Problem:

Insurance companies and assessment teams face significant operational challenges when processing accident related images from claim submissions. The manual review process for fire incidents, property damage, and other accidents is time-consuming and inconsistent, with different reviewers often describing identical images in varying ways.

This lack of standardization creates bottlenecks in claim processing, increases operational costs, and heavily depends on subjective human judgment. Compiling these images and descriptions into structured claim documents requires substantial human effort, delaying critical decision-making and reducing overall efficiency in the claims workflow.

Solution:

We developed an AI-powered vision-based application that automatically analyzes accident images and generates accurate, context-aware descriptions. The system leverages advanced Vision Language Models, including OpenAI's Vision models for cloud deployment or LLaVA for self-hosted environments4to identify accident-related elements and produce standardized captions.

Architecture:

Image Upload Intuitive interface for individual or bulk uploads with incident grouping, validation, and queuing. Includes UI for processing and in page report editing.

Image Analysis Vision Language Models interpret visual content, understand accident context, and generate structured captions describing visible damage and incident nature.

Report Generation Captions and images automatically injected into predefined templates, producing standardized documents ready for review or export.

Impact & Results:

The AI vision-based solution automates time-consuming aspects of accident and insurance claim processing, delivering accurate and context-aware descriptions that reduce human effort and turnaround time. By improving operational efficiency and report standardization, the system enables faster decision-making workflows and positions organizations for scalable growth in claims management