One reasoning layer for the whole infrastructure stack
Raincurve connects telemetry and dependencies across network, compute, cloud, application and AI inference infrastructure — then explains failures, predicts impact and verifies every action before it runs.
Cross-Layer Infrastructure
Live dependency graph across the stack.
Learn more →Causal Incident Intelligence
Alarm floods into incidents with a named origin.
Learn more →Failure & Impact Prediction
Blast radius before customers feel it.
Learn more →Inference Reliability
Trace serving latency to GPUs and fabric.
Learn more →Context Compression
Compact Contracts for 24/7 reasoning.
Learn more →Verified Remediation
Prove an action is safe, then pace it.
Learn more →Failures cross layers. Tools don't.
A degraded optic becomes a routing flap, which becomes a storage timeout, which becomes an inference latency alert in a different team's dashboard. Each tool sees its own slice; the incident lives in the joins between them.
Raincurve sits above the tools you already run. It keeps a live model of how your infrastructure depends on itself, reasons over the signals flowing through it, and hands operators a ranked, explainable answer — with the evidence attached.
Nothing is replaced. Raincurve reads from your existing monitoring, inventory and change systems and writes back into your ticketing and approval workflows.
How Curve-1 reasons
A staged pipeline, not a single black-box model. Every stage is inspectable, and the pre-filter keeps continuous operation economical.
Pre-filter scoring
An IQL-based scorer assigns a risk score to incoming telemetry in about 2 ms, so the expensive reasoning runs only where it matters.
Trajectory encoding
A contrastively trained temporal encoder maps event sequences from every layer into one shared space, where related symptoms land together.
Hypothesis generation
A latent diffusion model proposes calibrated root-cause hypotheses from incomplete evidence instead of waiting for a perfect picture.
Incident clustering
Topology-aware Hawkes processes group symptoms across network, compute, cloud and application into one incident with a named origin.
Measured, not asserted
Results from production and from published Raincurve Research notes. Each links to its method and limits.
Deploys where your infrastructure lives
Private, hybrid or cloud. Open-weight models run inside your boundary, and every execution is sandboxed.
Hardware-agnostic
Multi-vendor by design. Raincurve reasons over normalized signals, so mixed estates and acquisitions are the normal case, not the exception.
Runs in your environment
Open-weight deployments inside customer-owned infrastructure keep operational data and execution within your boundary.
Isolated execution
Each task runs in its own Firecracker microVM with copy-on-write snapshots, so a bad run cannot reach the host or other work.
Operator in the loop
Automation is introduced progressively. Recommendations, approvals and execution are separate, auditable steps.
Works with existing tools
Reads from monitoring, inventory and change systems; writes to ticketing, chat and approval workflows.
Audit trail by default
Every hypothesis, verification result and action is recorded with the evidence behind it.
The research behind the platform
Classical methods, carefully applied, with published results.
Curve-1: a reasoning architecture
The four-stage pipeline behind cross-layer root-cause ranking.
Read paper →ResearchTurning alarm floods into incidents with Hawkes processes
75% of root-cause devices found vs 42% for tuned rules, with no labels.
Read paper →ResearchProving a network action is safe before it runs
Exact graph checks: zero unsafe approvals, 14,680 devices in 0.19 s.
Read paper →