Every case study shows the full journey: what problem was solved, which tech was used, how the architecture works, and what results were achieved. No toy demos.
A mid-size law firm in Lucknow spends 8+ hours per case manually searching through hundreds of contracts, NDAs, and legal agreements to find relevant clauses. Junior associates waste 40% of billable hours on document review instead of client work.
Built a RAG (Retrieval-Augmented Generation) system that ingests PDF legal documents, chunks them intelligently, stores vector embeddings, and answers natural language questions with cited sources. The system returns answers in under 3 seconds with accuracy above 92%.
The student learned chunking strategies, hybrid search, pgvector indexing, and how to build a production RAG system with proper source citation — not just a chatbot wrapper.
An e-commerce startup tracks sales across 3 platforms (Shopify, Amazon, Flipkart). The founder manually exports CSVs daily, spends 2 hours compiling reports, and still misses trends. They need a system that can answer "What sold best this week?" in natural language.
Built a multi-tool AI agent that connects to Shopify API, queries a PostgreSQL database, performs web searches for competitor pricing, and generates automated Slack reports. The agent reasons through complex queries like "Compare our top 5 products vs Amazon prices and suggest repricing."
The student mastered tool integration, agent reasoning loops, API authentication, and building a system that actually runs in production — not just a demo.
A SaaS company with 15,000 users has a 28% annual churn rate. They know churn is expensive but have no way to predict which users will leave. Customer success team reacts after cancellation, not before.
Built an ML pipeline that ingests user activity logs, engineers features (login frequency, feature usage, support tickets), trains a gradient boosting model, and serves predictions via a FastAPI endpoint. The model flags at-risk users 30 days before likely churn with 87% precision.
The student learned the full ML lifecycle: data cleaning, feature engineering, model selection, experiment tracking with MLflow, API deployment, and monitoring model performance over time.
A 200-bed hospital in Lucknow tracks patient admissions, bed occupancy, doctor schedules, and billing in separate Excel sheets. The administrator spends 3 hours daily compiling reports for the management. Decisions are made on outdated data.
Built a Power BI dashboard that connects to their MySQL database, visualizes real-time bed occupancy, patient flow, revenue by department, and doctor utilization. Management now gets a live dashboard instead of daily Excel reports.
The student learned data modeling, DAX calculations, DirectQuery vs Import mode tradeoffs, and how to design dashboards that non-technical stakeholders actually use.
A recruitment agency screens 500+ resumes per week for tech roles. Manual screening takes 40 hours/week. They need an AI tool that parses resumes, matches skills to job descriptions, and ranks candidates — with a web interface for their team.
Built a full-stack AI application: frontend React form for uploading resumes, backend FastAPI with LLM-powered parsing, skill extraction, job matching scoring, and a dashboard showing ranked candidates. Deployed with Docker and CI/CD.
The student learned the full-stack AI workflow: React frontend, FastAPI backend, LLM integration, vector search, Docker containerization, GitHub Actions CI/CD, and production deployment — the complete AI engineer skill set.
A coaching institute gets 50+ WhatsApp inquiries daily. The owner can't respond to all of them instantly. Many leads go cold because of 2-4 hour response delays. They need a 24/7 chatbot that answers FAQs and captures leads.
Built a multi-model chatbot with intelligent fallback chain (Gemini → Llama → Qwen → Phi), resume parsing, lead capture form, and WhatsApp integration. The chatbot handles 80% of queries without human intervention and sends hot leads directly to the owner's phone.
The student learned multi-model orchestration, fallback chains, lead capture workflows, webhook integration, and building a chatbot that serves a real business need — not just a toy chatbot demo.
Every DSWallah student builds 5+ production-grade projects in 12 weeks. Start with a free demo class and see what you'll create.