Our Projects & Portfolio

Challenge

A major law firm was struggling with complex litigation cases involving large volumes of legal documents, pleadings, and evidence files. Manual document review and timeline creation was taking 2-3 weeks per case, with high error rates in identifying key events and document relationships. Lawyers were spending 60% of their time on document organization rather than legal strategy.

Solution

Implemented an AI-powered legal document management system using. The system automatically processes legal documents, extracts key events using advanced AI prompts, generates comprehensive case timelines, and provides conversational AI assistance for case analysis. Features include:

• Intelligent Document Processing
• AI Timeline Generation
• Smart Document Mapping
• Conversational AI Assistant

Results

• 85% reduction in document review time (from 2-3 weeks to 2-3 days)
• 90% improvement in timeline accuracy with automated dispute detection
• 70% increase in lawyer productivity by eliminating manual document organization
• 99.2% client satisfaction with faster case preparation and more accurate timelines

Challenge

Enterprise-level companies often striggle with invoice processing workflows that requires 3-5 days per document review, resulting in high operational costs, processing bottlenecks, and inconsistent data extraction accuracy across multiple document formats and languages.

Solution

Implemented a comprehensive AI-powered document processing system combining Azure Form Recognizer, custom Named Entity Recognition (NER) models, and GPT-4 integration to automatically extract, validate, and enhance invoice data from PDFs, images, and scanned documents with multi-language support.

Results

• 90%+ reduction in processing time (from 3-5 days to minutes)
• 95%+ field extraction accuracy through multi-model validation
• 99.8% processing reliability with comprehensive error handling
• Multi-tenant scalability supporting diverse organizational requirements

Challenge

A major plumbing supply company needed to process thousands of invoices daily from multiple suppliers, with manual data entry taking 3-5 days per invoice batch and high error rates in financial reconciliation. The company struggled with:

• Manual PDF-to-Excel conversion for 12+ different supplier formats
• Complex invoice item mapping to standardized product codes
• Time-consuming QuickBooks integration and tax distribution
• Limited visibility into financial data through complex database queries
• No natural language interface for business users

Solution

Implemented a comprehensive end-to-end invoice processing automation workflow using FastAPI, PostgreSQL, and Google Gemini AI. The system features:

• Automated Invoice Ingestion: Picks invoices directly from email inbox, identifies suppliers automatically, and processes 12+ different supplier formats with 99.5% accuracy
• Intelligent Supplier Recognition: AI-powered supplier identification and routing to appropriate processing modules
• End-to-End Processing: Complete workflow from inbox to QuickBooks integration with automated PDF-to-Excel conversion, item mapping, and tax distribution
• AI-Powered Natural Language Q&A: Direct database queries using Google Gemini for instant financial insights and business intelligence
• Advanced QuickBooks Integration: Automated tax distribution, class-based splitting, and batch entry generation
• Real-time Price Comparison: Monthly supplier price analysis with percentage increase calculations
• Enterprise-Grade Performance: Unlimited performance architecture with proactive resource management

Results

• 85% reduction in processing time (from 3-5 days to hours)
• 99.5% accuracy improvement in invoice data extraction
• 70% faster QuickBooks integration with automated tax distribution
• $500K+ annual cost savings through automation and error reduction
• Natural language database queries for instant business insights
• Unlimited scalability with enterprise-grade performance optimizations

Challenge

Researchers and students in Islamic studies needed an intelligent way to search and query the vast Shamela digital library, one of the largest repositories of Islamic texts. Manual searching through thousands of scholarly works was time-consuming and inefficient, with limited ability to find relevant passages across different texts and languages.

Solution

Developed an advanced RAG (Retrieval-Augmented Generation) system built on the Shamela digital library, featuring multilingual support and intelligent conversational AI for Islamic text research. The system provides:

• Intelligent Search: AI-powered semantic search through thousands of Islamic texts
• Multilingual Support: Processing and querying in multiple languages
• Conversational Interface: Natural language queries for complex research questions
• Academic Integration: Specialized for Islamic scholarship and research
• Large-Scale Processing: Handling one of the largest digital libraries of Islamic content

Results

• 90%+ improvement in research efficiency for Islamic studies
• Multilingual support for diverse scholarly communities
• Access to thousands of Islamic texts through intelligent search
• Enhanced academic research capabilities for students and scholars
• Live deployment at ShamelaGPT.com