Case Studies
Real examples of AI transformation across industries
Aircraft Lease Return Automation
Challenge: Manual lease return documentation review took 3-4 weeks per aircraft. Compliance gaps risked costly penalties and delays.
Solution: AI-powered document processing system reviewed lease agreements, generated compliance checklists, and detected discrepancies against EASA Part-145 and Part-M standards.
Results:
- ✓ Review time reduced from 3-4 weeks to under 1 week (70% reduction)
- ✓ Zero missed compliance items across entire fleet
- ✓ EASA Part-145 and Part-M compliant workflow
- ✓ Scalable to entire fleet with no additional headcount
Component Derating Analysis
Challenge: Design engineers spent weeks manually reviewing BOMs to verify component safety margins. Inconsistencies led to field failures and redesigns.
Solution: Built a BOM analysis tool that automated component derating assessment, scoring each part against its rated specifications and flagging risks.
Results:
- ✓ Design review time reduced by 60%
- ✓ Automated risk scoring for every component
- ✓ Consistent, repeatable derating analysis across all BOMs
- ✓ Eliminated field failure related redesigns
Liquid Stability Classification
Challenge: Researchers needed to classify liquid stability and phase separation dynamics manually, a subjective and time-consuming process.
Solution: Developed an automated image processing and ML pipeline to classify liquid stability from visual data, enabling objective, repeatable analysis.
Results:
- ✓ Objective, automated classification replacing manual inspection
- ✓ ADIPEC conference paper published
- ✓ Patent filed for the classification methodology
- ✓ Enables high-throughput experimentation
Real-Time CCTV Motion Detection
Challenge: Client needed a cost-effective motion detection system for legacy CCTV infrastructure that could run in real-time with configurable sensitivity.
Solution: Built a hybrid system combining traditional CV (frame differencing, contours) with YOLO-based deep learning detection, served via Flask with WebSocket streaming.
Results:
- ✓ Real-time detection at configurable sensitivity levels
- ✓ Reduced storage by 60% via smart recording triggers
- ✓ Dual detection pipeline (CV + deep learning) for reliability
- ✓ Deployed on existing hardware, no infrastructure upgrades needed
Arabic Named Entity Recognition (MEDALII)
Challenge: Arabic NER remains challenging due to rich morphology and dialectal variation. Existing models struggled with 7 entity types across diverse domains.
Solution: Built a comprehensive evaluation framework comparing 3 prompt strategies (LTNER-SUA, LTNER-AAA, GoLLIE) across multiple LLM backends with Arabic clitic-aware matching.
Results:
- ✓ Evaluated 3 prompt representations across 7 entity types
- ✓ Multi-backend LLM routing (OpenAI, OpenRouter, Falcon)
- ✓ Arabic clitic normalization for accurate evaluation
- ✓ Phase 2 integrated best agent (Google ADK) into active learning pipeline
Legal Document QA System
Challenge: Legal professionals spent hours searching through multi-document contracts and rulings to find specific clauses and precedents.
Solution: Built a RAG-based legal document Q&A system using LlamaIndex that answers natural language queries with citation-backed responses from uploaded documents.
Results:
- ✓ Instant answers from multi-document legal repositories
- ✓ Citation-backed responses for audit trail
- ✓ Streamlit web interface for easy document upload and query
- ✓ Persistent document storage with REST API access
Skeleton-Based Action Recognition
Challenge: Client needed to classify human actions from video feeds for security and HCI applications, requiring real-time performance.
Solution: Implemented multiple deep learning architectures (LSTM, GRU, Transformer) for skeleton-based action recognition using Mediapipe for real-time pose estimation.
Results:
- ✓ Multi-architecture support (LSTM, GRU, Transformer)
- ✓ Real-time inference via Mediapipe skeleton extraction
- ✓ Video streaming pipeline for live classification
- ✓ Benchmarked across all architectures for accuracy vs. speed trade-offs
PSX Trading Analysis Platform
Challenge: Building an end-to-end AI-powered trading analysis platform for the Pakistan Stock Exchange (PSX). Required real-time data ingestion, multi-model ML predictions, reinforcement learning portfolio optimization, and a multi-agent AI pipeline — all integrated into interactive dashboards.
Solution: Developed a comprehensive platform with 222 Python modules spanning ML models (RGR01 for 5-day price regression, RGR02 for 15-day forecasting, CLF02 for direction classification at 79.36% accuracy), reinforcement learning for portfolio allocation across top 30 PSX tickers, a Google ADK-based multi-agent pipeline (Data Specialist → Market Analyst), 40+ financial analysis tools, and a full mock trading simulator with real-time price integration.
Results:
- ✓ CLF02 direction classifier achieving 79.36% test accuracy on PSX data
- ✓ Dual-ML pipeline (RGR01/RGR02) for 5-day and 15-day price forecasting
- ✓ RL-based portfolio optimizer allocating across KMIALLSHR index constituents
- ✓ Multi-agent AI pipeline using Google ADK for automated market analysis
- ✓ Interactive Streamlit dashboards for data, technical, sentiment, and forecast analysis
- ✓ Mock trading platform with live price integration, portfolio tracking, and P&L analytics
- ✓ Complete M0-M9 trading course covering fundamentals through RL and Alpaca integration
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