A Scientific Approach to Computer Vision Technology

Science-Driven, Client-Centric, AI for Machine Vision

Our clients understand that precision and accuracy are paramount to achieving constant improvement, lasting value, and a persistent competitive advantage. They know that our AI services and technologies will deliver what traditional rules-based computer vision can’t.

We combine leading computer vision innovation and expertise with the use of tried and true models to provide not only breakthrough results but also the highest levels of program success.  Organizations seek us out when:

        • Accuracy truly matters
        • Image and video inputs are non-traditional
        • Physical dynamics of the environment present unique challenges

Computational neuroscience-derived methods, including the surprisal measure and spike sorting, deliver earlier and more accurate artifact detection, reduce overfitting, and improve precision and accuracy.

The highest level of confidence is achieved through our ability to not only look at the confidence bounds of prediction but also the prediction of the confidence bounds.

Data Preparation includes open source and proprietary ways to make sure data is clean, features can be extracted, and noisy data is reduced. Image manipulation tools such as ghost modeling remove non-essential elements of an image and a growing bank of software tools prepare images for deep learning allow trained neural networks to deliver unmatched performance.

Complex Physical Settings often pose unique challenges including environments that require an understanding or projection of a progression or movement of physical dynamics such as temperature, optics, sound, and motion.

Non-Traditional Data Inputs come into play when a task requires us to see the unseeable. Examples include thermal imaging, x-rays, and microscopic data in a variety of formats, varying angles, heights, and depths. Expertise in physics and engineering provides the necessary skills to overcome the challenges non-traditional inputs present.

Transfer Learning is when a pre-existing network that has been trained on one task is trained to do another task. Using a machine learning solution for one problem can deliver a solution to a related problem quickly and with less training data.

Explainable AI is essential for building trust between AI and the people who need to use it to do their jobs. Tools for understanding why a model gave an answer or result speed acceptance of AI-based recommendations within an organization and decrease resistance to AI-driven decisions.

Our Process

Our well-defined program structure provides the leadership and flexibility to serve organizations at any stage of their computer vision journey.

Stage 1

Discovery

We begin with a thorough evaluation of a client’s goals, use case, and project specifications as well as an analysis of their data as it relates to the outcome they seek. We then provide a detailed feasibility assessment and project scope of work specific to their vision.

Stage 2

Design

Next our data science and engineering teams develop a program designed to meet or exceed accepted KPIs. This includes data preparation and modeling as well as the application of proven algorithms from our library of open source and proprietary models and tools. At this stage, we produce a proof of concept (POC) to validate our assumptions.

Stage 3

Develop & Deploy

After a successful POC, we build out the network, integrate the solution into customer business processes, and deploy the solution into production.

Stage 4

Evolve

Once the solution has been deployed, the algorithms continue to learn, evolve, and optimize. We support our clients during this process of continued refinement to fully optimize performance.