As general-purpose AI dominates headlines, a quieter yet more commercially viable trend is emerging: Vertical AI — domain-specific, capital-efficient, and increasingly indispensable across industries. This report explains why vertical and agentic AI represent the next wave of investable AI infrastructure, with market data, company archetypes, and investment rationale. For VCs looking to deploy capital beyond foundation models, this is where traction meets defensibility.
While general-purpose AI models like ChatGPT generated tremendous excitement and investment in recent years, the natural evolution is innovation at the application level. Specialized AI companies are building sustainable businesses by applying AI to solve specific industry challenges.
Unlike Horizontal AI, which offers broad applications across industries, Vertical AI integrates deep domain expertise with advanced machine learning to solve industry-specific challenges. This specialization creates greater defensibility, faster adoption, and higher value capture, particularly in sectors like professional services, healthcare, financial services, logistics, and real estate.
Horizontal AI attempts to solve a key problem across many types of industries and businesses; vertical AI solves that problem for a specific type of customer, allowing for customized features, unique data for training and a more targeted go-to-market approach.
The vertical AI market, valued at $10.2 billion in 2024, is expected to grow at 21.6% annually through 2034 ($69B or more)1 But raw market size only hints at the opportunity.
What makes these companies particularly interesting to investors is their capital efficiency. While foundation model companies regularly raise nine-figure rounds just to cover training costs, vertical AI startups typically raise modest rounds of $2-8 million at pre-seed and seed stages, then quickly convert this capital into paying customers and revenue growth.
According to Bessemer Venture Partners, early-stage companies in this space are experiencing rapid funding and revenue growth.2 The increasing need for regulatory compliance and risk mitigation is further driving demand, as businesses pursue AI solutions customized to industry-specific standards rather than broad, one-size-fits-all models. Leading firms such as NEA, and Sequoia Capital have actively backed startups in this space, attracted by their market potential and differentiated value propositions.3
The operational metrics are equally impressive. Leading vertical AI companies demonstrate:
These aren't theoretical projections—they're results from companies already in market. Industry analysts predict at least five vertical AI companies will reach $100M ARR within the next 2-3 years, with the first vertical AI IPO likely in the same timeframe.
Finally, while vertical software-as-a-service solutions compete for software spend (1% of US GDP), vertical AI solutions, expected to replace end-to-end workflows, will compete for services spend (13% of US GDP).
Every founder needs to decide: are they enabling existing providers or replacing them?
That seemingly basic choice determines everything from customer acquisition costs to exit multiples. The six resulting business models offer investors dramatically different risk-reward profiles.
The table below maps the six dominant business models that have emerged in this space:
Jason Shuman, a General Partner at Primary, emphasizes the strategic importance of understanding these distinctions: "Understanding whether to enable service providers or to be a service provider is essential for founders navigating the Vertical AI space. The chosen model significantly influences a company’s growth trajectory and its potential impact on the industry.”4
Across sectors, Vertical AI is addressing pain points that general AI models simply cannot solve:
Beyond its technical and operational advantages, Vertical AI presents unique investment opportunity with several key benefits:
Unlike general AI models that can be applied across industries, Vertical AI is built for specific sectors. This specialization makes it significantly harder for new entrants to compete.
A critical advantage comes from proprietary data–often difficult to access or replicate. Many startups secure exclusive partnerships or develop unique datasets that create defensible competitive moats. Without this data, competitors struggle to deliver comparable accuracy and performance.
“In AI, access to proprietary data isn't just an advantage—it's a barrier to competition. Vertical AI companies that secure exclusive datasets and integrate deeply into industry workflows build moats that are nearly impossible to cross.”5
- Bessemer Venture Partners
Once embedded in operations, these solutions become difficult to replace. Businesses are hesitant to switch providers due to the challenges of retraining models, helping early entrants retain customers and strengthen their market position.
Vertical AI is designed to tackle real-world problems from day one. This focused approach accelerates adoption as businesses quickly experience gains in efficiency, accuracy, and cost savings. Its seamless integration into existing workflows minimizes disruption while maximizing impact. Whether automating manual tasks, improving decision-making, or optimizing resource allocation, these industry-specific AI solutions deliver measurable returns in a shorter timeframe.6
The TAM within these sectors is immense:7
These industries, along with others rapidly adopting specialized AI solutions, represent multi-trillion-dollar markets where Vertical AI can enhance efficiency at unprecedented scale.
Vertical AI thrives when integrated with other cutting-edge technologies like IoT, blockchain, edge computing, and automation. These combinations enhance efficiency, fortify security, and create deeper competitive moats, unlocking entirely new revenue streams. Companies that embed AI within broader technological ecosystems make their solutions harder to replicate, strengthening their market position and long-term value.8
By addressing mission-critical industry challenges, these AI-driven solutions justify premium pricing models and secure long-term contracts. Their deep integration into business operations results in high switching costs, making them indispensable to customers and ensuring recurring revenue.9
Look ahead three years and the vertical AI landscape will be dramatically different. Industry analysts expect at least five companies to cross the $100M ARR threshold by then – a milestone that separates the serious players from the merely interesting startups. The first vertical AI IPO will likely hit public markets in that same window, bringing broader investor attention to the category.
