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Case Study

AI-Powered Immigration Document Review

No of Users

7

Project Duration

18 weeks

About Client

Law Office of Kavitha Ramasamy is a U.S.-based immigration law firm specializing in visa applications and immigration services. The firm handles a high volume of client documentation, including petitions, supporting evidence, and compliance forms. They aim to improve operational efficiency, reduce manual review effort, and ensure accuracy in handling complex immigration cases.

Technology and Services

Python (FastAPI), LLM (Azure Open AI), Azure Form Recognizer, Sqllite3, Angular js

Industries

Legal (Immigration Law)

The solution demonstrated significant potential in reducing manual document review efforts and improving accuracy. The AI-assisted insights helped streamline workflows and provided confidence in handling complex immigration cases.

Metrics

  • 60–70% reduction in document review time
  • 85–90% accuracy in information extraction
  • Reduced manual errors in compliance checks
  • Faster case processing turnaround

Project Synopsis

LexiQA Immigra is an AI-powered document review system designed to assist immigration law firms in analyzing, validating, and extracting insights from legal documents such as visa applications. The platform leverages LLMs to enable intelligent querying, automated validation, and structured data extraction, reducing dependency on manual review processes.

The Challenge

  • High volume of complex immigration documents requiring manual review
  • Time-consuming verification of compliance requirements
  • Risk of human error in critical legal documentation
  • Unstructured data spread across PDFs and scanned files
  • Difficulty in quickly retrieving relevant information from large documents

The Solution

Built an AI-powered document processing pipeline:
PDF ingestion → Document Intelligence (OCR & structured extraction) → LLM processing
Leveraged Document Intelligence services (e.g., OCR/Form Recognizer) to:

  • Extract structured data from PDFs and scanned documents
  • Identify key fields such as applicant details, visa categories, and dates

Integrated Large Language Models (LLMs) to:

  • Interpret extracted data
  • Validate document completeness and consistency
  • Generate contextual insights and summaries

Automated

  • Key information extraction (names, visa types, dates, etc.)
  • Compliance checks based on predefined business rules

Designed and exposed REST APIs using FastAPI for seamless integration with downstream systems.
Ensured a modular, scalable, and cloud-agnostic architecture for future enhancements

The Result

  • Significant reduction in manual workload for legal teams
  • Faster document processing and decision-making
  • Improved accuracy in data extraction and validation
  • Scalable architecture ready for full production deployment
  • Enhanced user productivity and client satisfaction

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