A radiologist opens a scan and an alert has already circled the suspected stroke, then paged the on-call specialist. Vertical artificial intelligence (AI) built that moment: software trained on one industry's data and wired into its daily work.

The companies below sell into that kind of workflow across healthcare, security, finance and other regulated fields. Each one picked a narrow, document-heavy job and went further than general tools can reach. We invest in this category at CRV, an early stage venture firm, so the examples here are companies we know from the inside.

This guide names the leaders in each industry, the traits they share and the fields where the next wave gets built.

What Is Vertical AI?

A vertical AI product serves one industry, trains on that industry's data, follows its workflows and meets its compliance rules, so it completes domain work instead of generic tasks. General tools answer questions across every field. A vertical AI company does the job that one field runs on, whether that is a stroke read or a permit filing.

The economics change with that depth. A vertical AI company can charge for completed work rather than software seats, which ties its price to the value a customer receives. These are AI-native businesses built around a single industry's ontology, and the vertical version of software as a service (SaaS) is what they displace. The vertical AI market is on track to grow past $74 billion by 2033, a sign of how fast buyers are shifting real budget toward it.

Vertical AI vs. Horizontal AI: What's the Difference?

Horizontal AI runs wide while vertical AI runs narrow and deep. Founders weighing the two see the split across four dimensions:

• Breadth versus depth: Horizontal models train on broad cross-domain data and aim to handle as many tasks as possible. Vertical AI trains on the proprietary data a single industry generates every day.
• Accuracy on domain work: General models, including large language models (LLMs), slip on specialized, high-stakes calls, while purpose-built models tuned for one field handle its edge cases, such as prior authorizations and claims.
• Compliance posture: Horizontal tools rarely ship with guardrails for rules like the Health Insurance Portability and Accountability Act (HIPAA), while vertical tools build the regulatory frame in from the start and cut adoption friction in regulated industries.
• Where value accrues: Competitors copy a horizontal feature quickly, while vertical AI holds its position through workflow depth, proprietary data and regulatory complexity that give it pricing power.

These gaps widen as the work gets more specialized and the cost of a wrong answer climbs. Vertical SaaS helped a person finish the job; vertical AI can finish more of it directly.

Vertical AI Examples Across Industries

The clearest way to see the category is inside the companies building it. Each industry below shares a pattern: a language-heavy or document-heavy workflow that older software never handled well, now owned by a company built for that one field. The names here are all companies that CRV backs, picked because the mechanics read clearly in each one.

Healthcare: Diagnostics and Care Coordination

Healthcare produces clear examples because so much clinical work is trapped in scans, records and conversations. Freenome builds blood tests that use AI to catch cancer at earlier, more treatable stages. Lotus Health AI runs a 24/7 AI primary care physician that triages and follows up with patients between visits. Viz.ai reads CT scans to flag a suspected stroke and page the on-call specialist, which has cut treatment time by roughly half an hour in a multicenter study.

Security Operations: Alert Triage and Threat Defense

Security teams drown in alerts, which makes the work a strong fit for agents that investigate on their own. 7AI runs autonomous agents that triage security alerts and carry an investigation to a conclusion. Outtake uses agentic AI to detect and dismantle phishing and brand-impersonation attacks across the open web. Vega runs security operations directly on data wherever it sits and triages threats without moving them into a separate store.

Financial Services: Corporate Finance and Compliance

Finance combines high-stakes decisions with heavy regulatory load, which suits domain-specific systems. Concourse runs AI agents that handle corporate finance work like forecasting, reporting and board prep. Hadrius gives investment firms AI-native compliance built for the Securities and Exchange Commission and the Financial Industry Regulatory Authority. Both sell into a function where an error carries a regulatory penalty, so accuracy and an audit trail decide the purchase.

Customer Operations: Support and Sales Agents

Customer work shows how outcome pricing rewrites the economics of a function. Gorgias runs conversational AI that resolves e-commerce support tickets and turns some of them into sales. Siro records in-person sales conversations and coaches field reps on what moved a deal or stalled it. Each one earns its keep against a countable result, a resolved ticket or a closed sale, which is why buyers adopt them quickly.

Construction and Field Services: Permitting and Project Ops

Construction runs on fragmented workflows spread across spreadsheets and aging desktop tools, which leaves room for focused entrants. Northspyre gives real estate development teams AI-driven cost forecasting and project controls that flag budget risk early. Pulley handles commercial construction permitting and pairs software with local experts to move approvals from months to weeks. Both replace manual coordination in a field where a single delay is expensive.

