WHY TRAVERSAL

Production is too complex to explain, and too critical to fail

Trusted by the World’s Leading Enterprises

Pre AI
AI
Complexity
Human investigation
Humans can not keep up
More code
More telemetry
More alerts
More incidents
The Problem

Production complexity has outgrown human investigation

Systems are growing in complexity as change accelerates, leading to more outages and longer downtime.

AI is making it worse

More systems, more noise, and no clear explanation when things break.

Traditional observability can't keep up

When incidents hit, teams rely on escalation, guesswork, and hours or days of investigation.

THE PROBLEM

Production complexity has outgrown human investigation

Systems are growing in complexity as change accelerates, leading to more outages and longer downtime

Graph showing an upward curved line labeled with 'Complexity' at the lower left, 'Human Investigation' near the middle right, and 'Pre AI' with a left arrow at the top center.

AI is exponentially increasing production complexity

More systems, more noise, and no clear explanation when things break

Graph showing exponential growth of complexity over time labeled with 'AI' on rising curve and 'Humans can not keep up' below, highlighting increase in code, telemetry, alerts, and incidents.

Traditional observability can’t keep up

Observability shows causation
It can’t explain correlation

Abstract digital network visualization with interconnected white icons representing data and alerts on a dark green gradient background.
$400B

lost annually to downtime across the Global 2000

41%

of executives say customers detect outages first

50%

of developers lose 10+ hours per week to incident investigation

TRAVERSAL'S BELIEF

This is a causality problem.
Not an observability problem.

Until you can map cause and effect across production, you can’t explain failures fast enough to reduce downtime

WHY TRAVERSAL

Traversal was built by AI researchers to solve casual reasoning at scale

Traversal was built by AI researchers to solve casual reasoning at scale

Can it find root cause far from the initial symptom in minutes?
Does it get smarter itself—or require a team of FDEs?
Does it model causality—or just correlate signals?
Can it reason at scale—without blowing up costs?
Does it see all your production data—without gaps?
Agentic Enterprise Capabilities
Alert intelligenceRoot cause analysisAutonomous remediationCode resilienceAlert intelligenceRoot cause analysisAutonomous remediationCode resilience
Causal Search
Engine™
Knowledge Bank™
Production
World Model™
AI‑Native Compressor™
Agentless Data Capture™
MELT telemetrySource codeDeploysRunbooksHistorical incidentsJiraLogsMetricsTracesMELT telemetrySource codeDeploysRunbooksHistorical incidentsJiraLogsMetricsTraces

Causal Search Engine™
Identify root cause across 10+ hops, from apps to services to infrastructure to networking.

Knowledge Bank™
Learns from your runbooks, docs, and how your team investigates—no manual tuning required.

Production World Model™ 
Live map of millions of entities—apps, services, infra—connected causally.

AI-Native Compressor™
1,000 to 1 data compression with zero signal loss.

Agentless Data Capture™
Read-only by design: API-based capture with push and pull—no sidecars required.

Agentless Data Capture™
Read-only by design: API-based capture with push and pull—no sidecars required.

Production World Model™ 
Live map of millions of entities—apps, services, infra—connected causally.

AI-Native Compressor™
1,000 to 1 data compression with zero signal loss.

Knowledge Bank™
Learns from your runbooks, docs, and how your team investigates—no manual tuning required.

Causal Search Engine™
Identify root cause across 10+ hops, from apps to services to infrastructure to networking.

Ready to put AI to work?