We wrote the book
Good search understands a user's intent, not just what they type.
We wrote Relevant Search to help teams build smarter Elasticsearch and Solr applications.
One of the best and most engaging technical books I've ever read. — Trey Grainger, Lucidworks
Will help you solve real-world search relevance problems for Lucene-based search engines — Dimitrios Kouizes-Loukas, Bloomberg
Pioneers in machine learning
We built Elasticsearch Learning to Rank, which powers search at Yelp, Wikipedia, Snag, and others.
Since deploying learning to rank, we’ve seen a net 32% increase in conversion metrics across our historically lowest performing use-cases. — Jason Kowalewski, Sr Director of Engineering at Snag
Learning to rank can’t stay hidden away in obscure academic journals or multi-million dollar products. We’re making this stuff accessible to all search teams: opening up the black box for everyone to use”. — Doug Turnbull - OpenSource Connections CTO
We have the tools
We built Quepid, a search testing platform that takes the guesswork out of search relevancy.
Do NOT attempt a search project without it. — John Bickerstaff
Just want to understand why results are showing up in your search?
We support the community
We host a Slack Channel where practitioners support each other and share solutions.
The blog and other happenings
A guest blog from Alex Addison on how LexisNexis UK are building production-ready infrastructure for Solr using AWS autoscaling groups, Terraform and Zookeeper.
Search is a deceptively complex field, where competence is hard-won through training, practice, and experience. In that vein dear friends, I've exhumed the venerable "Falsehoods Programmers Believe" party from 4 years ago to bring you one about, no less, Search.
We won't address machine learning illiteracy in search if we can't go beyond buzzwords. We need to teach concrete, specific techniques.
Brandwatch and LexisNexis tell us about their experiences migrating to Solr, and the usual Q&A and networking
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