A cardiologist watches a general-purpose chatbot summarize a patient encounter and spots a fabricated medication in the first paragraph, the kind of error that could put a patient at risk. Vertical artificial intelligence (AI) lives between a tool that sounds fluent and one that knows the work is where vertical AI lives. Founders across healthcare, legal and financial services are turning their domain expertise into products that fit real workflows. CRV-backed Lotus Health AI shows what that looks like in practice, pairing an AI doctor with licensed physicians who review its findings before any diagnosis or prescription reaches a patient.

This guide covers what separates vertical AI from horizontal AI, why domain-specific models outperform general ones in regulated fields and how founders build positions that last.

What Vertical AI Means and How It Differs from Horizontal AI

Where your company falls on this spectrum affects product roadmap, positioning, pricing and how you explain your edge to investors.

Vertical AI Defined

Vertical AI refers to AI products designed for a specific industry, embedding domain knowledge, regulatory compliance and workflow integration that general-purpose models don't provide out of the box. A clinical documentation tool built by a cardiologist, a legal research platform trained on case law, or an insurance underwriting model calibrated to actuarial data all qualify. Two of CRV's investments map onto those archetypes: Lotus Health AI on the clinical side, offering physician-reviewed primary care around the clock, and Fulcrum on the insurance side, with agents that handle policy checking, coverage comparisons and proposals for brokerages. Early stage investors have backed companies across this category, describing the movement toward vertical AI as a direct response to the gaps general-purpose models leave in specialized workflows.

Vertical AI's narrower scope focuses these products on specific questions with the accuracy their industry demands. The reduced scope also reduces knowledge gaps and shrinks the risk of hallucinated outputs.

Horizontal AI Defined

Horizontal AI products work across industries without industry-specific customization. Copilots like ChatGPT Enterprise, Claude for Work and Microsoft Copilot fall into this category, alongside agent platforms and personal productivity tools. Horizontal AI handles universal tasks well: drafting emails, summarizing documents and answering general knowledge questions.

Horizontal tools offer less depth. A horizontal tool can schedule an appointment when a customer asks, while vertical AI adds process intelligence to diagnose a specific problem and route the right technician to fix it faster. Horizontal AI gives you breadth. Vertical AI gives you the process intelligence that closes deals in regulated industries.

The Dimensions That Separate Them

Three factors determine whether a company is building vertical or horizontal AI. Data type comes first: vertical AI trains on domain-specific, industry-focused data rather than general web-scale datasets. Customization depth is second: vertical products embed industry knowledge directly into the model itself rather than relying on prompt engineering alone.

For founders, the third factor is the source of competitive advantage. Horizontal AI companies compete on distribution, brand and compute scale. Vertical AI companies compete on data access, workflow integration and regulatory positioning. Founders who understand their industry's buying process, compliance requirements and operational pain points have an edge that pure technologists can't quickly replicate.

Why Domain-Specific AI Outperforms General-Purpose Models

The performance gap between vertical and horizontal AI is material. In regulated industries, the difference between a high accuracy rate and a materially lower one can determine patient safety, legal liability or financial exposure. Enterprise buyers have responded by shifting budgets toward domain-specific tools and by adopting specialized models more broadly.

Accuracy in Regulated Fields

General-purpose large language models (LLMs) hallucinate at alarming rates in clinical and legal settings. LLMs hallucinated at 65.9 percent under adversarial clinical conditions. Even with every available mitigation applied, the best-performing general model hallucinated 23 percent of the time on clinical case summaries.

Legal AI shows a similar pattern. Fine-tuned domain-specific models can outperform general-purpose LLMs on legal classification tasks, while general models hallucinate legal content at rates near 58 to 88 percent on legal queries, including tasks such as case citation and case-law interpretation, a pattern documented across legal benchmarks. Clinical documentation tools built on vertical AI catch up to 97 percent of confabulations in draft clinical documentation compared to GPT-4o's 82 percent. That gap has direct consequences for patient safety, and it illustrates why investors have concentrated vertical AI investments in companies with this kind of domain-specific architecture.

