jagadeesha

Jagadeesha co-founded arkavix to close the gap between a convincing AI demo and a system anyone actually runs. His work sits in the narrow and critical space where machine learning meets production data — evaluation harnesses, drift detection, feature platforms, and the guardrails that keep stochastic models from becoming stochastic liabilities.

Jagadeesha co-founded arkavix after years of watching enterprise AI and data initiatives stall between a pilot and a product. His standard is that every ML or LLM feature arkavix ships has an eval that runs in CI, a cost model that survives the first month's invoice, and a handover plan written before the first line of production code. He leads the firm's applied intelligence and data engineering practice — from feature stores and training pipelines to retrieval architectures and model serving. His background spans the full depth of the modern data stack: warehousing, streaming, ML platforms, and the LLM infrastructure that has quietly become a first-class citizen alongside them.