Cribl-enabled data pipeline engine for telemetry consolidation and transformation. Unified pipeline architecture to collect, process, and enrich logs, metrics, events, and traces across edge, cloud, and third-party services. Designed for high fidelity, real-time insights with built-in schema validation, dynamic routing, and compliance-ready governance.

High-performance, scalable data pipelines for modern observability demands. Purpose-built pipelines leveraging Cribl, Kafka, Snowflake, and native cloud services to consolidate fragmented telemetry from Kubernetes clusters, cloud-native apps, and edge environments. Optimized for reliability, latency, and cost-efficiency.

Telemetry pipelines built for edge-scale deployments. Custom-engineered for distributed edge infrastructure enables real-time data collection and local preprocessing before intelligent routing to central observability stacks. Reduces bandwidth usage and ensures localized control without sacrificing speed or fidelity.

Cloud-native data ingestion and orchestration for AWS, Azure, and GCP. Seamlessly integrates with cloud telemetry sources (e.g., CloudWatch, Azure Monitor, GCP Logging) to structure, enrich, and route data to analytics platforms.

Smart pipelines for enriched, high-quality telemetry. Enriches data with contextual metadata (e.g., app versioning, trace context, GeoIP, compliance markers) to enable faster anomaly detection, deeper insights, and precision alerting. Lays the groundwork for downstream AI/ML and automation use cases.

Governance-first architecture with real-time responsiveness. Ensures enterprise-grade observability pipelines with real-time filtering, latency-aware processing, and audit-ready controls. Balances operational overhead with agility, delivering precise and actionable telemetry to the right teams at the right time.