Measured at a Fortune 100 streaming giant, a security unicorn, a global genomics enterprise and more. Explore each proof-of-concept outcome below.
Chiron sits in the existing log path and consolidates repetitive events before they reach Splunk. Lossless consolidation reduces bytes and events while preserving source-event reconstruction and downstream query results. Existing routing, storage, and Splunk infrastructure remain in place.
In a large-scale customer POC, Chiron was deployed in-stream ahead of the existing Splunk path. The evaluation demonstrated a path to materially lower indexed volume while preserving the information teams rely on for investigation. Lossless consolidation can be deployed first, with semantic consolidation, aggregation, and sampling added selectively where appropriate.
Lossless consolidation — Reduce repetitive log volume while preserving source-event reconstruction and downstream query results.
Query continuity — Existing Splunk workflows remain familiar, with lightweight expansion through a macro where required.
Fits the existing pipeline — Deploy Chiron before Splunk without replacing existing routing, storage, or downstream tooling.
Optimize in phases — Start with lossless reduction, then selectively add semantic consolidation, aggregation, or sampling for additional savings.
Alert storms scatter context across dozens of tools, so on-call engineers chase symptoms instead of causes. Chiron replaces that noise with a single causal signal: the affected SLA, initiating layer and downstream blast radius are attached before anyone opens a dashboard. Pilots at a major live-streaming provider and a genomic life-sciences unicorn saw an 80 %+ reduction in storm pages and time-to-context measured in seconds.
Unified paging signal - one alert with blast radius and cause.
Topology-aware context - see upstream and downstream impacts immediately.
Faster first response - engineers act on a diagnosis instead of hunting through dashboards.
Chiron's platform correlates metrics, events, logs and traces in flight, pre-compiles a live dependency graph and performs stateful, zero-shuffle correlation. This rich state powers agentic workflows: AI SRE co-pilots can access the live context map to triage incidents or even remediate automatically. In high-growth cybersecurity environments, triage time dropped from 20-30 minutes to under five, with no changes to the existing stack.
Pre-compiled RCA - causal relationships are computed continuously.
Cross-layer correlation - metrics, logs, events and traces linked across layers.
Agent-ready context - AI agents can query the live dependency graph instead of a slow data lake.
Distributed tracing delivers deep insight into dependencies and performance, but the ingest and storage costs deter many teams from turning it on. Chiron makes tracing practical by correlating every span with metrics, events and logs in flight and aggregating them before storage. By retaining only high-value span summaries and linking them to other telemetry, Chiron surfaces root causes and blast radius from traces without escalating costs. Customers who previously disabled tracing saw materially faster resolution times and a 90–95% reduction in trace processing costs in customer deployment.
In-flight span aggregation - summarize spans and link them to metrics, logs and events before storing to cut ingest volume.
Cross-layer RCA - trace context is combined with other signals to surface the initiating layer and downstream blast radius.
Cost-aware tracing - high-value span summaries replace the raw trace firehose, making always‑on tracing affordable.
Chiron's unified storage, in-memory correlation and real-time alerting drastically reduce ingest, compute and storage costs across all customer environments. By collapsing noisy alerts into a single signal, teams can rationalize what they store and index in downstream tools such as Datadog or Splunk. Customers see significant savings within months while maintaining or improving diagnostics and alert fidelity.
Compact state - less data stored means lower bills.
AI-ready context - the same state powers agents and analytics.
Integrated approach - unified storage and streaming correlation work together to cut total cost.
Tell us which outcome matters most — we'll tailor a proof-of-concept to your stack.