Project Introduction
Welcome! This project is designed to bring advanced document, risk assessment, and AI-driven insights together into a unified system for smarter decision-making in the underwriting world. It combines backend orchestration, AI agents, and frontend interaction to help users process PDFs, assess risk, manage emails, and generate structured outputs efficiently.
1) What is the problem being addressed?
Many insurance organizations struggle with fragmented information workflows: scanned documents, compliance rules, email traces, and risk models are in separate workflows. That leads to slow reviews, human error, and low visibility. This project solves that by creating an integrated platform where documents and data are ingested, analyzed automatically, and served through a clean UI and API, reducing manual overhead and speeding up actionable results. Utilizing the power of AI concepts, the goal is provide a quick, but accurate way of representing the application where decisions could be made in a more organized and streamlined way.
2) What is your project idea and how will it work?
The idea is an AI-powered workflow platform with these key components:
- Backend services: handle ingestion, parsing, and risk scoring pipelines.
- Modular Lambda-like handlers (PDF, email, documents, requests): each path has dedicated parser and transformer logic.
- Risk assessment engine : applies layered rule sets (C1..C5) to compute compliance/risk profiles.
- Frontend app : React/Vite UI for dashboard, document upload, results display.
- Layered requirements and infrastructure (layers/): isolating dependencies for each microservice.
- Config and deployment: infrastructure as code for cloud deploy.
Flow:
0. Login/Sign up to the application & Dashboard view
User submits PDF/email; API routes in Flask/FastAPI.
Ingest module extracts text, metadata.
RAG/langchain module and policy rules evaluate content.
Risk scores , appetite Scoring, and structured output generated
- UI shows interactive items and downloadable reports; logs and observability hooks track runtime.
