We design and build detection-aware filter engines that sit between your log sources and your SIEM or observability platform — dropping what adds no detection value, enriching what does, and routing everything to the right tier. Every drop rule is validated against your active detection content before it goes live, so cost falls and coverage doesn't.
Most filtering projects fail the same way: volume drops, and three months later an incident reveals a detection silently starved of its data. Our approach inverts the order — detections first, filters second.
Before any rule is written, we inventory your active detection content and map every rule to the log sources, event types, and fields it depends on.
We design and build the pipeline itself — engine selection, sizing, high availability, and placement (on-prem, cloud, or hybrid).
Volume reduction engineered against the dependency map — never guesswork.
The right data in the right system at the right cost.
After filtering goes live, we re-tune detection content against the new data profile.
Your team owns the engine after we exit.
We are engine-neutral: we recommend and build on the platform that fits your volume, budget, and team — commercial where it earns its license, open source where it doesn't.
Enterprise-grade observability pipeline. We design routes, pipelines, and packs — detection-safe drop rules, field pruning, lookup enrichment, and multi-destination tiered routing to SIEM, data lake, and archive.
AI-powered security data fabric. We architect collection, reduction, and routing policies that keep detection-relevant telemetry in your SIEM while diverting bulk data to low-cost storage.
High-performance Rust-based pipeline by Datadog. We build VRL transforms for filtering, parsing, and enrichment — a zero-license filter engine with exceptional throughput per core.
Lightweight CNCF-graduated processor ideal for edge and Kubernetes. We engineer filters, parsers, and stream processors that reduce volume before it ever leaves the node.
The battle-tested Elastic pipeline. We optimise grok/dissect parsing, conditional routing, and drop filters — and tune JVM and pipeline workers for sustained throughput.
Vendor-neutral CNCF standard for logs, metrics, and traces. We build processor chains (filter, transform, tail sampling) and OTTL rules for unified, future-proof telemetry pipelines.
Mature CNCF log router with a vast plugin ecosystem. We design tag-based routing, buffering, and filter plugins for reliable multi-destination delivery at scale.
Every active detection mapped to the sources and fields it consumes
Topology, sizing, HA, and placement documentation for the chosen engine
Versioned, documented pipeline-as-code with validated drop and routing rules
Measured ingestion reduction per source with projected licensing savings
Evidence that every use case still fires — including rebaselined thresholds
Procedures for safely adding sources, changing rules, and monitoring the pipeline
Book a free 45-minute discovery call. We'll review your top ingestion sources and show you where a detection-aware filter engine would cut cost without touching coverage.