How AiimOne Helped Magpai Analytics Automate Insurance Submissions and Deliver Quote-Ready Decisions 92% Faster
Industry
Insurance
Service
AI/ML Engineering
Tech Stacks
Azure Open AI, .NET Core, Azure AI Document Intelligence
Executive Summary
AiimOne helped Magpai Analytics build an AI-powered underwriting assistant that cut submission processing time by 50%, boosted team capacity by 30%, and delivered risk-scored, quote-ready submissions 92% faster than manual review.
Magpai Analytics came to AiimOne with a clear mission: fix the mess commercial insurers face when submissions land in their inbox. Broker emails, ACORD forms, SOV spreadsheets, and loss runs were being reviewed by hand across inconsistent formats. Decisions took days. Risk evaluations varied from underwriter to underwriter. Data insights sat locked in PDFs.
AiimOne partnered with Magpai to build a full AI-driven platform that ingests submissions from any channel, classifies documents automatically, extracts structured data with NLP, applies business rules, and generates transparent risk scores. Underwriters now spend their time evaluating risk, not preparing paperwork.
Key Results:
- 92% faster underwriting turnaround
- 50% reduction in submission processing time
- 86% data accuracy on extracted fields
- 30% increase in team capacity without new hires
- Quote-ready submissions delivered in minutes instead of hours
- End-to-end audit trail on every decision
What Was Breaking Commercial Underwriting?
Turn Thousands of Submissions Into Organized Data
Manual review was slowing decisions, creating inconsistent risk evaluations, and leaving valuable data trapped inside insurance documents.
Commercial insurers receive thousands of submissions through broker emails, portals, and APIs. Every submission looks different. ACORD forms in one format, SOV spreadsheets in another, loss runs somewhere else, plus supporting docs in whatever shape the broker sent them.
The three problems stacking up:
- Slow decision-making delayed responses to brokers.
- Long turnaround times caused brokers to choose competitors.
- Risk assessments were inconsistent across underwriters.
- Valuable insights remained buried in unstructured documents.
- Manual document analysis consumed significant time.
- Existing workflows lacked efficient AI-powered support.
Goals and Objectives
Automate the Busywork and Focus on What Matters
Magpai wanted an AI-powered underwriting assistant that automated the submission lifecycle while improving speed consistency and decision quality.
The full goal set for the platform:
- Automate submission intake across all channels.
- Extract structured data from any document type.
- Apply business rules consistently across every submission.
- Generate AI-driven risk scores for faster underwriting.
- Deliver quote-ready submissions in minutes.
- Reduce manual effort through intelligent automation.
- Maintain a complete audit trail for every decision.
How Do You Turn Messy Insurance Documents Into Clean Underwriting Decisions?
Build an Event-Driven AI Platform That Reads Sorts and Scores Every Submission
- Built an event-driven Azure architecture to separate submission intake from AI processing.
- Combined document intelligence, NLP, and a rules engine into a unified processing pipeline.
- Supported multi-channel submission intake through emails, portals, and APIs.
- Used queue-based processing to handle traffic spikes without impacting performance.
- Processed multiple documents in parallel for faster turnaround.
- Combined AI with business rules to deliver consistent and transparent decisions.
- Maintained a complete audit trail for every underwriting decision.
- Delivered processed results instantly through API integrations.
The four-step engine underneath:
Ingest submissions automatically,
Classify documents by type (ACORD, SOV, loss run).
Extract structured data using NLP and document intelligence.
Validate low-confidence fields with human review before delivery.
Build Faster With Flexible Solutions and Human Control
Designed an event-driven architecture with queue-based load leveling to keep AI services stable under high workloads.
Built the intake service in .NET Core to process submissions from Exchange, IMAP, REST APIs, SharePoint, and SFTP.
Configured Azure API Management for authentication, routing, and rate limiting.
Integrated Azure AI Language, Azure OpenAI, and Azure Document Intelligence for document classification and data extraction.
Built a combined rules engine and AI scoring model for transparent, consistent decision-making.
Implemented retry mechanisms and asynchronous request/reply patterns for fault tolerance.
Stored submissions and decisions in Azure SQL, Blob Storage, and Cosmos DB with complete audit logging.
Delivered processed results through APIs, webhooks, and an analytics dashboard for underwriters.
Tested the platform with real-world insurance documents to validate accuracy and performance at scale.
What Changed After Go-Live?
Underwriters Got Their Time Back, and Magpai Got a Platform That Scales
Magpai’s platform turned faster processing, cleaner data, and transparent AI decisions into stronger underwriter productivity, better risk consistency, and a real edge for its insurance clients.
Outcomes:
- Underwriters shifted from preparing submissions to evaluating risk, which is what they were hired to do.
- Insurance clients started closing quotes faster, giving brokers a reason to send more business their way.
- Risk decisions got consistent across teams, thanks to standardized business rules and AI scoring.
- Portfolio visibility improved sharply, so leadership could spot trends and adjust strategy.
- Data quality jumped, which fed better pricing models and downstream analytics.
- Magpai now has a platform that scales with enterprise clients without breaking on volume spikes.
- The modular design means new document types and integrations can be added without rework.
- The audit-ready decision trail gave compliance teams the transparency they'd been asking for.