Semantic causality for engineering systems
Know what changed.See what it affects.Explain why it failed.
DecisionWeave builds a lightweight semantic graph over your source systems to produce causality chains—incident → service → dependency → deploy → PR → ticket— and makes them queryable by humans and agents.
Not a datastore • No log ingestion • No “new source of truth”
Persist only relationships, provenance, and causal edges.
DecisionWeave semantic causality layer
Source systems remain the system of record. DecisionWeave stores only relationships and causal edges.
Not a datastore
We don’t ingest metrics/logs. We fetch evidence on demand and persist only relationships + minimal provenance.
Causality-first
“What changed?” → “What did it affect?” → “Where is the blast radius?” in a single chain.
Agent-ready
The graph becomes an operational state model that agents can query and reason over safely.
What DecisionWeave unlocks
Observability tools are vertically strong but horizontally blind. DecisionWeave stitches reality together across systems.
Causality chains
Trace “why this alert” to the originating change and blast radius, with explicit edges and provenance.
Semantic dependency graph
Model services, teams, deployments, business flows, and ownership—then let agents reason over the operational state.
Automated triage
A monitoring agent detects failure patterns and generates candidate root-cause chains with confidence scoring.
Ready to see causality, not just symptoms?
Start with the demo, or talk to us about deploying DecisionWeave for your incident workflows.