Applications · See → Think → Act

Built for Real-World
Environments

Visual intelligence for the industries where conditions are never ideal and reliability is never optional.

The Common Thread

Different Industries.
Same Underlying Problem.

Every camera network runs in conditions no single product was designed to handle. The specifics vary — fog at a port, dust in a plant, turbidity beneath an offshore platform, motion blur on a surgical robot, darkness in a theatre auditorium — but the pattern is the same. Degraded input produces degraded intelligence — whether that intelligence is an AI model, an operator watching live, or a recording reviewed after the fact.

That’s why one architecture handles all of them. ProHawk AI restores the input. Clean signal flows into whatever analytics are reading it — an existing platform, our Vaidio intelligence layer, or both together. NVIDIA GPU compute carries the workload, delivered on HPE or Dell platforms. Partner One engages at whatever layer the customer needs — adding what’s missing, replacing what’s legacy, or building from scratch. The industries look different. The architecture underneath doesn’t.

Read the full argument: Why AI Vision Fails →

See

Energy & Utilities

The Challenge

Remote facilities, extreme weather, dust, smoke, low light, and vast perimeters. Utility infrastructure is inherently difficult to monitor — hundreds of sites in exposed environments.

What Changes

Extended detection range and restored clarity across existing camera networks — no forklift upgrades, no trenching, no site visits.

$7M
Camera upgrades avoided across 800 cameras
Major Hawaii utility case study
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Think

Manufacturing

The Challenge

Dust, steam, smoke, vibration, low lighting, and complex operational environments. Quality control, safety monitoring, and process analytics all depend on reliable detection through degraded scenes.

What Changes

PPE detection, fire detection that ignores dust and steam, defect inspection at production speed, and yard logistics analytics — all working on existing camera infrastructure.

$1M/yr
Operational savings across 12+ Vaidio use cases
Rand Whitney case study
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See

Ports & Maritime

The Challenge

Fog, sea spray, rain, nighttime operations, and glare off water. Ports operate in some of the most consistently degraded visibility environments on earth — and perimeter, vessel traffic, and cargo yard systems all depend on cameras that can see through it.

What Changes

Restored clarity across waterfront cameras enables reliable vessel detection, cargo monitoring, perimeter security, and worker safety — even in fog that would blind standard analytics.

Fog restoration Nighttime operations Existing cameras
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Think

Security & Public Safety

The Challenge

Perimeter breaches happen at night. Incidents occur in weather. The cameras that matter most are exactly the ones most likely degraded — and false alarms from legacy analytics drown out the real threats.

What Changes

Vaidio intelligence filters the noise: context-aware detection, cross-camera tracking, and real-time alerts that trigger action instead of fatigue.

2,000→10
Monthly false alarms, after replacing a competing AI platform with Vaidio
International media company
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See

Transportation

The Challenge

Rain, glare, fog, nighttime conditions, and tunnel darkness. Traffic monitoring, incident detection, and rail corridor surveillance all struggle when conditions turn against them.

What Changes

Clear feeds enable reliable traffic flow monitoring, faster incident detection, and consistent surveillance across highways, tunnels, bridges, and rail networks.

Weather-independent Tunnel & glare recovery Existing cameras
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Think

Smart Cities

The Challenge

Nighttime, weather variability, glare, and the sheer scale of citywide camera networks. Public safety, traffic management, and infrastructure monitoring require consistent performance across thousands of cameras in highly variable conditions.

What Changes

Consistent analytics across the whole network. Vision Language Models let operators create custom detectors for flooding, crowd formation, or suspicious behavior — in plain English, no engineering required.

Citywide scale VLM custom detection Multi-VMS
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See

Healthcare

The Challenge

Low contrast in X-rays, MRIs, and CT scans. Glare and obstructions in endoscopic and robotic surgery. Motion blur in fluoroscopy. Compressed telemedicine video. Image quality directly shapes diagnostic and procedural outcomes.

What Changes

Restoration recovers signal detail across imaging modalities — giving diagnostic AI and physicians cleaner input to work from, without changing existing equipment or workflows.

Diagnostic imaging Surgical video Physics-based
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See

Robotics & Automation

The Challenge

Low-light warehouses, motion blur from fast-moving arms and drones, glare from shiny surfaces, fog and rain for outdoor autonomous navigation. Vision-dependent systems fail in exactly the conditions they’re deployed to handle.

What Changes

Restoration keeps AMRs, robotic arms, drones, and autonomous vehicles operating through conditions that would otherwise shut them down — delivering cleaner input to the perception stack without retraining the models.

Low-light navigation Motion-blur recovery No retraining
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See + Think

Theatres & Live Venues

The Challenge

Historic theatres, landmark-designated interiors, darkened auditoriums during performances, crowd surges at intermission, and stage-door security all in one property. Cameras can’t be freely replaced — preservation constraints make rip-and-replace impractical or prohibited.

What Changes

Restored clarity in low-light auditoriums. Crowd and queue analytics at entrances. Sub-3-second forensic search across every house camera. All without modifying landmark-protected buildings.

Landmark-compatible Low-light restoration Existing cameras
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See

Subsea & Offshore

The Challenge

Turbidity scatters light the same way fog does on land. ROV footage, pipeline inspections, platform monitoring, and dive team video all lose clarity in suspended sediment — exactly when inspectors need it most.

What Changes

The same physics-based restoration that clears fog, clears turbidity. Inspection footage stays readable through conditions that would blind conventional cameras.

Turbidity restoration ROV compatible Physics-based
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