MCP Server
The Data Layer for Insurance AI

Connect AI agents to Fenris data services through a single MCP integration.
Power AI workflows with real insurance data, predictive scoring, and underwriting intelligence.

Request Early Access

The Fenris MCP Server connects AI agents to Fenris data services through a single Model Context Protocol (MCP) integration.

MCP is an open standard that allows AI tools to securely access external data services. Through Fenris MCP Server, AI systems can retrieve real insurance intelligence including household composition, vehicles, drivers, property attributes, business information, and predictive scoring models.

Instead of building custom integrations for every service, teams connect once and make Fenris intelligence available to their AI workflows.

AI Without Real Data is Guessing

AI adoption across insurance is accelerating, but most AI systems still lack direct access to structured insurance data.

76% of insurers report adopting AI, yet only 7% have successfully scaled it across their organization. The problem is rarely the model itself. It is access to structured, real-time data.

Large language models can summarize information, generate explanations, and assist with workflows. But real insurance decisions require access to verified data and predictive signals.

Without structured data, AI systems cannot reliably answer questions like:

  • What vehicles belong to this household?
  • What drivers live at this address?
  • What risks exist at this property?
  • Is this business a strong underwriting candidate?
  • Which prospects are most likely to convert?
  • Which submissions are highest risk?
How AI Agents Connect to Fenris

Compatible with Claude, ChatGPT, Gemini, and custom AI agents supporting MCP.

This is where Fenris MCP Server fits.

It connects AI agents directly to structured insurance intelligence so they can retrieve, evaluate, and apply real data inside operational workflows.

Request Early Access

What Fenris MCP Server Does

Fenris MCP Server gives AI agents dynamic access to Fenris insurance data services through a single MCP connection.

Instead of building custom integrations for each API, AI systems connect once and automatically discover available Fenris services.

Automatic Tool Discovery

AI agents automatically discover available Fenris services through MCP.

Dynamic Service Selection

Agents call the correct data service based on the request or workflow.

Multi-Service Orchestration

A single interaction can retrieve data across multiple Fenris services.

Structured Data Responses

Outputs return decision-ready intelligence for downstream workflows.

Fenris MCP Server uses the same underlying data services available through the Fenris API, giving AI agents access to Fenris intelligence without custom integration work.
Developers can explore the available endpoints and authentication flow in the Fenris API documentation.

Access Fenris Data Services Through MCP

Fenris MCP allows AI agents to retrieve insurance intelligence across multiple domains during a single workflow.

Through a single MCP connection, AI systems can access consumer, commercial, property, vehicle and driver, and predictive intelligence services. These services combine structured insurance data with predictive models trained on real insurance outcomes.

Consumer
Intelligence

Household composition, home ownership indicators, and contact intelligence for intake and enrichment workflows.

Commercial
Intelligence

Business identity data, firmographics, and business risk context across millions of companies.

Property
Intelligence

Property characteristics, valuation indicators, hazard context, and underwriting-relevant attributes.

Vehicle & Driver
Data

Vehicle ownership, driver history indicators, and household vehicle relationships.

Predictive
Scoring

Lead scoring and prioritization models for personal and commercial insurance opportunities.

Request Early Access

What Your AI Agents Can Do With It

Organizations building AI workflows in insurance often start with quoting assistants, lead scoring agents, or automated underwriting tools.
Fenris MCP allows those AI systems to retrieve insurance intelligence in real time.

Automated Application Prefill

AI agents collect a name and address, retrieve household, vehicle, driver, and property data, and populate insurance applications automatically.

This reduces manual data entry while improving quoting accuracy and intake efficiency.

  • Retrieve household members, vehicles, and property attributes instantly
  • Populate quoting or intake systems without manual data entry

Intelligent Lead Scoring

AI agents score inbound leads before human review using predictive models trained on real insurance outcomes.

This helps sales teams prioritize prospects most likely to convert and focus on the highest-value opportunities.

  • Evaluate lead quality using propensity-to-bind scoring models
  • Route high-intent prospects to agents or automated workflows

Real-Time Risk Assessment

AI agents retrieve underwriting intelligence during quoting or submission evaluation.

Instead of manual lookups, agents can evaluate risk signals instantly during intake or underwriting workflows.

  • Retrieve property hazards, replacement cost indicators, and building attributes
  • Access driver records and commercial risk intelligence in real time

Customer Service Enrichment

Customer service agents retrieve verified policyholder context during support interactions.

AI systems provide relevant information instantly, eliminating the need to search across multiple systems.

  • Retrieve household composition and property details tied to a policyholder
  • Verify contact information and customer identity during service requests

These workflows become possible when AI agents can access structured insurance intelligence in real time. Fenris MCP Server provides that connection.

Request Early Access

Why Fenris

Fenris MCP connects AI agents to one of the largest insurance intelligence datasets built specifically for insurance distribution and underwriting workflows.

Unlike generic MCP servers that expose internal tools, Fenris provides structured insurance data and predictive models built specifically for insurance distribution, underwriting, and customer workflows. Our models are trained on real insurance outcomes and delivered through real-time APIs designed for production workflows.

Fenris infrastructure is SOC 2 compliant and designed to support secure, production-grade AI deployments.

200M+
Individuals

35M+
Businesses

Property Data Across the U.S.

Vehicle & Driver Data

Predictive Insurance Scoring

Fenris data services power real-time workflows across quoting, underwriting, and insurance distribution platforms.

Request Early Access

Fenris MCP Server is currently available through an early access program for platforms, AI teams, and insurance innovators building AI-driven insurance workflows.

Tell us what you're building and our team will help you evaluate how Fenris MCP can connect your AI agents to real insurance intelligence.