Built an agentic RAG system that forces ancient spiritual literature to interoperate with modern LLM retrieval. Tuned a precise AI model to maintain absolute stillness over generic chatbot eagerness.
Ancient wisdom sits locked in Sanskrit texts most people can't access. Modern LLMs hallucinate when asked about philosophy or spiritual practice. The gap between technology and timeless knowledge felt solvable.
I built Sadhaka AI as a RAG system grounded in actual texts—not synthetic summaries. The goal: make ancient teachings discoverable and contextual without diluting their meaning.
RAG pipeline grounded in source texts. No hallucinations on philosophical concepts. Context-aware responses that cite original sources.
Detailed technical schematics and documentation for Sadhaka AI are proprietary and available upon request for deep-dive discussions.
Technical constraints forced creative solutions. We optimized for LLM from day one, which meant rethinking architecture at every layer. Shipped incrementally, validated with real users, and scaled what worked.
Executed deep semantic search and massive RAG implementation across an ancient text corpus
AI products need to be grounded in real constraints. Hallucinations kill trust. Accuracy at scale beats feature bloat.
A methodology for moving AI from experiments to operating systems. Four phases, built on lessons from digital and cloud transformation waves, tested across healthcare, consumer tech, and agencies.