Text search
We design keyword search, filters and ranking for products and documents, considering language analysis and typo handling for the content.
Search and analyze with Elasticsearch.
We work on search, filtering and analysis for product, content and log data alongside your application.
Tell us what you needWhere it fits.
We assess potential uses for Elasticsearch against your work and existing systems.
Logs and data analysis
We work on indexes and queries to search, filter and summarize application logs and structured data.
Semantic and hybrid search
Depending on the need and the features available in the chosen version, we can assess vector search alongside keyword search.
How we can help.
We define the scope around your needs, existing systems and team priorities.
Data and index design
We review data sources, field mappings and indexing flows, including updates, reindexing and retention in the design.
Explore AI-assisted development with AI LABSSearch and relevance
We evaluate queries, filters and ranking against sample searches. Language analysis and, where useful, vector or hybrid search are selected with version, model and license requirements in mind.
Explore training and mentoring with HGM Labs (opens in a new tab)Integration and operations
We connect search or log flows to your applications and plan testing, access, dashboards, suitable alerts and operational needs within the project scope.
Explore software development with Reformo (opens in a new tab)Example scenarios.
These examples illustrate what could be built for different needs.
Explore 16 use cases
- Application logs
- Elasticsearch index
- Log query
- Kibana dashboard
Kibana and the required ingestion settings are configured separately.
Product search
A customer can search by product name or code and narrow results using fields such as category and brand.
Investigate application logs
A team can search error records for a selected time period and examine them by shared attributes.
More use cases.
These scenarios illustrate potential applications. We assess the requirements of the chosen version and deployment together at the start of a project.
Product and content search4 examples
Field mappings, language analysis and indexes are configured for the content being searched.
- Catalogue filters. Customers can search products, narrow results by brand, category and price range, and see filter counts.
- Product code search. An exact product code can be searched in a separate field while descriptions use full-text search.
- Spelling variations. Appropriate analysis and query settings can account for spelling errors or synonyms in search.
- Search suggestions. Product or content titles can be suggested as a user types, with fields and queries prepared for the task.
Semantic and hybrid search4 examples
Vector, model and hybrid ranking options depend on the Elasticsearch version, deployment and licenses for the features used.
- Meaning-based document search. Vectors produced with a suitable model can find document passages with a similar meaning to a question.
- Hybrid results. A knowledge centre can rank text and vector search candidates together using a supported method.
- Answers with sources. An application can pass authorized passages to a language model and show their sources to the user.
- Comparing relevance. A team can evaluate text, vector and hybrid results against sample queries and review ranking settings.
Logs and operational investigation4 examples
Version and license requirements are checked for the components and features used for ingestion, Kibana dashboards and alerts.
- Errors by service. A team can filter application logs by service, error type and time range.
- Tracing a request. Logs sharing a request identifier can be found to examine an operation across services.
- Operations dashboard. Summaries of logs can be visualized by service and time range using Kibana.
- Threshold alerts. With a supported alerting feature and notification channel configured, a notification can be generated for a defined error threshold.
Data lifecycle and access4 examples
Retention, field- or document-level access and migration options are assessed against the version, deployment and license.
- Updating source data. Product or content changes can flow to the index; lag between the source and search results is monitored.
- Retention periods. Index lifecycle rules can be designed around the retention periods required by the business and applicable regulations.
- Authorized content access. The application determines which documents a user may access; deployment security features and licenses are checked.
- Changing an index. When field structure changes, reindexing and validation using sample queries can be planned.
Product documentation: Elastic hybrid search documentation (opens in a new tab) · Elastic feature and license information (opens in a new tab)