The Snap Platform

A turnkey machine vision system that handles everything from device management to algorithm development to support your quality and traceability goals.

All supported remotely by our in-house experts with zero on-site machine learning experience necessary.

A simple platform architecture

Camera stations are easy to setup

The Snap Platform combines cameras, data and algorithms using stations. Stations can be controlled manually by operators or run automatically. You can inspect products at high speed or catalog shipments for damage disputes.
An operator can trigger the image and data capture, in order to:
Add automated inspection to manual production line
Inspect and check from a mobile phone
Prototype automated inspection stations without test hardware
Smart cameras can automatically inspect from a PLC trigger, interval, or, in the case of video events, continuous capture. They can:
Integrate with existing automation
Install quickly where there is no existing automation
Run and perform inspections without an internet connection

Algorithms are simple to create and maintain

All data is saved, searchable and analyzed

Unlike a typical vision system, the Snap Platform saves all data from your process. Data is catalogued for easy search and analysis. Find new sources of yield loss and determine the root cause with data post-processing.

Deployment in as little as a week, with zero machine vision experience necessary

We support most hardware and are flexible across industrial environments. The Snap Platform includes easy-to-install smart cameras, webcam-based inspections in-lab settings, mobile inspections for field operators and even supports upgrading underperforming existing machine vision systems. Our rugged and reliable system can be deployed in as little as a week by teams with zero machine vision experience.

Integrates with your existing system and communicates across networks

Easily integrate a camera into a new or existing production system. Building rules to trigger alerts and communications can be done in a few minutes by anyone. Alerts or messages sent in:
Snap 7
Native Stack Light Control
Native Relay Control
Snap 7
Native Stack Light Control
Native Stack Light Control
More availability and less downtime with instant, best-in-class system support. 95% of setup, upgrades and troubleshooting can be done remotely. Issues are resolved in minutes instead of days through our remote connectivity.
Russell Nibbelink Overview Co-founder

Russell Nibbelink

Co-founder & Head of Engineering
Russell is a co-founder and head of engineering of Overview. At Overview, Russell helps customers implement cutting edge high speed Deep Learning algorithms. Prior to joining Overview, he was a software engineer at Salesforce where he worked on web-scale infrastructure projects. Russell received his BS from UC Berkeley’s College of Engineering, where he helped teach a class on High Performance Computing.
Chris Van Dyke Overview CEO

Chris Van Dyke

Co-founder & CEO

Chris Van Dyke is a co-founder and the CEO of Overview. Prior to founding Overview, Chris spent eight years in manufacturing engineering at Tesla. Most recently, he led the 80 person battery design team through the launch of the Model 3. During which time he was principally responsible for taking the team from battery design to high-volume production. Earlier in his career at Tesla he managed the infrastructure and equipment design for the first Gigafactory project including equipment for supporting battery cell manufacturing. Chris also launched Tesla’s Electric Vehicle Supercharger Program, which currently has more than 16,000 stations nationwide.

Chris holds several patents from his time leading custom equipment design while a Senior Engineer at H2Gen Innovations. Chris received his BS from Stanford University in Mechanical Engineering and an MS from the University of Virginia in Chemical Engineering.

Austin Appel

Co-founder & Head of Product

Austin Appel is a co-founder and head of product of Overview. At Overview, Austin leads product development and operations. Prior to Overview, Austin spent four years at Tesla in roles across battery manufacturing and R&D. He led the DFM and automation efforts for Model 3 battery pack production at Tesla’s first Gigafactory. Previously he expanded Tesla’s production of Model S and X battery packs through equipment design and implementation. He holds a dual bachelors degree in Manufacturing Engineering and Mechanical Engineering from Northwestern University.