Challenges

  • Processes are flooded with unstructured, multilingual, and handwritten documents (scanned files) that overwhelm conventional OCR and Vision Language Models.​
  • Manual document handling and extensive post-processing requirements for traditional OCR is inefficient and time-consuming.​
  • The average automation processing is highly error-prone due to lack of reasoning and hallucinations.​
  • Static models fail to evolve with new formats and lack feedback loops, blocking continuous performance improvement.​

Solutions

  • An Agentic Autonomous Platform backed by transformer-based VLMs designed to read complex, unstructured documents with images and layout preservation.​
  • Utilizes a multi-agent architecture (Client Agent, OCR Agent, AI Governance Agent, Reinforcement Agent) to coordinate task-specific goals end-to-end.​
  • Continuously learns from user feedback and corrections to optimize prompts and workflows via Reinforcement Learning (RL).​
  • Ensures strict compliance (HIPAA, GDPR) through role-based access control, data redaction, and end-to-end tracing/logging of decisions.​

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