Enterprise Adoption and Spend

Enterprise buyers have noticed the performance gap and are redirecting budgets accordingly. By 2027, more than half of enterprise generative AI deployments will be powered by domain-specific models, up from one percent in 2023.

The adoption pattern is especially visible in regulated sectors, where buyers need higher accuracy, better auditability and tighter workflow fit before they will move a product into production.

Where Vertical AI Companies Are Setting the Pace

Companies across healthcare, legal and financial services are reaching meaningful scale and capital continues to flow into these categories as domain-specific products show sustained commercial traction.

Healthcare: Clinical AI Reaches Scale

Abridge raised funding across two rounds in 2025 and captured a meaningful share of the ambient scribe market. The company's founding insight came directly from clinical experience: physicians spend a substantial amount of time on documentation relative to patient care. AI-focused startups captured 62 percent of all digital health venture funding in the first half of 2025, raising an average of 83 percent more per round than non-AI peers. Joyful Health, one of CRV's healthcare investments, automates the financial operations behind unpaid claims and denials for providers, and it raised a Series A as capital kept concentrating in AI-native health startups.

Legal: Category Leaders Pull Away

Harvey, a legal AI platform, tripled its valuation from three billion dollars in February 2025 to five billion dollars in June 2025 and reached about 190 million dollars in annual recurring revenue (ARR) by the end of 2025. The legal tech sector raised more capital in 2025 than the prior year, even as fewer companies raised funding, concentrating capital in category leaders. Founders entering legal AI today need to identify sub-vertical niches where current leaders don't have traction.

Financial Services and Construction: Revenue Milestones Accelerate

Financial services AI has produced breakout companies across market intelligence, investment banking and insurance. AlphaSense is one example of a company operating at a significant scale in market intelligence. In construction, Trunk Tools deployed its platform across active jobsites for Suffolk Construction, helping accelerate submittal review cycles. On the insurance side, CRV-backed Fulcrum has brought AI agents to policy checking, coverage comparisons and proposal generation for brokerages, and it already counts many of the top 50 US insurance brokerages among its customers.

How Founders Build Vertical AI Companies with Lasting Advantages

Across healthcare, legal, financial services and construction, founders with deep industry experience build products that general-purpose tools can't match and they reach revenue milestones at a pace traditional software as a service (SaaS) rarely matches. Creating a vertical AI product is one challenge. Keeping your position as foundation models improve and big tech expands into your category requires another set of decisions. Differentiation earns you the opportunity to compete. Long-term protection depends on more than differentiation.

Start with Domain Expertise

Founders who lack industry experience can underestimate sales cycle length in regulated verticals, and those projection errors ripple through hiring plans and runway calculations. A team of former insurance underwriters building an AI tool for insurance processes brings pattern recognition that a generalist AI team can't match, regardless of model access.

That difference shows up in every customer conversation, where domain fluency determines whether a prospect trusts you with their workflow. CRV-backed security startup 7AI deploys autonomous agents that investigate security alerts in real time rather than following static rules, and the company's founding insight came from seeing firsthand that the math on human-only security operations breaks down once threat volume keeps climbing. Deep domain knowledge speeds up the path to product-market fit in vertical AI because you already know what customers need.

Build Workflow Advantages, Not Data Hoards

Static datasets don't create lasting competitive advantages because the next generation of foundation models may absorb any training data you've accumulated. Workflows and customer networks create stronger sources of competitive advantage than data alone, and the strongest vertical AI companies build products so tightly integrated into their customers' daily operations that replacing them would require rewiring entire professional processes.

Every customer interaction, every edge case correction and every workflow completion feeds back into the system. Vertical AI companies have a particular advantage here because they collect industry-specific data that horizontal products can't access, and their goal is a product that gets better with use rather than one that sits on a fixed dataset.

