AI

DocBrain

Our client was tasked with reading scanned images of handwritten medical records as a part of the operation his business was running and the way they had chosen to solve this problem was via allocation of human resources under the experience based assumption that humans are needed to read complicated handwriting and most importantly understand without disregarding the subtle nuances.

We proposed a solution that uses OCR to extract text from these medical records and understand content utilizing LLMs that operate within defined guardrails. We managed to finetune our approach to an extent where we reached an accuracy of close to 97% in information extracted while flagging scenarios where human intervention is recommended.

DocBrain

Key Performance Results

-90%
Processing Time
Reduced document processing from hours to minutes
97%
Extraction Accuracy
Achieved human-level accuracy on key data fields
50K+
Documents Processed
Documents processed monthly through the platform
$1.2M
Cost Savings
Annual savings in labor and error-related costs

The Challenge

A large enterprise was spending thousands of hours annually on manual data entry from contracts, invoices, and regulatory filings. Error rates were high, processing times were slow, and skilled employees were stuck doing repetitive work instead of value-added analysis.

Our Approach

1

Analyzed document types and extraction requirements across multiple business units.

2

Developed a custom OCR pipeline optimized for the client's specific document formats.

3

Built a RAG (Retrieval-Augmented Generation) system for intelligent document Q&A.

4

Trained document classification models to automatically route documents to appropriate workflows.

5

Created a human-in-the-loop review interface for handling edge cases and improving model accuracy.

Technology Stack

PythonFastAPIOpenAI GPT-4LangChainPineconeTesseract OCRReactPostgreSQL

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