The timing couldn't be better. Healthcare, legal services, and financial firms face crippling labor shortages with no quick fix in sight. Vertical AI isn't just offering incremental efficiency – it's becoming essential infrastructure for industries that can't hire enough skilled professionals to meet demand.
What's curious is how relatively underfunded this category remains despite the clear potential. While founders chasing the next ChatGPT attract billion-dollar valuations on little more than technical demos, companies solving actual business problems with domain-specific AI are raising at valuations that still make fundamental sense.
The contrast in funding efficiency is stark. Training another large language model might require $100M+ just to cover compute costs, with commercialization still a distant hope. Meanwhile, vertical AI startups typically raise $2-8M at early stages and convert that capital directly into customer acquisition and revenue.
For investors, this presents a compelling proposition: back businesses that can achieve profitability without the infrastructure costs of general AI. The capital efficiency translates to lower dilution for founders and investors alike, while the faster path to revenue reduces overall investment risk.
The potential returns look particularly attractive as these solutions gain traction across major industries. Companies that establish leadership positions now will be difficult to dislodge later, creating powerful first-mover advantages for early investors.
Artificial intelligence is evolving from a system centered on data analysis and recommendation generation to one that plays a more dynamic and capable role in supporting complex tasks. The latest shift in artificial intelligence is the rise of agentic AI systems, which act more like capable collaborators than traditional tools. Instead of waiting for commands and offering suggestions, these agents act autonomously, plan ahead, adapt in real time, and complete complex tasks with minimal human input.
For example, in a business setting, an agentic system might be given a goal like “increase customer retention.” From there, it could identify the causes of churn, design and run targeted campaigns, measure results, and refine its strategy automatically, all without someone needing to oversee every step.
Ada illustrates the potential of agentic AI. Unlike traditional chatbots limited by scripted responses, Ada’s platform autonomously handles tier-1 and tier-2 support across industries such as fintech, e-commerce, and SaaS. With direct access to backend systems like Salesforce, Shopify, and Zendesk, Ada agents can perform actions, not just provide answers. They manage user issues end-to-end, reducing reliance on human agents and improving through continuous learning.
Key agentic capabilities include:
The results speak for themselves: companies using Ada report a 30–50% reduction in support ticket volume, increased customer satisfaction scores (CSAT), and faster time-to-resolution across thousands of interactions.
Flexport Flow is reshaping supply chain operations by automating critical decisions across freight, customs, and warehousing. It monitors delays, pricing, and bottlenecks in real time, adjusting routes, rebooking freight, and updating stakeholders without manual input. This operational autonomy reduces dependency on human intervention and enhances resilience in an industry where precision and speed are essential.
Key agentic functions include:
The future of AI isn’t general — it’s purpose-built, embedded, and autonomous. Vertical and agentic systems are more than technical innovations; they’re new business models with real traction and real moats. They solve industry-specific challenges with speed, accuracy, and efficiency — unlocking ROI from day one. For investors, the signal is clear: the next wave of enterprise value is forming at the intersection of intelligence and specialization. Quietly. Rapidly. And with lasting impact.
1. Global Market Insights. "Vertical AI Market Size, Share & Growth Report, 2024-2034."Global Market Insights, 2024, https://www.gminsights.com/industry-analysis/vertical-ai-market
2. Bessemer Venture Partners. The Future of AI Is Vertical." Bessemer Venture Partners, 2024, https://www.bvp.com/atlas/part-i-the-future-of-ai-is-vertical.
3. "Tomorrow's Titans: Vertical AI." New Enterprise Associates (NEA), https://www.nea.com/blog/tomorrows-titans-vertical-ai
4. Shuman, Jason. "Vertical AI vs. Software: How Founders Are Navigating the Rapidly Evolving Landscape." LinkedIn, 11 Mar. 2025, www.linkedin.com/pulse/vertical-ai-software-how-founders-navigating-rapidly-evolving-shuman-vuaze/
5. Bessemer Venture Partners. "The Future of AI Is Vertical." Bessemer Venture Partners, https://www.bvp.com/atlas/part-i-the-future-of-ai-is-vertical
6. "Vertical AI Market Trends." Global Market Insights, https://www.gminsights.com/industry-analysis/vertical-ai-market/market-trends.
7. "Artificial Intelligence Market Size | Industry Report, 2030." Grand View Research, https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market.
8. Pandey, Vijay. "Financial Technology Trends and Vertical AI Technology." Forbes, 19 Feb. 2025, https://www.forbes.com/councils/forbesfinancecouncil/2025/02/19/financial-technology-trends-and-vertical-ai-technology/
9. "Artificial Intelligence (AI) Market." MarketsandMarkets, https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html
10.Ada: AI Customer Service Automation Platform https://www.ada.cx
11.“How Agentic AI Is Revolutionizing Supply Chains.” https://kanerika.com/blogs/agentic-ai-in-supply-chain/