InsuranceAI implementation case study

Insurance – Specialty Lines MGA: Underwriting Workbench & Submission AI

AI for broker submission intake, loss run analysis, pricing guidance, and carrier referral packaging.

This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.

Key takeaway

PrimeAxiom automated specialty underwriting operations with AI agents assembling evidence and pricing context—not spreadsheets in isolation.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: specialty MGA. Complexity: non-standard risks.

02 · Challenge

Core problem

Manual reading, slow quotes, inconsistent pricing.

03 · Approach

Solution

PrimeAxiom deployed underwriting AI: submission normalization, loss-run extraction agents, pricing similarity search, and carrier-ready referral packets.

04 · Impact

Results

Quote cycle time −43%; underwriter capacity +31%; referral acceptance +18%; leakage from mispricing −27%.

System design

The full automation system

The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.

Submission Agent, Loss Run Agent, Pricing Agent, Referral Agent.

Workflow

How the automation runs

1. Broker upload. 2. AI extracts exposures. 3. Loss history structured. 4. Pricing comps. 5. UW review. 6. Carrier packet. 7. Feedback improves extraction.

Intelligence layer

AI agents and decision logic

Extract Agent, Compare Agent, Package Agent.

Under the hood

Technical architecture

RAG over loss runs, LLM with numeric validation, secure vault.

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