Use Regulation as a Structural Barrier

Regulation in healthcare, legal and financial services creates barriers that restrict which players can operate at full capability and founders who view compliance as a cost center miss the strategic opportunity.

Early investment in obtaining credentials, compliance infrastructure and institutional data access rights yields a barrier that competitors are slow to replicate, because it involves regulatory processes and institutional relationships rather than engineering effort.

Code review tools like CodeRabbit operate in a space where product performance depends on strong technical understanding that general-purpose tools may lack. In regulated verticals, the effort required to meet industry-specific requirements becomes the same thing that keeps competitors out, and every certification earned and every data access agreement signed widens your lead over later entrants.

Mistakes That Stall Vertical AI Founders

The market supports the vertical AI thesis, but the execution bar has risen sharply. The most frequent failures come from building for the technology instead of for the buyer. Founders who avoid these common errors give themselves a much better chance of becoming category leaders:

• Stopping at differentiation: Solving a specific problem ten times better than the alternative earns you customers and protecting your position requires deep product integration, network effects or product virality on top of that initial advantage.
• Automating the wrong work: Processes requiring constant improvisation or judgment calls that teams haven't codified resist automation. The best targets are standardized, repeatable workflows that your customers execute regularly with consistently positive results.
• Overbuilding before validation: Overbuilding before verifying that the pain is acute enough creates runway risk. The most capital-efficient founders start with a single high-value workflow, prove measurable return on investment and expand from there.
• Pricing against the wrong budget: Labor budgets in healthcare, legal and construction are often much larger than information technology (IT) budgets. Founders who demonstrate that their product replaces the cost of a full-time role close larger contracts and face less procurement friction than those who position themselves as software tools.

Each of these errors traces back to the same root cause: building around model capabilities while missing the customer's buying logic. The strongest vertical AI founders start with the customer's workflow and work backward to the model architecture, and that sequence determines whether a company becomes a category leader or a feature absorbed by the next platform update.

Why Workflow Knowledge Decides Who Wins

The vertical AI companies that endure tend to start from the customer's daily reality rather than the model's capabilities. We've watched companies in vertical AI reach scale fastest when their teams knew the workflow before they knew the model, and that head start shows up in faster sales cycles, stickier products and clearer answers when investors ask what protects the position. Founders who carry that kind of domain fluency rarely have to convince a buyer the tool was built for them.

If you're an early stage founder looking for early stage partnership, reach out to us to see if we'd be a good fit.

Frequently Asked Questions About Vertical AI

What makes vertical AI different from vertical SaaS?

Vertical SaaS brought cloud software tailored to specific industries. The AI generation takes the same industry-specific approach and adds capabilities that previous software waves couldn't deliver, particularly in language-heavy, multi-modal workflows. These products feel less like software and more like added operating capacity for the people using them.

How do vertical AI companies defend against big tech expansion?

The strongest vertical AI companies own three things big tech struggles to replicate quickly: proprietary industry data that can't be scraped from the open web, deep workflow integration that outlasts any single underlying model and regulatory expertise that takes years of institutional relationship-building to develop. Founders who combine all three create a position that remains strong even as foundation models improve. Big tech can ship a general feature overnight. Replicating years of domain-specific workflow data and institutional relationships takes much longer.

What traction do investors expect from vertical AI startups at Series A?

The traction bar for vertical AI companies raising a Series A continues to rise, with investors looking for clear revenue traction and a demonstrated ability to accelerate from there. Founders also need to articulate what protects their position, whether in proprietary data, workflow embedding or deep product integration, rather than leading with model architecture alone. Investors increasingly discount model differentiation on its own and look for evidence that the product has become embedded in customer workflows.

Do vertical AI startups need to build their own models?

Using AI models as a service lets you move quickly with a small team and swap model providers as the market changes. Some founders argue that training from scratch protects their position through proprietary product data, but that approach requires significantly higher capital and a less nimble product team. Your choice depends on whether your advantage comes from the model itself or from the data, workflow and domain expertise wrapped around it.