Cloud AI, at fleet scale.
See the pattern, not just the site.
Cloud-based AI for infrastructure analytics and predictive monitoring across your whole fleet, with the security architecture your team expects, and no data-science team required.
- Cross-site
- Managed GPU
- Region residency
- Encrypted
- Pay-as-you-grow
Illustrative
The problem
Six tools, six dashboards, six gut-feels.
Each monitoring tool gives you a fragment, and each vendor has its own idea of normal. Nobody sees the fleet as one, so a problem that is obvious across sites stays invisible on any single screen.
- A fragment per tool, never the whole fleet
- No comparable view across sites and vendors
- Patterns that only show up at fleet scale go unseen
Power
vendor A
IT
vendor B
BMS
vendor C
Network
vendor D
Capacity
vendor E
Tickets
vendor F
Each tool, a fragment
Fleet scale
See the pattern across the fleet.
A drift that looks fine on one site stands out against the fleet. The cloud model compares every site to its peers and flags the outlier.
A cross-site pattern a single-site view would miss · illustrative
What this helps you do: catch the one site drifting away from the rest, weeks before a single-site threshold would ever trip. Sample data shown.
What cloud AI is good at
Three things, done at fleet scale.
See the pattern, not just the site
Trends and anomalies surface across your full fleet, not one dashboard at a time.
- Fleet view
- Cross-site
- Benchmarks
Catch the failure before the trip
Trend gearbox vibration, battery impedance and PDU temperature against their own history.
- Predictive
- Trends
- Early warning
Quiet false-positive, loud true-positive
Models learn what normal looks like per circuit, per rack, per site, so alarms mean something.
- Per-asset
- Low noise
- Trusted
What to expect from a cloud AI service
Cloud AI without your data in a blender.
The architectural choices that let a security team sign off, not just a faster model.
GPU-backed inference
Heavy model workloads run on managed GPU pools, your console sees results, not infrastructure.
Continuous model updates
Models retrain against fresh fleet data on a regular cadence, no manual retraining jobs.
Cross-site insights
Same model, all your sites, so figures like PUE and battery health are comparable.
Encrypted in flight and at rest
TLS 1.3 to the API, AES-256 at rest, KMS-managed keys.
Region-aware residency
Pick the region your data lives in, North America, EU, or your private cloud.
Pay-as-you-grow scaling
Capacity follows your fleet, add sites and AI throughput scales with you.
No data-science team required
Raw telemetry to a useful alarm, automatically.
The pipeline runs the moment your sites start streaming. No labelling, no manual training, no notebooks to babysit.
1. Ingest
Telemetry from every site lands in the cloud data plane, tenant-isolated.
2. Train
Models build a baseline per asset class and per site, what normal looks like.
3. Predict
Real-time scoring runs continuously, what looks unusual now, what is trending wrong.
4. Alert
Findings turn into alarms in your existing ProDCIM workflow, with context attached.
On-Cloud is one deployment mode of AI Management. Need sovereignty or an air gap?
See On-Prem AI ModulesPart of the platform
Cloud intelligence, one model away.
See your whole fleet think
Book a walkthrough and we'll run cloud AI across a sample of your sites.