Deep engineering across the stacks that run at scale.

Elasticsearch-backed observability at scale — ingest pipelines, APM, and Kibana dashboards tuned for real incident response, not just uptime charts.

Metrics, logs, traces, and RUM wired together for fast root-cause analysis. Custom detectors, SLOs, and cost engineering on high-cardinality accounts.

OneAgent deployments, Smartscape topology, and Davis AI tuning for distributed systems where a slow trace is a business problem.

Grafana, Loki, Tempo, and Mimir engineered as a coherent open-source observability layer — dashboards built for the questions your team asks under pressure.

Prometheus and Thanos at production scale. Recording rules, alertmanager routing, and long-term storage strategies that hold up under real query load.

Grafana Loki as a cost-effective log backend — label design, chunk tuning, and multi-tenant deployments that don’t collapse at volume.

Grafana Tempo for high-throughput tracing, wired into your existing metrics and logs for true correlation across services.

OTel Collector rollouts, instrumentation standards, and vendor migrations that give you back control over your telemetry pipeline.

Wide-event instrumentation and BubbleUp workflows for teams that debug in production rather than staring at dashboards.

Relevance tuning, sharding strategy, and cluster performance at high query volume — from product search to enterprise knowledge bases.

Fully open-source distributed search — cluster sizing, ingest pipelines, and query performance that hold up under production traffic.

Instant, relevance-tuned product and content search — index design, ranking rules, and frontend integration that ship in weeks, not quarters.

High-volume log, event, and search-adjacent analytics at a scale where traditional databases fall over.

Document architecture and performance tuning for applications where search and complex document models sit side by side.

End-to-end SIEM on the Elastic Stack — detection rules, threat hunting workflows, and ingestion at security-team volumes.

Splunk Enterprise Security and Splunk Cloud engineering — SPL tuning, data model acceleration, and migrations off legacy deployments.

Falcon platform integration, telemetry routing, and detection pipelines that connect endpoint signal to your broader SecOps stack.

Cribl Stream and Edge deployments to control, shape, and route security and observability data before it hits your SIEM bill.

Wazuh deployments for teams that need open-source security monitoring alongside a commercial SIEM.

Vector database engineering for semantic retrieval, hybrid search, and RAG pipelines that need to survive production traffic.

Redis Stack and Redis Vector for low-latency semantic search alongside your existing caching and realtime workloads.

Vector search inside Postgres — schema, indexing, and hybrid retrieval strategies for teams that don’t want another data store.

LangChain and LangGraph pipelines integrated cleanly into your existing search, telemetry, and data infrastructure.

Vector and lexical search combined inside OpenSearch — one cluster, one relevance model, one operations story.