Ravindra Harige
Over a decade building and modernizing production search systems, from large-scale enterprise retrieval to AI-native search and Vespa migration.
Background
Ravindra's path into search began with a Master's in Artificial Intelligence at VU University Amsterdam, where his focus on NLP, machine learning, Semantic Web, and Knowledge Representation shaped how he thought about structured data and retrieval from the start.
Professional experience
After graduating, he spent over a decade working through search problems across industries, mastering content understanding, data engineering, query understanding, relevancy, ranking, and system operations.
At LexisNexis Intellectual Property, he moved from software engineering to data science and eventually to Senior Solutions Architect, leading development of a global IP search platform built on a 230-node Elasticsearch cluster that handled millions of daily queries.
His work there gave him a deep understanding of where Lucene-era architecture performs well and exactly where it begins to break.
Founding Searchplex
In 2021, Ravindra founded Searchplex to address a gap he had watched persist across organizations for years.
The problem was never the engineers. It was the architecture. Teams were being asked to deliver better search, smarter recommendations, and trustworthy AI experiences on top of systems that were never designed for today's retrieval demands. Legacy stacks were being stretched. Vector search was being bolted on as an afterthought. RAG was being treated as a shortcut even when the underlying retrieval quality couldn't support it.
Searchplex was built to help organizations handle that transition properly, with modern retrieval architecture, real production experience, and an honest view of what actually needs to change.
What I believe
The shift to AI-native retrieval is not a feature upgrade. It is a structural change in how search systems need to be designed.
Most organizations running search today have three separate problems they are treating as one: a production lexical search system that works but can't evolve, a vector search experiment that doesn't integrate cleanly, and a recommendation or personalization layer that has grown its own infrastructure entirely. The result is fragmented stacks, duplicated operational costs, and engineering teams spending more time on glue code than on relevance.
The opportunity, and the work I find most interesting, is collapsing that complexity into a coherent retrieval architecture that handles all of it. One system with shared signals, native ranking logic, real-time updates, and room to experiment. That is where search is going. Most organizations need a guide who has shipped it in production, not a vendor with a slide deck.
That is what Searchplex is for.
Want to talk about search, retrieval, or Vespa?
If your team is working through a production search or AI retrieval problem, I can help you evaluate the architecture and the path forward.