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
In 2012 I first saw OpenNLP, and was both excited by it, but also appalled by the documentation. I wrote this blog post in 2012, but turns out I never actually published it! So here it is, updated for OpenNLP 1.9.0!
We share our thoughts on the Solr OpenNLP Integrations
We share our attempts to implement partial phrase highlighting in Solr
Explore key considerations for leveraging your existing data in conjunction with open third-party content in order to extract valuable insights through methods of analysis and visualization.
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