DCIP unifies telemetry from every layer of your physical and virtual infrastructure, correlates events with AI, and delivers root cause and remediation in under 25 milliseconds — across any industry, at any scale.
Every organisation running complex infrastructure faces the same six challenges. DCIP was designed from the ground up to eliminate all of them.
A single fault generates hundreds of cascading alarms across servers, network, storage, and applications — simultaneously, from every monitoring tool you have.
Mean time to detect is measured in minutes. Customers feel downtime before operations teams have completed their first log search.
Root cause analysis means switching across five or more tools, comparing timestamps, cross-referencing logs — a slow, high-pressure, error-prone process.
Disk fills. CPU pins. Memory exhausts. Each a preventable, unplanned outage that arrives without warning at the worst possible moment.
The same faults recur because there is no institutional memory. Every incident is diagnosed from scratch, consuming the same time, every time.
No single monitoring tool covers the full stack. Servers, networking, storage, power, containers, and applications each require their own agent and console.
DCIP processes every infrastructure event through a precision sequence of purpose-built layers, each with a strict latency budget — so performance is deterministic at any scale.
Every protocol your infrastructure speaks — SNMP, syslog, gNMI, Redfish, Prometheus, Kubernetes Events, OpenTelemetry, IPFIX, storage REST APIs — is normalised into a unified event model in under 2ms. Built on Rust, Tokio, and io_uring for kernel-bypass-class throughput with zero garbage-collection pauses.
Budget <2ms · Rust + Tokio + io_uringAn in-memory topology graph — updated live from your CMDB and network discovery — maps every configuration item and its dependencies. A probabilistic Bayesian algorithm identifies the root cause, suppresses duplicates, and traces service impact across your full infrastructure graph, in-process with no network hop.
Budget <8ms · petgraph + Bayesian RETE rulesThree models run in parallel: a fast INT8 anomaly gate on CPU filters events in ~0.5ms; a Graph Neural Network analyses multi-node fault topology on GPU; and an LSTM forecaster predicts capacity exhaustion from 128 KPI features. All share CUDA streams and execute within a 10ms combined budget.
Budget <10ms · ONNX INT8 + LibTorch FP16 CUDAA RETE rules engine with 200+ expert rules maps each incident to ranked remediation actions. For novel faults outside the rule library, an on-premises local LLM generates context-aware guidance without any data leaving your environment. Approved actions execute automatically with a mandatory 30-second cancellation window.
Budget <3ms · RETE engine + llama.cpp local LLMA live topology map renders 10,000+ assets at 60fps. Each fault surfaces as a single enriched incident card — root cause, blast radius, ranked remediations, auto-heal status — pushed to the operator in real time over WebSocket. No polling. No page refresh. No tool-switching.
Budget <2ms render · React + D3 + WebSocketA three-node Raft consensus cluster provides >99.99% availability with sub-200ms automatic failover. Every event is appended to an immutable distributed log before processing — guaranteeing zero data loss on node failure. Kafka provides the replay window for recovery and model training.
Async · openraft + Kafka · <200ms failoverOperator resolutions, drift signals, and accumulated event logs feed three timescales of learning — real-time online updates, 30-minute recalibration, and full 12-hour retraining. New models are hot-swapped atomically with zero dropped events. DCIP gets more accurate the longer it runs in your environment.
Async · River + PyTorch sidecar · 0ms hot-swapDCIP's inference pipeline is a tiered hierarchy — speed where you can, depth where you must — delivering accuracy without sacrificing the latency budget.
A lightweight INT8 model screens every event on CPU. Events below the anomaly threshold never reach the GPU, preserving compute budget for signals that matter. Acts as a high-throughput pre-filter for the full deep pipeline.
A Graph Neural Network analyses the faulted component and its neighbours simultaneously in the topology graph — detecting correlated multi-node failures that per-device rules cannot catch. Trained on labelled incident data and continuously refined by the learning loop.
Tracks 128 KPI features per asset — compute, memory, storage, network, power — and projects utilisation trajectories. Fires a capacity alert with a runway estimate 30–120 minutes before any threshold breach. MAPE <8% at a one-hour horizon.
See how DCIP handles a storage failure cascade that would take a skilled operations team 20–40 minutes to diagnose and resolve manually.
A RAID controller on a high-frequency trading storage array develops a failing NVMe disk. Within seconds: I/O latency spikes on 12 virtual machines. Kubernetes pods begin evicting. Application alerts fire across three monitoring tools. The trading desk calls the NOC.
Without DCIP, the on-call engineer must correlate alerts across the storage console, hypervisor dashboard, Kubernetes cluster, and application APM — a manual process taking 20–40 minutes under pressure. Every minute of downtime costs six figures.
With DCIP, detection, root cause identification, impact mapping, and automated remediation complete in under 60 seconds. The operator sees one card and makes one decision.
DCIP's learning loop operates at three timescales — adapting continuously to your environment's unique failure patterns without pausing operations for a single millisecond.
Every confirmed incident resolution updates online models incrementally via the Python sidecar. Detection thresholds and confidence scores adjust in real time. The hot-path never pauses or restarts.
Continuous drift monitoring tracks model performance. When drift is detected — or every 30 minutes — models are recalibrated against recent event logs and hot-swapped atomically with zero dropped events.
A complete retraining cycle runs on 90 days of accumulated event data. New model candidates are validated against a labelled test set before promotion. Accuracy gates prevent any regression from entering production.
DCIP deploys entirely within your own environment — on-premises, air-gapped, or private cloud. No data egress. No vendor lock-in. Deployment prerequisites typically take 2–6 weeks.