Enterprise RAG AI: Private Doc Intelligence A fast-growing B2B software firm needed a secure, enterprise-grade Retrieval-Augmented Generation (RAG) system to query thousands of confidential internal documents (legal contracts, technical architecture specs, Notion pages, and employee SOPs) in real time without third-party data leakage.

Key Challenges:

High hallucination risk and irrelevant context retrieval using standard keyword search.

Managing multi-format files (scanned PDFs, tables, markdown) with strict access control based on user hierarchy.

Technical Implementation & Solution: • Engineered an automated ingestion pipeline in Python with LlamaIndex and LangChain for semantic chunking and metadata preservation. • Integrated a hybrid vector search architecture pairing dense embeddings (OpenAI text-embedding-3) with sparse BM25 indexing in Qdrant. • Added a cross-encoder re-ranking layer (Cohere Re-ranker) to prune low-relevance chunks, reducing API token costs by 40%. • Built robust Role-Based Access Control (RBAC) via FastAPI endpoints, ensuring departmental data isolation.

Results & Impact: • Dropped internal information retrieval time from 20 minutes to under 4 seconds. • Attained 98% factual precision with traceable in-line page citations. • Full on-prem Docker deployment ensuring 100% private data security.

I'd test revoking a user's document access mid-conversation. In your RBAC flow, do cached retrieval results get rechecked against current permissions before they're sent to the reranker or model?

AI Fraud Detection: Real-Time Risk Engine A digital payments startup processing over 100,000 daily transactions needed a high-throughput, low-latency AI fraud detection engine to intercept fraudulent charges, account takeovers (ATO), and identity theft in real time without creating friction for legitimate customers.

Key Challenges:

Reducing high false-positive rates that caused cart abandonment and user churn.

Evaluating complex behavioral features and payment metadata in under 60 milliseconds.

Adapting to rapidly shifting fraud tactics without requiring full model retraining.

Technical Implementation & Solution: • Real-Time Feature Pipeline: Engineered an ultra-fast streaming pipeline using Apache Kafka, Redis, and FastAPI to compute transaction velocity and device fingerprints on the fly. • Hybrid Machine Learning Architecture: Deployed an ensemble model combining XGBoost, Isolation Forests, and Graph Neural Networks (GNN) to uncover hidden mule-account rings. • Explainable AI (XAI): Integrated SHAP values into the decision engine, providing compliance analysts with immediate human-readable reasons for flagged transactions. • Automated Webhook Routing: Created dynamic webhooks to instantly trigger Step-Up 2FA, soft declines, or outright blocks based on real-time risk scores.

Results & Impact: • Slashed fraudulent chargeback losses by 43% in the first quarter. • Reduced false-positive transaction declines by 28%. • Achieved sub-45ms average inference latency across peak payment volumes.