Infrastructure doesn’t fail in isolation.
01
Cross-Layer Topology
Reconstruct the operational relationships that connect every critical layer.
02
Causal Incident Intelligence
Follow multi-signal evidence through the chain of a cascading failure.
03
Failure & Impact Prediction
Prioritize the potential impact of an emerging condition before it becomes a larger incident.
04
Remediation Intelligence
Evaluate response paths against policy, evidence, and operator oversight.
Research
Grounded in reliability research.
Raincurve’s infrastructure intelligence is grounded in systems, reliability, and formal methods research.
A staged reasoning pipeline that converts fragmented operational telemetry into ranked, explainable root-cause hypotheses in near real time.
Raincurve Research
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A topology-aware Hawkes model found the root-cause device for 75% of simulated incidents vs 42% for tuned rule-based correlation, with no labels. The gap widens with noise.
Raghav Balasubramaniam
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The usual runbook check approved 98% of unsafe maintenance actions. Exact graph methods made zero errors, and the fastest checks a 14,680-device network in 0.19 s.
Raghav Balasubramaniam
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A compression layer that preserves cross-layer diagnostic context while reducing raw MELT telemetry volume by roughly 6,000×.
Raincurve Research
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Optimal-execution pacing from trading, adapted to network rollouts. Adding a hard churn limit cut expected damage 43% vs plain Almgren-Chriss; canaries still win the worst case when the fix might be bad.
Raghav Balasubramaniam
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A microVM and copy-on-write snapshotting model for safely executing reasoning and remediation workloads inside customer-controlled infrastructure.
Raincurve Research
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Reliability teams build on Raincurve
How operators use cross-layer intelligence to understand impact and reduce time to resolution.
What’s new at Raincurve
All researchTurning alarm floods into incidents with Hawkes processes
A topology-aware Hawkes model found the root-cause device for 75% of simulated incidents vs 42% for tuned rule-based correlation, with no labels.
How fast should a network fix roll out? Almgren-Chriss for operations
Optimal-execution pacing from trading, adapted to network rollouts. A hard churn limit cuts expected damage 43%.
Proving a network action is safe before it runs
The usual runbook check approved 98% of unsafe actions. Exact graph methods made zero errors.
Curve-1: a reasoning architecture for cross-layer infrastructure intelligence
A staged reasoning pipeline that converts fragmented operational telemetry into ranked, explainable root-cause hypotheses.