Generates
Dynamic Twin
A continuously updated model of your system as it actually runs — built from runtime behavior, not documentation. Reflects every component, dependency, and interaction.
System Map
Your system, as it actually runs — not as it was documented.
Most architecture documentation describes what was planned. By the time you read it, it no longer reflects what runs. PULSAR builds a continuously updated model of your real system — from specifications, logs, traces and architecture data — and keeps it current automatically.
How PULSAR works
From raw runtime data to system intelligence.
PULSAR takes only what already exists in your system — log files and trace data — and transforms them into a living, causal understanding of your entire architecture. No documentation required. No source code access.
What goes in
Log files
System events, errors, state changes
Any formatTrace data
Distributed spans, timing, call chains
Any formatNo specifications or docs
No source code access required
PULSAR engine
Processing
01
Normalize & parse
Ingest any format. Clean and correlate events across all sources.
02
Build event graph
Map every component interaction. Reconstruct real communication paths.
03
Learn causal chains
AI learns how events propagate. Reveals the logic behind system-wide behavior.
04
Build Dynamic Twin
Continuously updated model of the implemented system — not the documented one.
What you get
Reveals
Causal dependency graph
Every component relationship and propagation path across ECUs, domains, and supplier tiers. See how events travel through the system before they become failures.
Enables
Root cause in hours
Cross-domain failure analysis that previously took 1–4 weeks of manual log inspection. PULSAR traces symptoms back to their origin automatically.
Detects
Anomalies before failure
Rare system states and critical corner cases detected before they become SOP delays or warranty claims. Continuously monitored against the learned baseline.
Why this approach is different
Works immediately
No data labeling. No training phase. No knowledge modeling. PULSAR learns your system structure unsupervised from day one. Just connect your existing data pipeline.
Cross-domain by design
Existing tools are siloed by domain — ADAS, IVI, Chassis each have their own. PULSAR builds a single unified graph across all domains and supplier tiers simultaneously.
IP-safe by architecture
No source code access. No architecture documentation required. PULSAR reconstructs system understanding purely from observable runtime behavior — respecting every IP boundary.
What makes the preprocessing the moat
Challenge
Naive LLM approach
PULSAR
3 GB of raw logs
Exceeds any context window
Pre-processed into structured graph first
Unstructured noise
Hallucinated analysis on raw input
Anomalies extracted before AI reasoning
Cross-domain causality
No system context — isolated answers
Full causal graph available for every query
Repeatable analysis
Different answer every run
Deterministic graph + targeted AI reasoning
Cost at scale
Token cost grows with data volume
Preprocessing reduces input to what matters
How it works
From runtime data to causal system intelligence.
Normalize the runtime data
PULSAR ingests raw traces, logs, timestamps, channels, and system metrics from test runs and turns them into a clean, canonical event model. This creates a reliable data foundation for understanding what actually happened in the system — without relying on architecture documentation or source code.
Reconstruct system behavior
From the normalized runtime data, PULSAR builds a behavior graph: which components communicated, which events followed each other, where dependencies appeared, and how CPU, memory, timing, and traffic reacted during the run.
Learn what normal looks like
Across many good test runs, PULSAR learns recurring sequences, timing patterns, communication paths, and system baselines. Once this behavioral model exists, a new test run can be checked immediately for deviations, causal chains, and likely root-cause candidates.
System screenshot placeholder
Insert screenshot from a real system run here (for example: timeline, traces, event graph, and detected anomaly markers).
What you can do with it
What becomes possible with a living system model
Root Cause Analysis
Trace any failure to its origin — automatically, across domains.
Anomaly Detection
Detect behavioral drift before it becomes an incident.
Change Intelligence
See exactly what changed, what it affects, and what risk it introduces.
System Health
Know if your system is ready to ship — with evidence.
Outcomes
We change how teams build and debug complex systems
🧭 Causal clarity
Symptoms → Causal understanding
Instead of chasing logs across tools and teams, PULSAR reconstructs where issues started, how they propagated, and which components were affected.
⏱️ Failure Search Time
Weeks → Minutes
Engineering teams spend less time chasing failures across domains and more time building and shipping software.
🧠 Continuous learning
Thousands of test runs → Continuous system learning
PULSAR learns normal system behavior across historical runs — so future anomalies can be detected earlier, from a single test execution.
Use cases by industry
How teams apply this capability in their sector (more industries coming soon).