Intelligent Document Segmentation, Medical AI & Workflow Scaling for Phelix AI

DCube is the technology and deployment partner for Phelix AI, delivering AI-powered document intelligence and conversational medical assistance at production scale. Beyond developing segmentation algorithms and LLM-based chatbots, DCube manages infrastructure and workflow scaling to process thousands of insurance claims per hour reliably and securely.

The Client

Client Name: Phelix AI
Industry: Healthcare
Region: Canada
Company Size: Enterprise

The Challenge

Phelix AI operates in a high-volume healthcare and insurance environment where large numbers of unstructured claims and medical documents must be processed accurately and at speed. The system needed to scale reliably under fluctuating workloads while maintaining extraction accuracy, low latency, and compliance-ready deployment—without service degradation as claim volumes increased.

The Solution

DCube designed and implemented a scalable document intelligence and conversational AI architecture, combining robust document segmentation pipelines with LLM-based medical assistance. In parallel, DCube engineered and manages the deployment and scaling infrastructure, enabling Phelix workflows to handle thousands of concurrent claims per hour with high availability and performance.

Key Features

  • Document Segmentation & Information Extraction: Automated extraction of key insurance and clinical data from complex, multi-page claims documents
  • LLM-Based Medical Assistance Chatbot: Context-aware conversational interface for querying medical and workflow-related information
  • High-Throughput Workflow Processing: Scalable pipelines optimized to process thousands of claims per hour
  • Production-Grade Deployment: Infrastructure designed for reliability, monitoring, and rapid scaling
  • End-to-End Integration: Seamless integration across document ingestion, AI inference, and downstream healthcare workflows

Technologies Used

  • OCR & Document Vision Models
  • Natural Language Processing (NLP)
  • Large Language Models (LLMs) for medical assistance
  • Python
  • Scalable Deployment & Inference Infrastructure (production-grade, workflow-oriented)

Results & Impact

  • Enabled high-volume, automated processing of insurance claims with consistent accuracy
  • Supported thousands of claims per hour without performance degradation
  • Reduced manual intervention and operational bottlenecks
  • Delivered reliable, scalable AI infrastructure suitable for healthcare production environments
  • Positioned Phelix AI for rapid growth with DCube as a long-term deployment and scaling partner

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