Vertical AI

There are computer programs meant for everyone, and there are those built for a single professional group. Vertical AI belongs to the second kind. The term describes programs with artificial intelligence that are tailored to exactly one industry. One example: a program that only summarizes medical findings for radiologists, and nothing else. The counterpart is called Horizontal AI – tools like ChatGPT, which anyone can use for almost any purpose. “Vertical” here refers to the vertical slice through the economy: one sector, but covered in full depth.

Why industry knowledge is worth more than general knowledge

A general chat program can talk about medicine, but it doesn’t know the forms used by a German clinic. It doesn’t know what information a health insurer requires, and it bears no liability for anything. This is exactly the gap that specialized providers address. They don’t sell intelligence in general, but a ready-made solution for a concrete work step.

For investors, this is interesting because such providers are harder to replace. Whoever has spent years collecting contract data from the real estate business owns something a new competitor cannot quickly rebuild. Experts call this a moat: an advantage that keeps imitators at bay. Added to this are switching costs: if a law firm has tied its entire file management to a program, it won’t switch just because of a better price.

A common misconception is that Vertical AI is simply a narrow market. The target audience is smaller, but it pays significantly more per user. A tax advisor will unhesitatingly spend three-figure sums for a tool that saves them ten hours a month.

What lies behind the industry solution

Most of these companies do not train their own language model. A language model is the large base program that understands and generates text; building one costs hundreds of millions. Instead, providers rent an existing model and build their work around it. The actual value lies in this wrapper.

The wrapper first includes access to specialized data. The model is supplied with the matching documents for every question – laws, guidelines, the customer’s price lists. This way it answers from verified sources instead of from memory. Added to this are connections to the programs already running in the industry, such as a doctor’s practice management software.

The third building block is controls. In regulated professions, no result may go out unchecked. That’s why many providers insert a review step in which a human confirms or corrects it. In addition, the origin of every statement is logged. This sounds unspectacular, but it’s often the very reason a customer is allowed to buy at all.

From the doctor’s office to the stock market announcement

In everyday life, one usually encounters Vertical AI without hearing the term. In some practices, the doctor no longer types up the conversation themselves – instead, a program writes the entry into the record. Insurers have damage photos automatically assessed. In law firms, tools search through thousands of pages of contract material for risky clauses.

In business news, the word mostly appears in connection with funding rounds. When a startup describes itself as “Vertical AI for logistics,” it means to say: We are not just another chatbot, we have a fixed customer base. Analysts then usually ask about the same points – how many customers stay, and how deeply the software is embedded in their workflows.

Still, one shouldn’t read the term as a seal of quality. Some providers merely put a nice interface on top of someone else’s model and charge a lot of money for it. The interesting question is always what’s left of the solution once the underlying model improves on its own next year.