What Is Vertical AI? The Future of Domain-Specific Intelligence

Vertical AI refers to artificial intelligence solutions built specifically for a particular industry or domain. Unlike general-purpose or "horizontal" AI, vertical AI is designed to solve specialized problems using industry-specific data, workflows, and knowledge. 

This type of AI is trained with deep context from a single field—such as insurance, healthcare, finance, or legal—to deliver higher accuracy and faster deployment for niche use cases

Key Characteristics of Vertical AI:

• Domain Expertise: Encodes deep knowledge of industry-specific terminology, processes, and requirements.
• Custom Training: Uses proprietary or public data from a single industry to train the model.
• Workflow Integration: Seamlessly connects with established business tools and processes.
• Regulatory Compliance: Embeds rules and protocols tailored to industry-specific compliance standards.

How Does Vertical AI Differ From Horizontal AI?

Vertical LLMs (large language models) are a key innovation within this space—these are language models fine-tuned on industry-specific data to power solutions with built-in domain expertise. 

In insurance, for example, Brisc employs AI agents trained specifically on insurance workflows and processes across bordereaux, claims, and submissions data to automate manual work and deliver precise, regulation-aware insights that general models often miss.

The Risks of Horizontal AI in Regulated Industries

While general-purpose AI tools offer broad utility, they often break down when applied to complex, regulated industries. Horizontal AI models lack the contextual understanding and compliance awareness required to perform reliably in high-stakes environments.

Why Horizontal AI Falls Short:

• Terminology: Struggles to interpret specialized language relevant to niche industries.
• High Risk of Hallucination: Without domain constraints and contextual awareness, general models may generate inaccurate or non-compliant outputs.
• No Built-in Compliance: Horizontal AI lacks the mechanisms to account for SOC2, HIPAA, or other industry-specific regulations.
• Generic Recommendations: Provides responses that aren’t aligned to industry workflows or logic.
• Integration Burden: Requires significant configuration to make it compatible with business-critical systems.

In contrast, vertical AI models are built with guardrails that reflect the real-world expectations, data structures, and regulatory frameworks of the industries they serve

Why Industries Need Vertical AI Solutions

General-purpose AI can struggle to deliver meaningful results in specialized fields due to lack of context and compliance. Vertical AI addresses these limitations head-on:

Key Challenges Solved by Vertical AI:

• Navigating complex regulatory environments (e.g., SOC2, HIPAA)
• Understanding industry-specific terminology and logic
• Automating specialized workflows that don’t follow generic patterns
• Ensuring data privacy, security, and compliance at the core of operations

Real-World Examples:

• Insurance: AI systems that automate key underwriting, claims, and bordereaux reconciliation processes with full regulatory awareness.
• Finance: Vertical AI models that detect fraud patterns unique to financial institutions
• Healthcare: Models trained on clinical data for diagnostics, reducing false positives
• Legal: Tools that understand case law and legal precedent

Benefits of Vertical AI

Businesses that adopt vertical AI solutions can expect faster results, better outcomes, and lower long-term costs:

• Improved Accuracy: Domain-trained models reduce false positives and AI hallucinations, and improve decision quality
• Faster Implementation: Pre-trained on industry data, enabling quicker deployment
• Reduced Training Time: Less need for customization compared to general AI models
• Better ROI: Faster time-to-value thanks to alignment with real business needs
• Competitive Advantage: Early adopters gain efficiency and insight that general tools can’t provide

In the case of insurance, vertical AI is much better suited to understand the nuances of real-world decisions—like evaluating policy exclusions, identifying missing submission details, or surfacing trends across bordereaux data—allowing insurance teams to move faster with greater confidence. Instead of spending time stitching together insights from multiple systems, vertical AI brings clarity directly to the decision point.

Steps to Implement Vertical AI

1. Assess Your Domain Requirements

• Where are your bottlenecks?
• What are your most manual, repetitive tasks?
• Which processes require expert judgment or interpretation?

2. Identify Key Data Sources

• Internal databases, documents, PDFs, customer forms
• ACORD forms (for insurance), medical records, financial filings
• Ensure data is clean, structured, and relevant

3. Choose the Right Tools

• Does the solution specialize in your industry?
• Does it support your key workflows and terminology?
• Does the vendor have proven expertise in your field?

4. Integrate With Existing Systems

• Use APIs and connectors to embed AI in your tech stack
• Ensure the AI can interact with your CRM, ERP, policy admin, etc.

5. Monitor and Iterate

• Track KPIs like accuracy, processing time, user satisfaction
• Fine-tune models based on real-world performance

Key Use Cases and Applications

Vertical AI is transforming workflows across industries. Here are a few examples:

Insurance

• Claims Management: Automate triage and monitor critical claims
• Underwriting: Use vertical LLMs to assess risk with unstructured data
• Submissions: Ingests data from ACORD forms, PDFs, spreadsheets, and other formats to validate and prepare information for underwriting workflows
• Bordereaux Reconciliation: Processes complex bordereaux files, flags discrepancies, and ensures accuracy across reporting
• Policy Recommendations: Tailor policies based on customer needs
• Fraud Detection: Identify anomalies to flag for human review
• Customer Service Automation: Handle complex inquiries with domain-trained AI agents

Other Industries

• Healthcare: Clinical decision support, medical transcription
• Finance: Regulatory compliance monitoring, loan underwriting
• Legal: Contract review, litigation research
• Manufacturing: Predictive maintenance, defect detection

Vertical AI as a Strategic Shift

Adopting vertical AI is a transformation of how organizations operate. By embedding intelligence directly into industry-specific workflows, organizations shift from reactive operations to proactive, insight-driven decisions. This enables faster execution, smarter resource allocation, and continuous improvement across the business. As vertical AI continues to mature, it will become a core differentiator for companies looking to lead in complex, regulated, and data-rich environments.

Where Is Vertical AI Headed?

Vertical AI will build on existing momentum, continuing to enable:

• Multimodal Inputs: Combining text, documents, images, and audio
• Autonomous Decision-Making: Domain-specific AI agents that take action, not just advise
• Deep Workflow Integration: Embedded directly into core operations
• Built-In Regulatory Alignment: AI solutions that evolve with industry compliance
• New Service Models: AI-as-a-Service offerings customized by vertical

Transforming Insurance With Brisc

Brisc exemplifies how vertical AI can transform a complex, regulated industry like insurance. Its domain-specific AI agents help accelerate insurance operations like:

• Submissions Intake and Triage
• Bordereaux Reconciliation
• Claims Management and Prioritization

Curious how vertical AI can streamline your insurance operations? See Brisc in action.