InsigthFlow

Long Description The Problem Modern e-commerce enterprises generate massive volumes of fragmented operational data. Extracting actionable insights typically requires specialized data teams or complex SQL knowledge. While integrating Large Language Models (LLMs) offers an intuitive solution, exposing raw database schemas directly to AI agents introduces severe vulnerabilities, including SQL injections, out-of-context computational abuse, and internal architecture leaking. Our Solution: DevTacOS DevTacOS is an enterprise-grade, secure Text-to-SQL analytics engine designed to democratize data access for business decision-makers without compromising infrastructure integrity. Our architecture is built upon three core pillars: High-Performance Data Engineering: We treated raw dataset files as the initial ingestion layer of our Data Lake. We engineered a robust data pipeline that automates cleaning, schema normalization, and strict deduplication. To eliminate the high computational overhead of complex, multi-table runtime JOINs, we modeled and populated a dedicated Physical Analytical Master Table. Aplanarizing the data dimensions into this optimized single-table structure dropped AI query execution latency to milliseconds, shielding transactional databases from stress. Intelligent Security Bastioning: Security is baked into our backend network perimeter. The application implements an advanced security middleware that intercepts LLM-generated SQL queries, parsing them via Abstract Syntax Tree (AST) tokenization against a strict literal whitelist. Destructive operations (DROP, DELETE, INSERT, ALTER) are hard-blocked at the syntax level. Commercial Abstraction & Error Escape: To prevent malicious prompt engineering or out-of-context requests (e.g., asking the system to solve math puzzles or write poetry), we programmed a structural JSON escape rule.