Industrial Equipment Doesn’t Wait for the Cloud. Your AI Shouldn’t Either.

Sphere’s Industrial Edge Anomaly Detection solution deploys trained ML models directly onto industrial edge devices – detecting equipment faults, process deviations, and safety hazards in real time, at the machine, with sub-10ms response. Fully functional during network outages.

<10ms

Detection Response Time

Offline

Operation Capable

72hr

Average Failure Lead Time

60%

Reduction in Unplanned Downtime

Why This Matters Now

Heavy industrial environments – oil refineries, power plants, mining operations – can’t wait for cloud-based AI inference when equipment is showing signs of failure. A 200ms cloud round-trip is the difference between catching a compressor surge and a $5M explosion. These environments also frequently operate in areas with unreliable connectivity, making cloud-dependent AI operationally unacceptable.

1. Cloud-Dependent AI Fails When You Need It Most

Remote facilities, underground operations, and network-isolated industrial environments lose connectivity exactly when operational stress is highest – taking cloud AI offline during critical moments.

2. Latency Is a Safety Issue

For compressor surge detection, transformer protection, and pressure relief monitoring, a 100ms+ cloud round-trip isn’t just slow – it’s potentially catastrophic.

3. Existing SCADA Systems Lack AI Capabilities

Legacy SCADA and DCS systems excel at data collection and control but have no native anomaly detection or predictive capabilities – leaving critical equipment intelligence on the table.

What Sphere Delivers

Sphere deploys trained anomaly detection models on industrial-grade edge computing devices (AWS Greengrass, NVIDIA Jetson, or ruggedized industrial PCs) – positioned physically at equipment or in local control panels. Inference runs locally, at full speed, 24/7 – regardless of network status. Cloud connectivity is used for model updates and aggregated analytics, not for real-time inference.

Equipment-Specific Anomaly Models

Custom-trained models for your specific equipment types – compressors, turbines, pumps, motors, heat exchangers – capturing the unique signatures of impending failure for each asset class.
Multi-Signal Fusion Analysis

Fuse vibration, temperature, pressure, flow, current, and acoustic signals for dramatically higher detection accuracy than single-parameter threshold monitoring.
Real-Time Safety Integration

Anomaly detection outputs can directly trigger safety system responses – alarm activation, speed reduction, automatic shutdown – via hardwired or OPC-UA integration with existing safety systems.
SCADA/DCS Overlay Architecture

Sphere’s edge AI layer sits on top of existing SCADA and DCS infrastructure – adding AI intelligence without replacing existing control systems or requiring process downtime.
Federated Learning Across Fleet

Anomaly patterns discovered on one asset improve models across your entire equipment fleet – enabling the detection of rare failure modes that no single site would see enough examples of alone.
Edge Deployment, Model Updates & Fleet Control

Run anomaly detection continuously at the asset level while managing models centrally across sites and equipment groups. Sphere handles edge rollout, version control, health monitoring, and secure model updates.

Built On Industry-Leading Technology

Sphere’s edge AI platform is built for real-time industrial inference directly at the equipment layer, where latency, uptime, and control-system compatibility matter most. The stack combines GPU-accelerated edge compute, AWS-based model training and fleet aggregation, industrial protocol support, and local time-series storage so anomaly detection can run continuously on site while fitting into existing SCADA and operations environments.

icon AWS IoT Greengrass (edge ML runtime)
icon NVIDIA Jetson Orin (GPU-accelerated inference)
icon AWS SageMaker (model training)
icon OPC-UA / Modbus / DNP3 (SCADA integration)
icon InfluxDB (local time-series storage)
icon AWS IoT Core (cloud aggregation)

Who This Is For

INDUSTRY

VERTICAL APPLICATION

Oil & Gas

Compressor surge detection, pump cavitation monitoring, separator level anomaly detection – at remote facilities with intermittent connectivity.

Power Generation

Turbine vibration monitoring, transformer partial discharge detection, and cooling system anomaly detection at power plants.

Mining

Conveyor belt anomaly detection, crusher monitoring, and ventilation system fault detection in underground environments.

Chemical Processing

Reactor temperature and pressure anomaly detection, agitator fault monitoring, and heat exchanger fouling detection.

Water Treatment

Pump station anomaly detection, filter performance monitoring, and aeration system fault detection.

Schedule a Free Industrial AI Assessment

Sphere’s industrial AI engineers will assess your facility’s top equipment risk areas, evaluate your existing sensor and SCADA infrastructure, and propose an edge AI deployment plan with projected ROI.

icon No sales pressure icon Senior engineer call icon Custom ROI estimate

How It Works

Equipment Audit

Sphere engineers review target equipment, existing sensor infrastructure, and SCADA/DCS architecture to define integration points and sensor requirements.

Sensor Deployment

Install or leverage existing vibration, temperature, pressure, and acoustic sensors on target equipment. Connect to edge gateway.

Model Training

Collect 2–4 weeks of normal operating data. Sphere trains anomaly detection models specific to each equipment type.

Edge Deployment

Deploy trained models to industrial edge devices. Configure SCADA integration and alarm outputs.

