Use Cases

We help teams build and scale Elasticsearch and OpenSearch search experiences that are fast, relevant, and reliable:

• Full-text search across applications, docs, and catalogs
• Faceted search, filters, and relevance tuning
• Autocomplete, suggestions, and did-you-mean
• Index design and mapping for search quality
• Multi-tenant and multi-index search architectures

From e-commerce and content platforms to internal tools, we design search that users trust.

Centralized logs, metrics, and traces with sustainable design and cost control:

• Log aggregation, retention, and analysis
• APM, RUM, and application performance visibility
• Alerting, anomaly detection, and on-call workflows
• Reducing ingestion and storage costs without losing signal
• Boundaries between logs, metrics, and traces across Elastic, OpenSearch, Prometheus, Grafana, Datadog, Fluent Bit, and OpenTelemetry

We focus on observability that stays maintainable and cost-effective as it grows.

Many deep Elasticsearch and OpenSearch problems start with SQL-shaped assumptions, uncontrolled mappings, and indexes that no longer match how the system is queried:

• Mapping explosion and accidental dynamic fields
• Field type mistakes that make search or aggregation expensive
• Oversharding, undersharding, and index sprawl
• Index template drift across environments and versions
• Data modeling that needs denormalized, query-shaped documents instead of relational thinking

The right fix is often schema and query design, not a larger cluster.

Secure clusters and audit-ready configurations:

• Authentication (native, SAML, OIDC, LDAP)
• Role-based access control and field-level security
• Encryption in transit and at rest
• Audit logging and compliance (SOC2, HIPAA, etc.)
• Hardening and least-privilege design

We help you meet security and compliance requirements without sacrificing usability.

Safe version upgrades and platform migrations:

• Elasticsearch version upgrades (e.g., 7.x to 8.x) with minimal downtime
• Elastic to OpenSearch and OpenSearch to Elastic migration assessment and execution
• Amazon OpenSearch Service planning and managed-service tradeoff review
• Cluster-to-cluster and cloud migrations
• Reindexing strategies and data validation
• Post-migration tuning and optimization

We reduce risk and ensure data integrity throughout the process.

Clusters that handle growth without degrading:

• Query and aggregation optimization
• Shard sizing, index design, and capacity planning
• JVM, thread pools, and resource tuning
• Bottleneck identification and remediation
• Horizontal and vertical scaling strategies

We design for the workload you have today and the one you'll have in 12–24 months.

Lower spend without sacrificing capability:

• Right-sizing nodes and storage
• Reducing log and metric ingestion (sampling, filtering, retention)
• High-cardinality and duplicate log reduction
• Storage tiers and lifecycle policies (ILM)
• Tradeoff analysis: visibility vs. cost

Many of our cost audits pay for themselves within weeks.