ElasticSearch shoutouts

Testimonials from top launches

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Super
Christophe Pasquier
used this to buildSuperSuper
(389 points)
Elasticsearch serves as the backbone of Super's search capabilities, enabling fast and accurate retrieval of information across diverse data sources. Its flexibility allows us to handle complex queries generated by our AI system, while providing robust analytics on usage patterns. The graph-based architecture we built integrates Elasticsearch queries with LLM prompts, creating a powerful system that can search, analyze, and synthesize information. Elasticsearch's scalability and customization options have been crucial for local development, error handling, and continuous improvement of Super's performance. Simply put, Elasticsearch's speed and adaptability made it the ideal foundation for building Super's advanced knowledge retrieval system.
Pulse for Elasticsearch and OpenSearch
Maddasar Azim
We support Elasticsearch in all versions, and also Elasticsearch in our core
fn7 Helix
Shubham Pancholi
used this to buildHelixfn7 Helix
(701 points)
It gives vector database as well a search capability having all this in one DB is good.
Vitaly Aver
used this to buildLayer
(998 points)
We implemented Elasticsearch to power fast and flexible search capabilities, allowing users to quickly filter and retrieve large volumes of information.
Gan.AI
Anupreet Singh Lamba
used this to buildGan.AI
(509 points)
Detailed analytics and logs for error tracking and debugging
Gan.AI
Suvrat Bhooshan
used this to buildGan.AI
(181 points)
Detailed analytics and logs for error tracking and debugging
Gan.AI
Saloni Jaju
used this to buildGan.AI
(206 points)
Detailed analytics and logs for error tracking and debugging
Vectopus
Ramy Wafaa
used this to buildVectopus
(252 points)
Search engine is fully powered by elastic search and ai
Ontosight.ai
Sahil Arora
used this to buildOntosight.aiOntosight.ai
(111 points)
Its quite dependable and has a stronger community
Arthur Morillon
used this to buildWander
(101 points)
One of our key challenges was identifying duplicate events, and Elasticsearch proved to be the ideal solution for this task. We also leveraged it for our search bar functionality and some recommendation features. While we considered Algolia as an alternative, Elasticsearch offered better scalability for our needs.
Ellipsis Travel
Daniel Evans
used this to buildEllipsis TravelEllipsis Travel
(151 points)
ElasticSearch is the backbone for our geospatial data infrastructure.
Playbook
Daniel Zhang
used this to buildPlaybook x GPTPlaybook
(155 points)
Elastic provides the infrastructure that powers our search.
Goran Brkuljan
used this to buildNodeCosmos
(56 points)
A cornerstone for our searching and data analysis features. Elasticsearch allows our users to navigate and analyze large datasets with ease, enhancing the overall functionality of Nodecosmos.
resmate.io
Brian Savage
used this to buildresmate.io
(13 points)
High-performant full-text and document search, easy to use