I spent 10 years building enterprise search for clients. Then I open-sourced all of it.

Every enterprise search project I did in the last decade ended the same way. A search engine (Solr or Elasticsearch), a pile of glue code for facets and relevance, then, in recent years, a RAG pipeline bolted on the side, and finally an agent framework on top of that. Three stacks, three sets of bugs, one recurring client requirement: the data cannot leave our network.

After repeating that build enough times, I turned it into a single platform. This year I open-sourced the whole thing under Apache 2.0: Viglet Turing ES.

This post is about the three design decisions that I think matter most, and how to try it in one command.

Decision 1: run on the engine you already have

Most teams evaluating "AI search" already operate Solr or Elasticsearch. Asking them to migrate to a new engine just to get RAG is a non-starter.