Sematext uses its expertise to offer services around Search and Big Data Analytics. We offer Solr & Elasticsearch Production Support as well as Solr & Elasticsearch Development Support (aka Consulting). In our role as consultants we help our clients save precious time and money by using our experience to implement best practices, avoid unecessary errors and dead ends, design, implement, configure, and deploy efficient and scalable data processing and search architectures, and so on.

Our customers range from large, multinational Fortune 100 household name companies, governmental and financial institutions, to small and midsized busineses, as well as startups.

Professional Services

• Expert consulting around architecture design, best practices, scaling, performance, tuning, hardware requirements, search relevance, etc.
• Design, architecture, and development of Search & Big Data solutions using Solr/Elasticsearch/Lucene, Hadoop, HBase, Kafka, and other open-source technologies
• Code reviews, mentoring, and troubleshooting
• Migration from FAST ESP and other commercial solutions to Solr or Elasticsearch
• Evaluation of open-source and commercial search solutions

During our engagements, especially those focused on scalability and performance, we often use our SPM for Solr and Elasticsearch performance monitoring and alerting solution to ensure we end up with the most optimal indexing and search setup for our clients.

SPM monitors all performance metrics for Solr/SolrCloud and Elasticsearch, as well as for a number of other technologies, and is thus invaluable to both us and our clients during and after the engagement. During our engagements focused on search relevance we often rely on our Site Search Analytics solution to understand various search metrics that are indicative of search results quality, user search experience and behaviour, etc. Using this tool we can actually measure the quality of relevance and, ultimately, the effect it has on user experience. With this tool we can visually see the before and after metrics and understand which search relevance-related “knobs” affect quality and how much.