Insurance: Underwriting and Cyber Risk

Insurance stays document-heavy and slow to digitize, so AI-native entrants have room to move, and AI now automates a quarter of submission workflows at one large broker across underwriting, policy review and billing. Fulcrum automates insurance underwriting and policy work so account teams can carry larger books. Resilience quantifies a company's cyber risk in financial terms and pairs the assessment with cyber insurance. Both sit on budgets that already exist, which shortens the path to adoption.

What the Best Vertical AI Companies Have in Common

Model quality alone explains little here, since every company can call the same frontier models. The ones that keep outgrowing horizontal software tend to share five operating traits:

• Proprietary data loops: Each customer's accumulated context makes the system harder to leave the longer it runs, and no new entrant can buy that history off the shelf.
• Ownership of the core workflow: The strongest companies run that work directly rather than offer suggestions from the side.
• Outcome-based pricing: Charging against resolved tickets or completed work ties revenue to the value a customer receives.
• Compliance from day one: Auditability and determinism sit above surface polish, because a general model cannot produce the bias audit a regulator wants.
• Distribution into fragmented markets: The clearest openings sit in industries defined by manual work and messy data that older software skipped.

A product a team uses every day is far harder to dislodge than a tool it has to remember to open.

Why Vertical AI Is Eating Vertical SaaS

Vertical SaaS competed for a slice of software spend, a fraction of what an industry pays to get its work done. The category changes when a product sells into the labor budget itself, so the addressable market swells the moment it completes real work. Buyers have already moved toward flexible pricing, and 43 percent now prefer consumption models over fixed seats.

At CRV, we back founders attacking these labor budgets directly, and we expect most vertical SaaS companies to become vertical AI systems or lose ground to one. An agent that does the work of 10 people does not ask for 10 seats, so per-seat pricing breaks as software starts to replace labor. The shift rewards companies whose agentic reasoning holds up on the messy, regulated work a single industry runs on.

Where the Next Vertical AI Leaders Will Emerge

The next wave gets built in fields older software skipped, where labor was too expensive to apply at scale. A pilot is not a contract, and most enterprise pilots stall before production, so the openings go to companies that reach real deployment. CRV is early in four of these fields:

• Public safety: Closure builds an AI digital analyst that organizes and searches case evidence for law enforcement, while Flock Safety runs a camera and license-plate network that helps departments solve crime.
• Logistics and freight: Kodiak Robotics runs autonomous long-haul trucks in commercial service, while Storyboard gives fleets a voice-first AI assistant for drivers and back-office tasks.
• Robotics and industrial automation: Dyna Robotics, Skild AI and Theker build AI-native robots for the physical economy, including a general-purpose robot brain and factory systems that learn on the job.
• Science and materials: Periodic Labs builds AI that predicts material properties to speed up physical research and development.

Each field shares a starting point: manual work around regulated data that general models cannot legally touch. Those conditions turn a narrow entry into a broad system once the product earns trust inside the work.

Vertical AI Is the Defining Company-Building Opportunity of the Decade

The companies in this guide are the first wave, and the pattern under them holds because each customer makes the next one easier to win. A vertical AI company that turns daily usage into better output builds a lead that grows with every account, which is the core reason domain expertise wins in this category. Founders who break out tend to own one unglamorous workflow completely before expanding.

We spend our time with technical founders chasing exactly this kind of problem across regulated, document-heavy industries. If you're an early stage founder looking for a partner to build a vertical AI company, reach out to us to see if we'd be a good fit.

Frequently Asked Questions About Vertical AI Companies

What is the difference between vertical AI and horizontal AI?

Horizontal AI is general-purpose software trained on broad, cross-domain data to handle many tasks. Vertical AI is built for one industry, trained on that industry's proprietary data and wired into its workflows. The vertical version performs better on high-stakes domain work and ships with the compliance guardrails general tools lack.

Will vertical AI replace SaaS?

Vertical AI is pulling budget away from traditional vertical SaaS because it sells into labor spend, a larger pool than software spend. Many vertical SaaS companies will either grow into systems that do the work or lose ground to AI-native companies that do. The shift already shows up in pricing, where per-seat models give way to outcome and consumption structures.

What gives a vertical AI company a lasting edge?

A lasting edge comes from proprietary data built up through daily use and from owning the workflow itself rather than advising on it. Compliance built into the product adds another barrier, since regulated buyers require audits and domain depth. Each customer's accumulated context makes the system costlier to leave the longer it runs.

How is vertical AI different from a fine-tuned LLM?

A fine-tuned LLM is one component, while a vertical AI company is a full product built around a workflow. The company structures messy industry data, integrates with legacy systems, designs approval steps and sets acceptable error rates. That surrounding work is what creates value and makes the company hard to copy.