Dynam.AI clients have one thing in common, an innovation mindset and the desire to make their image data smarter. The following use cases are a glimpse of what is possible.

Medical Devices

Deep Learning applied to image-recognition functions helps with recognizing complex patterns in the images and provides assessments and/or guidance using medical characteristics.

AI models can be an effective tool for medical procedures such as:

  • Image-guided spine alignment during surgery
  • Detection of cardiovascular abnormalities
  • Identification of lung disease such as pneumonia
  • Monitor the development of tumors and melanoma
  • Check for fractures using high-resolution medical imagery
  • Monitor patient vitals & blood glucose levels
  • Track the flow of blood through the body

AI can help physicians to provide more timely and precise treatments to patients. 

Medical Devices Brochure


As manufacturing facilities are becoming fully automated, the demand for more intelligent systems to monitor industrial processes and outcomes is increasing.

While the Internet of Things (IoT) is revolutionizing the manufacturing sector and making industrial operations more autonomous, machine vision is being used for quality inspection of manufactured products for the detection of non-conformities and defects.

Significant advancements in computer vision and deep learning make robotics more powerful.

Deep learning-based machine vision allows robots to make complex decisions about parts classification, allowing for less human interference and more accuracy.

Artificial intelligence (AI) and machine learning in finance are revolutionizing how consumers and companies alike access and manage their finances. According to Business Insider, “the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023”. 

Financial Advisors need to understand their clients’ needs better to provide the services that help maximize assets under management. 

  • Providing clients with the ability to manage their own financial health & personalizing the experience
  • Using analytics to identify the customer life stages
  • Using computer vision and machine learning for security and fraudulent activity “deep fake” detection
  • Turning your proprietary financial models & methodologies into an algorithmically driven platform
  • Risk analysis and management solutions with performance simulations
  • Consumer profiling based on transactional data – used to drive upsell, cross-sell
  • Credit risk profile intelligence and advisor fraud detection


Computer vision plays a significant role in a wide range of security applications.  Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. Below are a few examples of how our team can apply AI to computer vision security applications to help you keep people, property, and the spaces where we live and work safe and secure.

  • Cybersecurity – with deep learning, new applications of computer vision techniques have been introduced and are now becoming part of our everyday lives
  • Face recognition, indexing, and photo stylization
  • Machine vision in self-driving cars
  • Port security and cargo inspection
  • Facility security for governments, power plants, banks, hospitals, offices, etc…
  • Video surveillance for offices, ports, parking lots, parks, stadiums, malls, train stations, etc…

The challenge is not in acquiring surveillance data from these video cameras, but in identifying what is valuable, what can be ignored, and what demands immediate attention.



According to the 29th Annual Retail Technology Study by RIS, only 3% of retailers have already implemented computer vision technology, with 40% planning to implement it within the next two years.

The customer experience can be redefined by making store layout improvement decisions based on real data rather than guesswork. Consumers expect as much personalization and convenience in retail stores as they experience online.

  • Routine tasks can be automated with autonomous robots.
  • Inventory management – Object Character Recognition (OCR)
  • Analyzing customer movement heatmaps
  • Computer vision customer heatmaps
  • Virtual mirrors and recommendation engines

The potential for AI and machine learning to enable and improve computer vision systems is almost without limit.  Reach out today to learn how Dynam.AI can help you get the most out of your image and video data.