Enabling Computer Vision for Insurance Maintenance with AI Solutions

Kagen AI developed an AI-powered inspection system, integrating computer vision and automation technologies to enhance home insurance maintenance checks. The solution streamlined risk assessments, improved precision, and optimized operational workflows for scalable and real-time processing.

90% Inspection Accuracy Improved
30% Operational Efficiency Enhanced
89% Improved Precision

Business Requirements

Our client, a leading US-based risk assessment business serving Fortune 500 companies, specializes in insurance and financial risk management. They aimed to modernize home insurance maintenance checks by automating visual inspections, reducing manual workload, and enabling real-time risk assessment.The client required an AI-powered platform capable of:

Key requirements included:

Conducting automated home maintenance checks via image analysis.
Detecting and assessing residential utilities from images.
Ensuring seamless deployment with zero downtime and scalable infrastructure.

Solution

Kagen AI collaborated with the client to build a robust AI-powered inspection system integrating computer vision and automation technologies to streamline home insurance inspections.

Real-Time Voice Interaction

Implemented Twilio for seamless speech-to-text (STT) and text-to-speech (TTS) conversion.
Established an automated outbound calling system to initiate reminders and handle inbound queries.
Leveraged WebSocket for real-time audio streaming and communication.

Context Management

Integrated Redis stores and retrieves conversation history, ensuring continuity in multi-turn interactions.
Utilized vector databases (Weaviate) to match user queries with prior conversations for enhanced context awareness.

Natural Language Processing (NLP)

Integrated OpenAI’s LLM to process voice-to-text inputs and generate human-like responses.
Fine-tuned AI models for domain-specific conversations, ensuring relevant and accurate responses to associates.

Query Processing and Response Generation

Combined vector search with LLM to dynamically generate answers based on prior interactions.
Implemented caching mechanisms for frequently asked questions to reduce response time.
Used response templates to ensure consistency and clarity across different scenarios.