Healthcare · Case study
Multimodal Document Intelligence
Vision + LLM pipeline that extracts fields from clinical packets, tables, and handwriting with reviewer queues and audit logs.
Outcome: 4x intake throughput
The challenge
Intake teams keyed data from mixed PDFs, scans, and handwritten packets. Backlogs grew, error rates climbed, and managers lacked visibility into exceptions.
Our solution
We delivered a multimodal document pipeline: OCR and vision models (including Hugging Face / PyTorch components), LLM field mapping via OpenAI and Azure OpenAI, confidence scoring, and a React review queue with role-based access and full extraction audit trails.
Results
- Intake throughput improved about 4x
- Reviewers focus on low-confidence exceptions
- Structured fields feed downstream clinical systems
- Clear evidence of who approved each extraction
Tech stack
Core technologies used on this engagement.
Python
OpenAI
Azure OpenAI
Hugging Face
PyTorch
PostgreSQL
React
Docker
AWS
Services involved
Capabilities that powered this delivery.
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Voice agents, agentic RAG, document intelligence, and automation built for real workflows.
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