPT with GPT
The difference initially sounds semantic, but it is fundamental. ChatGPT is a product with its own interface, its own features, search functions, personalization, and a specific orchestration in the background. A GPT model, on the other hand, is also available via APIs and can therefore become part of completely different products and enterprise architectures.
This becomes particularly relevant when companies centralize access to generative AI. Microsoft Azure API Management, for example, supports various model backends and API schemas from OpenAI, Anthropic, and Google and provides a unified Model API through which multiple backends can be accessed via a common endpoint. AWS describes a multi-provider architecture that can be used to bundle models from Amazon Bedrock, OpenAI, and Anthropic, among others, behind a central gateway. [1] [2]
For users, all of this is actually invisible. You type something into a chat and receive a response. The company itself decides which model or models from which providers are used. By now, this is a very important question when you look at the individual prices. A lot is happening in the market, and models with good performance are now becoming more affordable as well.
If we want to measure AI Visibility, or brand visibility and all other important metrics in B2B, why are we looking specifically at the public consumer interface of a model provider? In other words, only what we ourselves know privately from, for example, chatgpt.com.
The enterprise internet could look different from the consumer internet
The development toward such gateways is not an exotic infrastructure topic. Several major enterprise technology providers are building exactly this control layer. Google Cloud Apigee explicitly lists Model Abstraction and Multicloud Model Routing as functions of its AI Gateway offering. Datadog likewise describes AI Gateways as a central interface to different model providers, including routing, fallbacks, and governance. MuleSoft also positions its AI Gateway as a central, controlled access point for various LLM providers such as OpenAI, Azure, Google Gemini, and Amazon Bedrock. [5] [4] [7]
I would not want to claim that the majority of all B2B AI searches already run through such gateways today. I am not aware of any reliable, representative figure for this, and that limitation is important. However, there are more and more platforms that allow companies to integrate an enterprise chat with custom-built agents and give them the option to select their own models.
Why should they actually do this? Well, because the needs of a company are different from those of a private ChatGPT user. A company wants to be able to determine which models may be used, protect sensitive information, track usage, control budgets, swap models, and switch to alternatives in the event of outages. Gateways are positioned by the providers mentioned above precisely for these requirements. Many more providers will certainly enter the market. The more profitable the game is, the more companies will emerge, and the better for companies. At the moment, I see the cost problem in the middle. Many companies will not be able to afford it and/or will not want to afford it.
For AI Visibility, this has a consequence that, in my opinion, we still talk about too little. In the consumer space, the question “What does ChatGPT answer?” can be a very useful proxy for actual usage. In enterprise B2B, the more relevant question may eventually become: “What do the models running behind my target customers’ AI infrastructure answer?”
That is not the same thing. And it is not easy to answer either. You also have to know which models are being used. You have a much larger selection of models, which is a dilemma because it also costs quite a bit to run prompts through every conceivable available model and analyze the response. API costs vary from manufacturer to manufacturer and between the individual models of those manufacturers. Many models are relatively inexpensive. It becomes expensive when you want the models to use the internet for research. Then it quickly becomes very expensive.
UI scraping measures a real reality. But only one.
With that, I do not want to downplay UI scraping at all. On the contrary: If I want to know what a user sees today on chatgpt.com, Claude, or Gemini for a specific prompt, then the actual interface is an enormously important measurement point. After all, it contains more than the pure output of a base model.
That is exactly why the claim “API output is the same as ChatGPT output” would be wrong. It is not. Anyone who measures via an API and concludes from this that they know exactly what a user sees in a particular consumer interface is conflating two different systems. For good AI Visibility measurement, this difference should be transparent.
But the reverse conclusion is just as problematic. Just because UI scraping is closer to the use of ChatGPT does not mean that it is automatically closer to the AI usage of a B2B decision-maker. If that decision-maker conducts their research within an employer-provided AI Assistant that accesses various APIs through a gateway, the supposedly “artificial” API world suddenly becomes a very real part of their user journey.
For me, this changes the definition of closeness to reality. There are actually two realities. The measurement method as a whole is the same. The access methods are different.
Maybe we need three types of AI Visibility
I therefore believe that the discussion should be less binary. API or scraping has, for me, become the wrong question because both measurement methods can make different parts of actual usage visible. It would make more sense to distinguish between three levels of AI Visibility.
Consumer AI Visibility describes what people actually see in the public products of ChatGPT, Gemini, Claude, or other assistant systems. If the specific product experience is to be examined here, browser-based measurement is particularly relevant.
Model Visibility, on the other hand, describes how visible a brand is to different models and providers under controlled conditions. This is where API-based measurement becomes interesting because models can be systematically compared against each other, prompt sets can be repeated, and larger volumes of questions can be processed.
Enterprise AI Visibility. It describes how present a company is within the AI infrastructure that professional users actually use for research, evaluation, and decisions. This level cannot be perfectly reconstructed today because we normally do not know the gateway configuration, system prompts, retrieval systems, and routing rules of a specific target company. But conceptually, it may be closer to the business reality that B2B companies are actually interested in.
Sources
[1] Microsoft Azure API Management: AI gateway capabilities, multi-provider model access, governance, and unified Model API. [learn.microsoft.com]
[2] AWS: Multi-Provider Generative AI Gateway, architecture for centralized access to different LLM providers. [builder.aws.com], [aws.amazon.com]
[3] Palo Alto Networks: Prisma AIRS AI Gateway, central gateway layer for models from different providers. [docs.paloa...tworks.com]
[4] Datadog: AI Gateway, cross-provider routing, fallbacks, and governance. [datadoghq.com], [docs.datadoghq.com]
[5] Google Cloud Apigee AI, Model Abstraction and Multicloud Model Routing. [cloud.google.com]
[6] MLflow AI Gateway, unified interface for multiple LLM providers as well as routing and failover. [mlflow.org]
[7] MuleSoft AI Gateway, multi-provider access and centralized governance. [blogs.mulesoft.com]