We design, modernize, and optimize search systems to improve relevance, performance, and scalability across enterprise, eCommerce, and AI-powered search.
Innovent designs and optimizes distributed search architectures, addressing relevance, performance, and scalability challenges to ensure consistent and reliable search systems.
FindTuner works with Elasticsearch, OpenSearch, and Solr to enhance search performance through intelligence, insights, and control. It enables teams to understand search behavior, apply targeted optimizations, and continuously refine relevance and ranking as data, user behavior, and business strategies evolve.
Modern product discovery requires more than traditional search alone. Learn how Large Language Models (LLMs) and BM25-based search engines work together to understand shopper intent, improve search relevance, and deliver more effective ecommerce search experiences.
Building a search proof of concept requires a different mindset than building a production system. This article shares practical OpenSearch tips for rapid prototyping, including vector embeddings, kNN search, hybrid search, indexing strategies, and UI considerations drawn from real-world experimentation.
Traditional search works well when users are looking for specific items. But workflows centered around building cohesive environments, such as set design and scene construction, introduce a different kind of retrieval problem. This article explores how OpenSearch, vector embeddings, and multimodal retrieval techniques were used to experiment with scene-oriented discovery and cohesive item selection.
Work with our team to solve relevance, performance, and scalability challenges and design a search platform that performs at scale.
