Search & AI expertise for

Our mission is to provide cutting-edge Elasticsearch and OpenSearch consulting, full-text & vector search solutions. We specialize in offering custom services, software development, search relevance optimisation, architecting billion-document search systems, guiding and implementing research and development projects and providing comprehensive training in search technologies.

Our deep understanding of search ensures that you achieve unparalleled efficiency, accuracy, and precision with your search relevancy problems. Our solutions leverage the power of open-source software, including Elasticsearch, OpenSearch, Apache Lucene/Solr, Weaviate, Marqo, and Vespa, ensuring that you benefit from the most advanced tools available.

We also offer cutting-edge capabilities in retrieval-augmented generation (RAG) and LLM (Large Language Model) integration, enabling you to leverage the power of AI-driven search. By combining advanced retrieval techniques with generative models, we empower your business to access contextually relevant, high-quality information in real-time. Whether it's enhancing customer support with AI-powered chatbots or generating insightful, data-driven content, our solutions bring the future of search to your fingertips.

Beyond implementation, our consultancy services include ongoing support, ensuring that your search infrastructure evolves alongside your business. By staying ahead of the curve in search innovation, we help you harness the latest advancements in information retrieval, from scalable search architecture to machine learning-driven ranking improvements.

How AI Is Built Podcast

Search Systems at Scale : Avoiding Local Maxima and Other Engineering Lessons

Episode Synopsis: Modern search systems must balance performance, relevancy, and cost — requiring smart architectural decisions at every layer. While vector search is popular, hybrid approaches combining traditional text and vector methods often achieve better real-world results.

A strong architecture covers three key areas:

• Ingestion and Indexing: Batch vs. streaming decisions.
• Query Processing: Balancing understanding and speed.
• Analytics and Feedback: Enabling systematic improvement.

Testing must happen against real production data, using golden sets and A/B testing to avoid local maxima — improving one area at the expense of another. Ultimately, it’s about finding the right balance between corpus size, latency, and cost while keeping systems manageable and results strong.

Success relies on deep query understanding, structured relevancy testing (not anecdotal fixes), and strong data governance. Even 1–2% performance gains can be significant at scale.