Production-oriented AI,
carefully engineered.

Engineered a production-oriented autonomous AI data analyst platform using Python, FastAPI, and LangGraph, orchestrating 8 specialized AI agents for zero-shot intent classification, Text-to-SQL analytics, statistical anomaly detection, and cross-modal Vector RAG across 581,718+ (15m+ development) structured business records. Architected a 4-tier LLM routing architecture spanning local, cloud, and offline inference paths, implemented AST-validated read-only SQL execution, and connected quantitative SQL results with unstructured operational incident logs through hybrid retrieval, achieving 100% Recall@5. Integrated Critic Fact Validation, automated executive reporting, interactive Plotly.js visualizations, ReportLab PDF reports, Excel workbooks, and a 12-metric evaluation and observability framework achieving a 100% pass rate across defined target scenarios.

Architected a production-ready 12-layer multi-agent AI system featuring hybrid vector search across 15,000+ document chunks, AST-guarded SQL query execution, and automated n8n workflow triggers. Achieved a 35.4% overall RAGAS benchmark improvement (0.5648 → 0.7650), driving +151.7% Context Recall and +83.3% Context Precision through Multi-Query Expansion, BM25 + FAISS Hybrid Search, and RRF Reranking. Integrated sub-1ms semantic vector caching, dynamic cost-aware LLM routing, hallucination verification, and live OpenTelemetry telemetry.