Stage 01
Deterministic ingest
We normalize OTLP spans, Kafka edge-events, and ledger hooks in under 400ms. Every event gets deterministic IDs and compliance guards.
Product · Technical deep-dive
How predictive latency mapping actually works.
This split-screen walkthrough is written for the skeptics and SREs. Left: the methodology. Right: the live Neo4j Cypher trace that proves the math.
Stage 02
Graph-based causality
Neo4j relationships encode propagation probability, service owners, and cumulative latency. We run Monte Carlo drift to see how far the blast will travel.
Stage 03
Actionable propagation
We surface only the causal paths above your risk tolerance. Each path renders with owners, runbooks, and impact deltas ready for the board.
neo4j@latency-core
Sticky context
Scroll the explanatory column. The Cypher stays pinned so SREs can validate every claim against the raw query.
Subsystem detail
What happens inside each hop.
Split-screen clarity extends to the subsystem view. Each hop is treated as a first-class citizen with deterministic IDs and determinism checks.
Edge validation
Proxy waveform
Istio + Envoy signatures confirm request lineage before graph ingestion.
Causality
Propagation tensor
Neo4j path scoring fuses SLO budgets, owner escalation, and Monte Carlo drift.
Narratives
Executive binder
Outputs board decks with trace anatomy, mitigation, and finance deltas.
Feedback
Runbook router
Feeds Jira/Linear/PagerDuty with zero manual correlation.
The anatomy of a trace
Visualize the propagation mesh.
When the page loads, the data flow animates between microservices. Every line is powered by Framer Motion to prove where the milliseconds go.
Gateway
Graph Core
Board Deck
Walk the full how-it-works stack with our architects.
We'll stream the Neo4j console, trace the mesh, and leave you with an action register.