Case Studies
AI- Powered Customer Support Automation
AI-first automation transformed support from a cost center into a scalable system.
Our Approach
We identified an opportunity to deploy a Generative AI-powered support assistant integrated into their existing system.
Steps:
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Analyzed historical support tickets (100k+ dataset)
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Designed NLP pipeline for intent detection
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Implemented LLM-based response generation with guardrails
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Built human-in-the-loop fallback system
Solution
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AI Chatbot integrated with web app and CRM
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Knowledge base ingestion (docs, FAQs, tickets)
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Context-aware response generation
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Escalation logic for complex queries
Results
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65% of tickets resolved automatically
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Response time reduced from 24 hours -> under 2 minutes
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Support costs reduced by 40%
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Customer satisfaction improved significantly
Our Approach
We focused on building a predictive analytics system for patient risk and operational forecasting.
Steps:
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Cleaned and structured fragmented datasets
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Built ML models for risk prediction
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Designed real-time data pipelines
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Created dashboards for actionable insights
Solution
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Predictive models (patient risk scoring)
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Data pipeline (batch + real-time processing)
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Visualization dashboard for clinicians
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API Integration into existing systems
Results
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30% improvement in early risk detection
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25% reduction in operational inefficiencies
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Faster, data-driven decision-making
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Improved patient outcomes
Predictive Analytics for Healthcare Platform
Turning raw healthcare data into intelligence created measurable clinical and operational impact
AI Fraud Detection System (Fintech)
AI enabled proactive fraud prevention without compromising user experience
Our Approach
We implemented a real-time fraud detection system using machine learning
Steps:
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Analyzed transaction patterns
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Engineered behavioral features
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Built anomaly detection models
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Designed real-time decision engine
Solution
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ML-based fraud detection pipeline
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Real-time transaction scoring system
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Risk threshold tuning dashboard
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Continuous model retraining pipeline
Results
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Fraud detection accuracy improved by 45%
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False positives reduced by 35%
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Real-time decisioning under 200ms
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Increased platform trust and security
Our Approach
We designed an AI-driven recommendation engine to personalize user experiences.
Steps:
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Analyzed user behavior and purchase history
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Built collaborative filtering + hybrid models
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Integrated recommendation APIs
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Optimized for real-time personalization
Solution
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Personalized product recommendation engine
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Real-time behavior tracking system
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A/B testing framework for optimization
Results
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20% increase in conversion rate
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35% increase in average order value
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Improved user retention
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Higher engagement across platform
AI Recommendation Engine for E-commerce
Personalization powered by AI directly translated into revenue growth