Compress Incidents, Discover Root Cause Faster

Alert and Incident Compression ▼

ML-based alert compression compresses high-volume telemetry into meaningful, high-confidence groupings while preserving causal context. This reduces 2-3X more operational noise before ticketing and creates structured signals for prediction and agentic execution.

Incident Specification ▼

Structured incident specification enriches compressed signals with operational context and prioritization, enabling teams to focus on the most impactful issues and support predictive analysis and agentic actions.

Root Cause Analysis ▼

Causal root cause analysis identifies the true origin of incidents — not just correlated symptoms — providing precise insight to accelerate resolution and improve future prediction.

Root Cause Analysis ▼

Causal root cause analysis identifies the true origin of incidents — not just correlated symptoms — providing precise insight to accelerate resolution and improve future prediction.

Root Cause Analysis ▼

Causal root cause analysis identifies the true origin of incidents — not just correlated symptoms — providing precise insight to accelerate resolution and improve future prediction.

Root Cause Analysis ▼

Causal root cause analysis identifies the true origin of incidents — not just correlated symptoms — providing precise insight to accelerate resolution and improve future prediction.

Droma’s Cognitive AI Architecture

Droma’s Cognitive AI Architecture is modeled after key brain functions, such as the neocortex, responsible for higher cognitive tasks like causal inference and decision-making. Droma performs object detection by synthesizing and associating data with underlying causes, similar to how the brain processes sensory inputs. This design enables Droma to: