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Infrastructure AI · one control plane

One control plane for infrastructure AI.

On-prem precision, cloud scale. Your choice, per workload.

AI Management is one console that runs intelligence wherever each workload belongs, on-prem for the sensitive, in the cloud for scale, turning raw telemetry into decisions.

  • Anomaly detection
  • Forecasting
  • Root-cause
  • Deploy anywhere
  • One console
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The problem

Drowning in event noise.

Operations teams face alarm streams from power, cooling, IT, environment, network and safety, each with its own thresholds, vendors and false-positive rates. Six dashboards, no single judgement.

  • Every layer has its own thresholds and its own noise
  • Real problems hide behind a wall of false positives
  • No one view that turns events into a decision
Power
Cooling
IT
Environment
Network
Safety

Six streams, six dashboards

On-prem precision, cloud scale

Run each workload where it belongs.

One management plane governs both deployment modes, deploy, monitor and tune every AI workload on-prem or in the cloud, from a single console.

AI Management

One control plane

deploy anywhere

On-prem

Sovereign & air-gapped

Data stays on-site, sub-second, single-tenant.

On-cloud

Fleet scale & GPU

Cross-site models, managed GPU, region residency.

Run each workload where it belongs · illustrative

prodcim.local · AI plane
ProDCIM AI Management console

What this helps you do: govern every model from one console, and decide per workload whether it runs in your facility or in the cloud. Sample data shown.

One plane, two deployment modes

Choose the model, not a compromise.

On-Prem AI Modules

Run inference locally for sensitive or air-gapped workloads, data never leaves the site.

  • Air-gapped
  • Single-tenant
  • Local

On-Cloud AI System

Offload broad, cross-site analytics to the cloud for scale and heavier models.

  • Fleet scale
  • GPU pools
  • Residency

Single management plane

One console governs both, deploy, monitor and tune every AI workload from one place.

  • One console
  • Per-workload
  • Govern

Intelligence on every signal

Telemetry into decisions, not just charts.

The same models that watch a single rack scale to a whole estate, on-prem or in the cloud.

Anomaly detection

Spots drift and outliers across thousands of parameters before they become incidents.

Forecasting

Predicts capacity, load and failure windows from historical trends.

Root-cause analysis

Correlates signals across layers to point at the likely cause, not just the symptom.

Natural-language queries

Ask in plain language and get a grounded reply, through the AI Companion.

Automated reporting

Generates operational and compliance reports on schedule, no manual assembly.

Continuous learning

Models adapt to your environment's normal, accuracy improves the longer they run.

From signal to action

Raw signal to recommended action.

Every metric runs the same path, and you decide whether each step executes on-prem or in the cloud.

1. Ingest

Live metrics stream in from sensors, gateways and the monitoring layer.

2. Detect

AI models flag anomalies, drift and emerging risk in real time.

3. Explain

Each finding arrives with context and likely root cause, not just a flag.

4. Act

Recommended actions route to the right team, executed on-prem or in the cloud.

Part of the platform

The intelligence layer of ProDCIM.

AI Management governs the models; the rest of the platform feeds and acts on them.

AI Companion
On-Cloud AI System
On-Prem AI Modules
ProDCIM Platform
Data Acquisition
API Integration

FAQ

Frequently asked questions

What is ProDCIM AI Management?
AI Management is a single control plane for infrastructure AI inside ProDCIM. It turns raw telemetry into decisions with anomaly detection, forecasting, and root-cause analysis, and lets you deploy, monitor, and tune every AI workload from one console, whether it runs on-prem or in the cloud.
Can I run some AI workloads on-prem and others in the cloud?
Yes, the choice is per workload, governed from one management plane. Run inference locally for sensitive or air-gapped workloads so data never leaves the site, and offload broad, cross-site analytics to the cloud for fleet scale, managed GPU, and heavier models.
What AI capabilities does the AI Management System include?
Six core capabilities: anomaly detection across thousands of parameters, forecasting of capacity, load, and failure windows, and root-cause analysis that correlates signals across layers. It also includes natural-language queries through the AI Companion, automated operational and compliance reporting, and continuous learning that adapts to your environment's normal.
How does AI Management reduce alarm noise and false positives?
Instead of six separate alarm streams from power, cooling, IT, environment, network, and safety, each with its own thresholds and vendors, AI Management applies one layer of judgement across all of them. Self-learning thresholds cut alert noise, and root-cause analysis points at the likely cause rather than raising every symptom as its own alarm.
What happens after the AI detects a problem?
Every metric follows the same path: ingest, detect, explain, act. Each finding arrives with context and a likely root cause, not just a flag, and recommended actions route to the right team, executed on-prem or in the cloud.
How does AI Management fit with the rest of ProDCIM?
AI Management is the intelligence layer of ProDCIM: it governs the models while the rest of the platform feeds and acts on them. Data Acquisition streams live metrics in, On-Prem AI Modules and the On-Cloud AI System are the two deployment modes it manages, and the AI Companion answers plain-language questions grounded in your live infrastructure data.

Put your telemetry to work

Book a walkthrough and we'll show AI Management governing models on-prem and in the cloud.

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