ROI & Bussines Impact

Industrial edge AI deployments achieve average unplanned downtime reductions of 60%, saving $3M–$8M annually for large industrial facilities. Safety incident risk reduction from earlier detection translates to $500K–$2M in annual risk cost reduction.

Average payback period: 4–7 months for oil & gas and heavy industrial applications.

Let’s Connect

Trusted by

Flexible, fast, and focused — Sphere solves your tech and business challenges as you scale.

Luke Suneja

Client Partner

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Hear From Our Clients

Sphere Partners
Selah Ben-Haim VP of Engineering at Prominence Advisors

Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).

Sphere Partners
Ben Crawford Senior Product Manager at Enova Financial

I would expect to be delighted. It’s been a really positive experience, working with Sphere, and I would expect you to have the same.

Sphere Partners
Mark Friedgan CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. They bring innovative and well-considered solutions, consistently surpassing my expectations.

Sphere Partners
René Pfitzner Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and work with us.

Sphere Partners
Bruce Burdick Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. Their combination of offshore talent, pricing, and shift offsetting is hard to beat. They provide crucial augmentation to our in-house team. We simply couldn't achieve our production ambitions without their service.

Sphere Partners
Jemal Swoboda CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise to complete their tasks successfully, with the team easily scaled in either direction. The deliverables are always high-quality.

Sphere Partners
Arthur Tretyak Founder and CEO at IntegraCredit

With Sphere, we were able to migrate in half the time it would take to train an additional FTE…

Sphere Partners
Lee Ebreo VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system. We augmented our development team capabilities using Sphere’s developer, who works very well with our Dev Lead in Chicago. Sphere’s developer was an expert in the new system, and continues to be an expert as we evolve it.

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Sphere in Numbers

We understand that actions speak louder than words and numbers
but here are some key facts about us.

20

Years of Experience

230

Delivered Projects

200+

Senior Specialists

94%

Satisfaction Rate

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Frequently asked question

Edge AI for industrial equipment is the use of machine learning models running locally on industrial hardware near the asset, not in a remote cloud environment. Edge AI for industrial equipment helps plants detect abnormal behavior in pumps, motors, turbines, compressors, and heat exchangers in real time. Sphere builds edge AI systems that run directly at the equipment layer so inference continues even when connectivity is unstable.

Industrial anomaly detection at the edge works by collecting live equipment signals such as vibration, temperature, pressure, current, flow, and acoustics, then running trained models locally on an edge device to identify patterns linked to failure or process drift. Sphere’s solution combines local inference with cloud-based retraining and fleet analytics, so detection stays fast on site while models keep improving over time.

Edge AI is a better fit for predictive maintenance when operations need low latency, continuous uptime, and local action without depending on a cloud round trip. Cloud AI is still useful for model training, central analytics, and version control, while edge AI handles the real-time decision layer. Sphere uses that hybrid architecture so plants get both local speed and centralized intelligence.

edge AI can integrate with SCADA and DCS systems through industrial protocols such as OPC-UA, Modbus, and DNP3. That makes it possible to add AI-based anomaly detection on top of existing controls without replacing core infrastructure. Sphere’s approach is built for overlay deployment, which helps industrial teams add intelligence without tearing out the systems already running the process.

The best signals for industrial anomaly detection usually include vibration, temperature, pressure, flow, current, and acoustic data, especially when several signals are analyzed together. Multi-signal industrial anomaly detection is typically more accurate than threshold monitoring on one parameter because the model sees a fuller picture of equipment behavior.

Yes, edge AI can trigger safety responses such as alarms, speed reduction, or controlled shutdown when the architecture is designed to connect model outputs into the plant’s safety and control logic. Real-time safety integration is especially valuable for high-risk assets where failure develops quickly. Sphere supports these integration paths so anomaly detection can become operational action, not just another dashboard alert.

Edge AI in industrial monitoring often runs on NVIDIA Jetson devices, ruggedized industrial PCs, and AWS IoT Greengrass-compatible edge hardware. The best hardware depends on the inference workload, environment, sensor mix, and latency requirements. Sphere helps clients choose the right deployment pattern so the edge AI stack fits the asset, the facility, and the control environment.

Federated learning for industrial assets is a way to improve anomaly detection models across multiple sites or asset groups by sharing learning from distributed equipment behavior without relying only on one machine’s history. Federated learning is useful for rare failure modes because a single site may never generate enough examples alone. Sphere uses fleet-level learning strategies to help manufacturers and operators improve detection quality across the broader asset base.

Yes, edge AI can continue running if the network goes down because the inference happens locally on the edge device near the equipment. That local architecture is one of the main reasons companies adopt edge AI for industrial monitoring. Sphere designs edge deployments so plants can keep real-time anomaly detection active even when cloud connectivity is interrupted.

Buyers should look for equipment-specific models, multi-signal analysis, SCADA and DCS integration, local runtime stability, secure model updates, and a clear path for fleet-wide scaling. Sphere’s strength is in turning edge AI into a real industrial operating layer by combining model training, deployment, protocol integration, and long-term fleet management in one solution.

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