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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Our Approach
We designed an AI-driven recommendation engine to personalize user experiences.
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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
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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
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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
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AI Recommendation Engine for E-commerce
Personalization powered by AI directly translated into revenue growth