Insights

Engineering teams often mistake giant context windows for a replacement for good architecture, but in practice, reliability decays as input grows and models confuse simple retrieval with actual reasoning. While LLMs excel at basic searches, they struggle to synthesize meaning or detect contradictions across massive, unstructured datasets. To build reliable production systems, we must move beyond context dumping and instead use specialized agents to isolate information and handle focused slices of data. By prioritizing structure over raw window size and using code generation for deterministic validation, teams can avoid "context rot" and build systems that actually scale.

Every enterprise wants AI. Almost none have the data understanding required to use it. At Turgon, the first agent we deploy is a self-learning Data Dictionary / Ontology Engine — a digital twin of your entire enterprise data landscape. It reads your systems, learns them, reasons about them, and becomes the foundation for every downstream AI workflow. If enterprises don’t start here, nothing else works.

Discover how AI integration streamlines operations and improves enterprise efficiency. Explore benefits, challenges, best practices, and real-world examples from leading companies.

Discover how Turgon AI Insights uses AI agents to revolutionize analytics. Learn about benefits, features, and real-world applications across retail, healthcare, finance, and marketing.

Discover why traditional MVP and linear product strategies fail for AI-first products. Learn how to design adaptive, loop-driven, and experiment-based frameworks for LLM-powered systems.

Discover how data migration experts leverage AI to streamline your infrastructure. Learn key migration steps, benefits, and real-world examples for efficient, secure data transitions.

CTO-level comparison of LangChain, LangGraph, and custom agent frameworks. Learn when to use each for production-grade LLM agents, observability, and scalability.