1) Brokers supply location level Data in messy formats.
This adds significant complexity for an insurance company to analyse, process and to make use of the data.
2) Huge costs for insurers to convert these files.
Insurers outsource this conversion process. This involves paying high fees to third party vendors e.g converting to RMS specific file formats
3) Significant resources required for an Insurers to convert these files "in house".
Employees require a lot of time converting this data due to it being different for each file and the size of the file e.g. 25k+ locations in 1 file.
Whether managed in-house or outsourced, data processing introduces unnecessary delays, slowing down the underwriting workflow for an insurer.
1) Automated Data Transformation.
Automatically transforms exposure data into catastrophe modelling (e.g. RMS and AIR).
2) Intelligent Data Enrichment
AI determines missing exposure attributes and extracts key policy information from documents.
3) Fast Processing Speeds
Processes 1K+ locations per minute. eliminating bottlenecks and accelerates data transformation time.
4) Scalable AI Platform
Designed to expand into document intelligence, slip reading and broader insurance data manipulation tasks.
1) Purpose built for catastrophe modelling teams
generating model-ready input files for platforms such as RMSI and AIR without manual data preparation.
2) Secure AI
Built on open-source language models that can be deployed to a client's environment, ensuring sensitive data never leaves the safety and security of their network.
This also reduces processing costs for the client by cutting out commercial API'S usage fees.
3) Learns and improves over time
FileForge continuously refines its inference, column mapping accuracy as it processes more exposure data. It becomes a more effective tool for the client over time.
4) User Controlled Automation
Allows users to review and adjust AI-generated field mappings and extracted data, ensuring accuracy while maintaining full control for the client.
