AI apps ship monthly with zero observability
average annual spend on 3 separate tools
until churn is noticed after an incident
median incident resolution with Causal
The Platform
Every other platform started from one side — infrastructure or users — and tried to bolt on the rest. We built the unified data model from day one.
Layer 01
Distributed tracing across every microservice. Latency maps, dependency graphs, database query analysis.
Layer 02
Real-time user events, session replay, funnel analysis, churn prediction. Every action tagged with infra context.
Layer 03
Not alerts — actions. When a P0 hits, Causal opens a PR, notifies Slack, pauses A/B tests, calculates revenue impact.
Module 04
Full LLM call logging, hallucination detection, token cost per user per revenue dollar.
Module 05
AI-generated code has predictable vulnerability patterns. Runtime scanning for insecure routes, missing auth.
Module 06
Infrastructure events, user behavior, AI calls, and business outcomes stored as nodes in a single causal graph.
How It Works
Step 01
Five lines. Node, Python, Go, or Java. Drop-in OpenTelemetry replacement.
Step 02
Infra traces, user events, LLM calls woven into the Unified Event Graph in real time.
Step 03
The engine traverses the causal graph — from business impact to root cause — in seconds.
Step 04
PR opened. Slack notified. A/B tests paused. Revenue impact calculated. All automatically.
Pricing
Per event pricing with a free tier that lasts until you have real scale.
Startup
per month
Growth
per month