Content and product search
We design text search, filtering and ranking for documents, content and products around the application’s needs.
Search and analyze with OpenSearch.
We build content search, log investigation and data analysis applications with open-source OpenSearch.
Tell us what you needWhere it fits.
We assess potential uses for OpenSearch against your work and existing systems.
Logs and dashboards
We develop queries and dashboards that help teams search logs and investigate them by structured fields.
Vector and hybrid search
With suitable models and versions, we can assess meaning-based search alongside keyword search.
How we can help.
We define the scope around your needs, existing systems and team priorities.
Data and index preparation
We review data sources, field mappings and indexing flows together and define update, reindexing and retention requirements.
Explore AI-assisted development with AI LABSSearch and application integration
We design queries, filters and ranking, then connect search to existing applications. For vector and hybrid search, we review version, model, plugin and search pipeline requirements.
Explore software development with Reformo (opens in a new tab)Operational visibility
We plan log investigation, dashboards, suitable alerting plugins and access with your teams. For migrations, we assess version, API and plugin compatibility.
Explore training and mentoring with HGM Labs (opens in a new tab)Example scenarios.
These examples illustrate what could be built for different needs.
Explore 16 use cases
- User query
- Text and vectors
- Search pipeline
- Ranked results
The embedding model, plugins and search pipeline are assessed for the installed version.
Investigate error logs
A team can filter application logs by time and service name to investigate relevant error records.
Internal content search
Employees can search the content they are permitted to access by keyword or, with an appropriate model, by meaning.
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.
Text and catalogue search4 examples
Search fields, language analysis and filters are configured for the application’s content.
- Product discovery. A catalogue can search product names and descriptions and narrow results by brand and category.
- Multilingual content. Help or publishing content can be searched with analysis settings suited to each language.
- Suggestions while typing. With suitable indexes and queries, product or content titles can be suggested as a user types.
- Search filters. Result counts for categories and other attributes can help users narrow a list.
Vector and hybrid search4 examples
Vector search requires the k-NN plugin and a suitable embedding model. If Neural Search is used to generate embeddings automatically, its plugin and model must also be configured. The hybrid search pipeline’s normalization processor was added in OpenSearch 2.10; other capabilities must be checked against the version.
- Semantic help search. Vectors created with a suitable model can retrieve help content similar to a user’s question.
- Hybrid product search. Keyword and vector results can be combined and ranked with supported search pipeline settings.
- Image similarity. Vectors produced by a compatible image model can be used to find similar catalogue images.
- Relevance testing. A team can compare query and ranking settings against sample searches and assess the results.
Logs, dashboards and alerts4 examples
Dashboards require OpenSearch Dashboards; the alerting and anomaly examples require their respective plugins. Each must be available and configured for the chosen version and deployment.
- Investigating errors. A team can filter logs by service, error type and time range to investigate relevant records.
- Log dashboard. OpenSearch Dashboards can visualize log data by service and time range.
- Threshold notification. With the Alerting plugin and a notification channel configured, an alert can be sent for a defined query or threshold.
- Unusual changes. The Anomaly Detection plugin can produce signals for changes in a selected time series; the team reviews the results.
Index and migration management4 examples
Availability of index lifecycle, security and migration tools depends on installed plugins, version and deployment.
- Log retention. Index lifecycle management can be used to plan log retention and deletion rules around business needs.
- Access-scoped results. Content search access rules are addressed in the application and the security features of the chosen deployment.
- Changing field mappings. A move to a new field structure can include indexing, reindexing and checks using sample queries.
- Migrating existing search. When moving from Elasticsearch or another system, version, API, index and plugin differences are assessed and data and queries validated.
Product documentation: OpenSearch normalization processor documentation (opens in a new tab) · OpenSearch vector search documentation (opens in a new tab)