Case studies
A portfolio of projects, each told in a different business context.
Each case below focuses on the problem, the solution, and the outcome.
Selected portfolio
Project context, challenge, approach, and outcome.
Selected work
Case studies spanning insurance, finance, manufacturing, and AI product delivery.
Each card is structured to make the context obvious and the business relevance easy to scan.
Insurance AI
Policy adviser prediction system
Built to identify advisers likely to stay active by analyzing behavior, collaboration patterns, and engagement signals.
Challenge
Retain high-value advisers before churn becomes visible to leadership.
Approach
Feature engineering, time-series thinking, Logistic Regression, K-Means, and an Azure-hosted Flask application with Power BI integration.
Outcome
Enabled targeted engagement actions and a more proactive retention workflow.
Insurance Operations
Adjuster policy-selling intelligence
Designed a machine learning model to predict when an adjuster would likely sell an insurance policy product to a client.
Challenge
Surface sales opportunities from operational data without slowing field workflows.
Approach
Modeling with feature selection, validation, and deployment-friendly data pipelines connected to enterprise tooling.
Outcome
Improved decision support for field teams and sales prioritization.
Generative AI
Multi-agent domain assistant
Created a routed AI assistant that answers questions across Wikipedia, finance, medical, and insurance domains.
Challenge
Provide accurate, context-aware responses through multiple specialized agents instead of one generic model.
Approach
LangChain, LangGraph, prompt-based routing, retrieval support, and modular orchestration.
Outcome
Delivered a flexible assistant architecture that can scale to more domains.
Insurance AI
RAG-based insurance chatbot
Built an intelligent customer-facing chatbot for policy selection, claims guidance, and premium-related queries.
Challenge
Make insurance information instantly accessible while keeping responses domain-aware and relevant.
Approach
RAG pipelines, embeddings, fine-tuned LLM workflows, and document-backed retrieval with low-latency serving.
Outcome
Improved response quality and customer experience with a more useful self-service layer.
Computer Vision
Damage detection and segmentation POC
Developed a deep learning proof of concept for damage estimation, classification, and segmentation in images.
Challenge
Reduce manual inspection effort in claims, automotive, and property assessment workflows.
Approach
YOLOv8-based detection, segmentation techniques, training optimization, and API-ready deployment design.
Outcome
Reduced inspection time and created a stronger foundation for automated claims evaluation.
Finance Analytics
Customer retention propensity model
Built a model to estimate whether a newly onboarded user would actively trade during the first month after signup.
Challenge
Spot customers at risk of inactivity and improve early engagement.
Approach
Behavioral analysis, logistic modeling, random forest methods, and Power BI reporting for sales actioning.
Outcome
Helped teams focus outreach on higher-value customers with better conversion potential.
Manufacturing Forecasting
Inventory requirement forecasting
Created a planning model to forecast inventory needs using stock, production, and supply chain data.
Challenge
Balance dead stock risk against shortage risk in a production environment.
Approach
Historical analysis, demand forecasting logic, and a Flask web interface deployed in the cloud.
Outcome
Improved procurement planning and stock visibility for operations teams.
Retail Banking
Fixed deposit propensity model
Developed a classification model to identify customers most likely to open a high-value fixed deposit product.
Challenge
Help banking teams prioritize outreach using behavioral and financial indicators.
Approach
Machine learning classification, customer segmentation, and an Azure-deployed application layer.
Outcome
Improved targeting for personalized offers and product conversion planning.
Sports Analytics
Performance intelligence dashboard
Built a Power BI reporting experience combining official match, player, and team performance data.
Challenge
Turn raw sports statistics into a visually clear analysis experience for decision-makers.
Approach
Data collection, metric modeling, bar charts, heatmaps, and radar-style skill comparisons.
Outcome
Created a better view of trends, strengths, and head-to-head patterns.