Across industries, artificial intelligence (AI) pairs with cameras, sensors and other devices to extract meaningful information from images. To use this information in a way that leads to significant business improvements, these sophisticated devices and AI models must deliver in real time.
However, most enterprise organizations lack the in-house infrastructure needed to unlock the speed and scale they need from AI-powered image processing. And while major cloud providers can provide that muscle, customers with highly sensitive or regulated data may be concerned about control, privacy or compliance.
The solution is hybrid cloud.
A hybrid infrastructure for regulated industries includes the following:
- Strong on-premises servers;
- A private cloud for scale and speed;
- And a secure network to bypass the public internet.
- Internet of things (IoT) technology
A hybrid approach allows sensitive device data to be analyzed on premises — where it’s secure and meets data residency and sovereignty requirements — while using a private, non-multitenant cloud’s computational power to launch advanced machine learning models. Further, large volumes of visual data — from MRI images to insurance claims — can be processed locally and routed to private cloud GPUs for analytics and AI Inference
“An AI model can scan millions or billions of images to identify anomalies faster and often better than the human eye,” said Kevin Cochrane, chief marketing officer of Vultr, a cloud services provider. “For AI to truly reach its potential, you need to rethink your critical infrastructure, and that infrastructure has to be safe and secure.”
Vultr partners with Console Connect for secure 5G IoT data transfer and Gravio for no-code local data processing. Together, these tools enable companies to verify insurance claims, find defects in product lines and detect possible cancer in a patient’s MRI. With data-protecting infrastructure in place, organizations can leverage the power of AI at scale to dramatically improve efficiency, cut costs and improve lives.
3 ways a hybrid cloud environment supports AI-enabled image processing
Virtually every industry has been impacted by AI. Within them, organizations are at various stages of integrating AI-based tools into their workflows. According to a Goldman Sachs report, about 6% of U.S. companies overall, and 10% of companies with over 250 employees, use AI to produce goods and services. A report from the U.S. Chamber of Commerce, meanwhile, found that about 40% of small businesses surveyed use generative AI, while 77% said they have plans to adopt emerging technologies such as AI and the metaverse.
One of AI’s strengths lies in its ability to analyze massive amounts of data: it can sift through volumes of spreadsheets, text or images with surprising speed and accuracy. That power has the potential to help businesses of all stripes improve productivity substantially. “Academic studies imply a 23% average uplift to productivity, while company anecdotes imply slightly larger gains of around 30%,” the Goldman Sachs report stated.
Here are three examples of how organizations can and have used AI-powered image processing in a hybrid cloud environment to improve business outcomes.
Financial services claim analysis
An insurance company can use IoT-enabled cameras and sensors on roadside assistance vehicles to capture real-time visual data, such as accident photos. It may also use cameras (within an iPad, for example) to capture images of home or other property damage.
Gravio’s edge computing processes the data on-site. An AI model — with human review — performs legal causality analysis, determining the sequence of events and liability. The data is transferred via Console Connect’s secure private networking to Vultr’s cloud servers.
Vultr Serverless Inference, powered by GPUs, analyzes historical patterns, including metadata, lighting patterns and pixel anomalies to assess damage and detect potentially manipulated photos. The process helps ensure policy adherence and maintains compliance with privacy standards. This setup avoids latency and bandwidth issues by filtering data before transmission. Only high-quality, relevant images go to the cloud, reducing compute cost and preserving customer privacy.
Healthcare & life sciences: image analysis
Hospitals may use IoT-enabled devices to capture and transmit patient X-rays, while AI models analyze the images to detect early signs of disease. Examples may include reviewing images for possible signs of lung infection, lung cancer or stomach cancer.
Gravio’s edge platform processes data locally to ensure privacy compliance. Vultr Serverless Inference and cloud GPUs then handle advanced analytics on these large datasets. And Console Connect ensures secure transmission of sensitive medical data between edge devices and the cloud.
As a result, the AI model can quickly flag suspected lesions or inflammation for radiologists to review, generating reports faster and enabling rapid diagnosis and earlier treatment. This flow enables fast triage, with critical images prioritized and delivered to doctors in near real time, helping reduce diagnostic bottlenecks and improving patient outcomes.
Manufacturing and energy: drone surveillance
A manufacturing company can implement IoT devices to oversee quality control in its facilities. Connected drones equipped with cameras and sensors can, for instance, capture real-time images and videos of oil fields and refinery infrastructure.
Gravio orchestrates the processing of sensor and visual data locally for rapid, secure anomaly detection. The data is then transmitted securely via Console Connect’s EDGE SIM infrastructure to a private Vultr server.
Vultr’s high-performance cloud infrastructure supports advanced AI image-processing workflows, including emerging Visual Question Answering (VQA) capabilities: By analyzing images and answering structured queries about them, AI and VQA tools provide actionable insights, detect operational trends and trigger quality-related alerts designed to improve safety and efficiency. This hybrid process helps identify risks — like corrosion, leakage or cracks — before they cause downtime or safety incidents, enhancing operational reliability.
Data security, residency and sovereignty
Now that generative AI capabilities are cropping up seemingly everywhere (e.g, Google Gemini, Microsoft Copilot), users worry more than ever if their data is safe when entered into these agents. While guardrails exist, such as the option to opt out of having data used for AI model retraining, concerns remain over enforcement and transparency.
Vultr follows rigorous data privacy standards, including full support for data residency (where data is stored) and sovereignty (the legal authority a nation or region has over that data). “We can't use your data in any way, shape, or form, for any other external service,” said Cochrane. “Your data is your data only. We store data in your region only.”
As a provider of open-source tools and applications, Vultr also provides a flexible foundation for a growing technology environment. “We offer an open composable infrastructure to harness an entire ecosystem of open source and open standards,” said Cochrane. “We partner with both AMD ad NVIDIA for GPUs because we believe in choice, freedom and flexibility. And you can scale your AI initiatives with maximum performance without breaking the bank.”