Ravindra Harige
Over a decade building and modernizing production search systems, from large-scale enterprise retrieval to AI-native search and retrieval architecture.
Background
Ravindra holds a Master of Science in Artificial Intelligence from VU Amsterdam. His studies in natural language processing, machine learning, and the Semantic Web provided the foundation for his later work in structured data, information retrieval, and production search.
Professional experience
Ravindra's work in search has spanned software engineering, data science, and solution architecture, taking him from building retrieval systems and ranking models to translating product and customer needs into search architecture. Across media, legal, tax and regulatory, and patent-information systems, he encountered distinct retrieval and ranking challenges shaped by each domain.
At LexisNexis Intellectual Property, he defined a three-year search platform roadmap, built and led a 12-person engineering team, and directed development of a global patent-search platform running across a 230-node Elasticsearch cluster.
That experience shaped his understanding of where Lucene-based architectures perform well, where they begin to constrain product development, and what organizations need from a modern retrieval platform.
Founding Searchplex
In 2021, Ravindra founded Searchplex, a specialist search and retrieval engineering firm, after repeatedly seeing capable teams constrained by architectures that could no longer support their search and AI ambitions.
Today, Ravindra leads a distributed multidisciplinary team of search, AI, software, product, and research specialists. He works directly with CTOs and senior product and engineering leaders, from initial diagnosis and strategic alignment through architecture, hands-on implementation, evaluation, production rollout, and team handover. He remains directly involved in the technical decisions that most affect relevance, reliability, and long-term operating cost.
Perspective on modern retrieval
Ravindra sees the shift to AI-native retrieval as more than a feature upgrade. It is a structural change in how search and AI systems need to be designed.
Many organizations now operate a production lexical search system, a separate vector-search capability, and independent recommendation or personalization infrastructure. As these systems evolve separately, retrieval becomes fragmented: signals are duplicated, operational costs increase, and engineering teams spend more time maintaining integrations than improving relevance.
His approach is to treat search, RAG, recommendations, personalization, and AI agents as consumers of a shared Retrieval Foundation. That foundation brings source quality, retrieval modeling, ranking, freshness, permissions, and evaluation into a coherent architecture that can evolve with the organization's needs.
This framework guides his work on architecture reviews, technology evaluation, and production retrieval systems at Searchplex.
Speaking engagements
Ravindra presents and participates in industry discussions on production search and modern retrieval through webinars and podcasts, meetups, and conference talks, including Berlin Buzzwords and Vespa.ai Live.
View all events→Want to talk about search, retrieval, or Vespa?
The Searchplex team helps organizations evaluate production search and AI retrieval problems, clarify the architecture, and determine the right path forward.