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 Kouzis-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 we're adapting to continue empowering search teams with online consultancy & training
Does your pet feature actually help users? Feedback debt, like technical debt, eventually needs to be repaid.
Now that you are using Quepid to collaborate with members of your team by sharing your cases with other folks, you need a way to view basic information for *ALL* of your owned and shared cases and navigate to those cases within Quepid. That is why we have the Multi Case Dashboard.
Go hands on with machine learning tools to improve relevance. Using an open source search engine, we see our models come to life, see the pitfalls of letting machines control relevance, and work to mitigate those pitfalls. From Hello World to Learning to Rank Lessons you can avoid learning the hard way. See how you can apply open source tooling to optimize your search results with machine learning. Ideal for production focused data scientists, relevance engineers, machine learning engineers, or search developers.
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