Overview.ai announces Series A funding!

Why use a deep
learning CV solution
for automated inspections?

Deep Learning is a recognition technique that allows inspection systems to find mistakes more consistently and in a wider variety of situations.

Deep learning systems are best suited for computer vision automated inspection, they:

Adapt to the changing conditions typ ical of a factory changing lines and products
Improve over time
and with more data

Inspect complex surfaces

Operate without any on-site (client-based) knowledge of vision algorithm design
Overview’s Snap Platform
Keyence-CV Series
Cognex
Custom-build Solution
Total Cost of Ownership
$$
$$$
$$$
$$$$
Maintenance/Support
Ongoing remote support maintenance, diagnostics, remote upgrades, algorithm improvements, remote programming
N/A
N/A
N/A
Deep Learning or Classical
Deep Learning
Classical
Classical w/ Limited Deep Learning
Classical
Programming Expertise Required
None required
On-site expert required
On-site expert required
On-site expert required
Set-up
Turnkey

Set-up is included in service
Not included

Extensive set-up process and expertise required
Not included

Extensive set-up process and expertise required
Extensive set-up process and expertise required

How does Deep Learning compare to a Classical approach for automated inspections?

Deep Learning is a data-driven process that builds an algorithm based on good and bad example images of an inspection point that is so complex a human could not do it themselves. The algorithm gets better with more data and more time. Inspection cameras can then catch subtle and varied mistakes in numerous situations. It can also handle far more complex visuals than a classical approach. Deep learning systems can look for everything at once, rather than looking for one specific type of failure.
The Classical approach to computer vision solutions is to create hand-coded algorithms and treat a picture as a bunch of values. The Classical approach algorithms are created by a human and are limited in the amount of complexity they can manage. Classical requires a programming expert be on-site to create and manage algorithms for the automated inspection.

Overview provides reliable computer vision inspection systems for manufacturers of sophisticated, high quality products.



We specialize in deep learning algorithms to create the most flexible and accurate automated inspection systems possible.

To see if deep learning works for your factory, schedule a demo.
Russell Nibbelink Overview Co-founder

Russell Nibbelink

Co-founder & Head of Engineering

Russell Nibbelink is the head of engineering and a co-founder at Overview. At Overview, he leads the team that helps customers implement cutting-edge, high-speed deep learning algorithms to reduce manufacturing waste and defective parts. 

Before joining Overview, Russell was a software engineer at Salesforce, where he worked on web-scale infrastructure projects. His many accomplishments at Salesforce include the migration of Salesforce’s largest customers to upgraded hardware with no downtime, live database change record ingestion, and the creation of visibility tooling for calculating data throughput rates.

Russell received his Bachelor of Science from the University of California Berkeley College of Engineering, where he was also the head teaching assistant in the field of High Performance Computing.

Chris Van Dyke Overview CEO

Chris Van Dyke

Co-founder & CEO

Chris Van Dyke is the CEO and co-founder of Overview. He is responsible for the company’s strategic direction, corporate development, sales and marketing. With over 16 years of engineering and manufacturing expertise, Chris focuses on optimizing customer’s manufacturing operations using artificial intelligence and computer vision to accelerate product development, reduce rework, and improve yields. Since founding Overview in 2018, Chris has been instrumental in building a company and team dedicated to a disruptive, positive impact on manufacturing for decades to come. 

Before Overview, Chris served in several senior manufacturing engineering positions at Tesla for eight years. For Tesla’s Model 3, he led the 80-person battery design team from the initial battery concept to the product launch to the high-volume production. He also managed the infrastructure and equipment design for the first Gigafactory, including equipment for supporting battery cell manufacturing. Chris also launched Tesla’s Electric Vehicle Supercharger Program, with more than 25,000 stations worldwide. 

Prior to Tesla, Chris was a senior engineer at H2Gen Innovations, during which time he co-authored multiple patents. Chris holds a Bachelor of Science degree in Mechanical Engineering from Stanford University and a Master of Science in Chemical Engineering from the University of Virginia.

Austin Appel

Co-founder & Head of Product

Austin Appel is the head of product and a co-founder at Overview, where he is responsible for leading product development and operations. With over 8 years of experience in manufacturing and systems engineering, Austin works with customers to ensure that manufacturing operations can quickly adopt and use artificial intelligence-based computer vision systems.

Before Overview, Austin spent four years at Tesla in various capacities, including battery manufacturing, and research and development (R&D). He led the design for manufacturability (DFM) and automation efforts for the Model 3 battery pack production at Tesla’s first Gigafactory. Earlier at Tesla, he expanded the production of Model S and X battery packs through equipment design and implementation.

Austin holds a dual Bachelor of Science degree in Manufacturing Engineering and Mechanical Engineering from Northwestern University.