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Root Cause

Find the cause.
Not the symptom.

PULSAR traces anomalies back through causal chains to the exact origin — across all domains, all suppliers, in less than a day.

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<1 day Root cause localization
€70k Saved per incident (typical)
95% Reduction in expert hours

Workflow

The same incident. Two completely different outcomes.

Without PULSAR Manual investigation
Day 0
Something broke — nobody knows what

Symptom in the logs. Origin unknown. Development stops until someone owns the chase.

Days 1–2
Tool after tool, no single view

War room spins up. Experts dig through siloed dashboards and stale docs. Cost clock starts.

€15k+ / day typical
Week 1
“Not us” — blame game

Domains point at each other. Supplier email chains grow. Progress is meetings, not answers.

Week 2
Management in — release at risk

Escalations, SOP pressure, and still no grounded causal path across the stack.

Weeks 3–4 (if lucky)
Root cause maybe found — knowledge still in heads

A change from weeks ago surfaces. Learnings rarely stick in the system for the next incident.

up to €150,000
With PULSAR Causal twin in the loop
Hour 0
Signal in — PULSAR already tracing

Anomaly hits the Dynamic Twin. Automated causal traversal starts across domains.

Hour 1
One view — full picture

Dependency path and propagation order materialize. No manual log stitching.

Automated analysis
Hour 2
Origin confirmed — with evidence

Root candidate ranked by confidence. Cross-domain hops explained, not guessed.

Hour 3
Right owner — one targeted ticket

Supplier or internal team gets packaged evidence. No scatter-shot coordination.

One ticket · clear chain
Day 1
Fixed — development keeps moving

Twin retains the pattern. Next sprint impact is mapped, not hoped for.

Fraction of classic cost

How it works

From anomaly to root cause in three precise steps

01

Ingest & normalize system data

PULSAR ingests logs, traces, and architecture specifications from your existing tools — no rip-and-replace. It normalizes heterogeneous data into a canonical event model.

Connects to logs, traces, CAN bus, ETH, diagnostics
Canonical event schema across suppliers and domains
Enriches events with timing, channel, and run context
ECU-GW → ADAS: msg_0x4A2 [OK]
SW-Stack-B → MW: cfg_sync [WARN] t=+14ms
Normalized → graph event #7481
Domain: C · Confidence: 0.91
Ingesting 14.2M events · 3 domains
02

Build the Dynamic Twin

PULSAR reconstructs the real system graph — not the documented one. It learns causal chains from healthy runs, building a continuously updated behavioral baseline.

Cross-domain dependency graph (ECUs, functions, services)
Designed vs. observed architecture deviation detection
Baseline learning from good test runs
Health index per component and supplier tier
03

Causal trace to root origin

When an anomaly appears, PULSAR does not just detect it — it traverses the causal dependency graph to trace the signal back to its origin node across all domains and supplier tiers.

AI-driven causal traversal, not correlation
Cross-domain path traced in seconds
Evidence-backed root cause ranking with confidence scores
Explainable engineering summary generated automatically
Trace initiated: anomaly #A-2241
→ Symptom: ADAS timeout [D=A]
→ Propagated via: Middleware [D=B]
→ Originated at: cfg_v2.1.3 [D=C]
ROOT CAUSE · Confidence: 0.94
Config version mismatch · Domain C · ECU-Tier3

Measured impact

The numbers are the argument

Based on conservative calculations across real SDV platform programs — single incidents and entire program cycles.

1–4 weeks
<1 day
Root cause localization

From weeks of war rooms to targeted investigation with a clear causal path.

10–50 experts
Automated
Analysis execution

Senior engineers focus on solving — not searching. One targeted ticket replaces email chains.

up to €150k
€70k
Saved per incident

Conservative estimate: 10 FTE × 2 weeks × €500/day. Each incident resolved faster pays for the platform.

Lost after ticket
Retained
System knowledge

Every resolved issue is captured in the Digital Twin — not lost in individual log analyses.

10
Senior engineers
14
Days average
€500
Per FTE per day
€70,000
Per single incident — avoidable

Large platform launches routinely see 1,000+ such incidents per program cycle · cumulative savings: millions

Use cases by industry

How teams apply this capability in their sector (more industries coming soon).

Automotive