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
How to convert the _default managed schema in Solr v8.3.0 to the classic schema of yesterday
We explore what the basic unit of your search system should be. What *should* your document be? What 'result' in the SERP UI best satisfies your users?
In which we deploy Tika and Tesseract as a API in Solr, exposed via the /extract handler!
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