What Is Vertical AI? How VCs Judge AI Startups
Have you ever introduced your company as "an AI startup" and watched an investor's interest fade? Now that almost every company says it uses AI, that label alone no longer tells anyone what you actually do.
Vertical AI is AI built to solve a specific problem inside one industry, such as finance, manufacturing, or healthcare, using that industry's data and workflows. The daglo team put this guide together from talks we recorded and summarized with daglo at an AI infrastructure event (NVIDIA and Azwell's AI Acceleration Day), cross-checked against official sources. Here is how one venture capital speaker said VCs judge AI startups today.
What is vertical AI?
Vertical AI goes deep on one concrete problem in one industry. Think reviewing insurance sales calls for compliance, spotting defects on a factory line, or helping clinicians read medical images. The task and the customer are clearly defined.
General-purpose chatbots and foundation models that anyone can use for anything are often called horizontal AI. The VC speaker's point was simple: "we use AI" is no longer a pitch. Investors want to hear which industry you serve and which problem you solve for it.
Vertical AI vs. horizontal AI
Horizontal AI aims for the widest possible user base, while vertical AI aims for depth in one industry. Its edge comes less from model size and more from industry data and proven customer deployments.
| Horizontal AI | Vertical AI | |
|---|---|---|
| Who it serves | Everyone, many use cases | One workflow in one industry |
| Competitive edge | Model capability and scale | Industry data and domain know-how |
| Typical customers | Consumers and businesses of all kinds | Companies and institutions in that industry (mostly B2B) |
| Proof of value | User growth | Real customer deployments |
| Examples | General chatbots, writing assistants | Insurance call review, factory defect detection |
4 criteria VCs use to judge AI startups
According to the talk, VCs look at four things: data, industry understanding, references, and global reach. Model performance is the starting line, not the finish line.
- Data: Can you keep collecting customer data, and is that data closely tied to the problem you are solving?
- Industry understanding: Do you know the industry's workflows and vocabulary, and can you explain exactly where you sit in its standard value chain?
- References: Do you have real customers who deployed your product and saw results?
- Global reach: Can your industry expertise carry you into overseas markets? For physical AI, investors also look for the ability to build software and hardware together.
Be precise about your technology category, too. In Korea, deep tech categories such as the government's 12 National Strategic Technologies (designated in 2022 and including AI and advanced robotics and manufacturing) map directly onto fund mandates and targeted investment programs.
Where AI investment is heading: from infrastructure to services
Per the talk, AI investment in Korea through the first half of the year leaned heavily toward infrastructure such as NPUs (AI-specific chips) and data center power. The speaker expects services that make money on top of that infrastructure to grow next.
Venture booms and busts tend to show up as keyword waves. In the 2010s, money poured into mobile apps, and once smartphones and networks were everywhere, the services built on top of them took off. The speaker expects AI to follow the same path. They also noted that Korean venture investment splits into software, hardware, bio, and medical, and that apart from parts of bio, nearly all of it now connects to AI.
Sectors to watch: five industries and physical AI
The priority, according to the talk, is B2B vertical AI that plays a real role in five sectors: retail, manufacturing, finance, healthcare, and IT. Over the next three to four years, startups that solve industry problems well with AI are expected to attract the most funding.
- The speaker noted that vertical AI companies are growing in the US as well, with meaningful acquisitions happening quickly.
- Physical AI is also on the radar. Autonomous driving is expected to open up first, with humanoid robots taking longer. For the basics and real-world examples, see our guide to physical AI.
How daglo applies the same idea to voice records
Recording and transcribing speech looks different in every industry, with its own vocabulary and security rules, so a one-size-fits-all engine often falls short. daglo solves this by training its transcription engine on each organization's own data.
DB Life Insurance, a Korean insurer, tested an engine tuned to insurance terms and customer speech patterns inside its closed internal network, then rolled it out officially. Daegu, one of Korea's largest cities, has generated city council minutes automatically since 2019 with an engine retrained on the city's own data every year. Read more in our on-premise speech recognition case studies.
The same approach works for individuals and teams. When you record a conference talk or an investor meeting with daglo, as we did for this article, you can ask Board Chat to "list only the questions the investors asked" and walk into your next meeting prepared. Speaker ID remembers the voices of the people in your meetings and fills in their real names instead of "Speaker 1" from the next recording on, so you can see at a glance who asked what. daglo is the first service in Korea to offer this to all users. Learn more about Speaker ID
Related reading: On-premise LLMs vs. APIs: a cost comparison
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