Plainsight Technologies provides computer vision for a wide variety of business use cases in the form of applications called Vision Intelligence Filters. Pulse 2.0 interviewed the newly appointed CEO, Kit Merker, to learn more about the company.
Kit Merker’s Background
Merker first got interested in computer vision when he was a teenager. And Merker said:
“I had been programming since I was a kid and was always fascinated by artificial intelligence (AI) and genetic algorithms. I built a very simple neural network with supervised learning using back-propagation. I had a 386, so not much computing power, and I was just trying to have my system complete a drawing of a square from partial border pixels.”
“Over the last couple of decades, I went from tinkering with software to working on some of the coolest software projects in the world, including Windows, Office 365, and Kubernetes at software giants Microsoft and Google, and later as an executive scaling software startups JFrog and Nobl9.”
Responsibilities At The Company
What are Merker’s goals as CEO? Merker shared:
“Plainsight Technologies sits at a point in the software stack – between the camera and the spreadsheet. We help businesses adopt computer vision in a practical and impactful way. We focus on situations where the return on investment (ROI) is super clear. Plainsight offers Vision Intelligence Filters designed to solve specific business problems that extra business intelligence from image data.”
“As CEO, my responsibility and focus right now is to scale the business by building a fantastic product that meets customers where they are. A lot of customers I talk to are overwhelmed by the rapid pace of change in AI and the complexity of building and consuming solutions. And they worry about the implications of giving their precious data to AI companies that will train and resell models to their competition. This creates an amazing opportunity for Plainsight – to not steal data, and to make it really easy to adopt computer vision for businesses.
Favorite Memory
What has been your favorite memory working for the company so far? Merker reflected:
“The most exciting experience was when I was introduced to a customer who demoed back to me how filters worked and the before and after of adopting our technology. Hearing from a customer firsthand the impact the technology has on their business gets me excited and drives me to work harder to serve them.”
Core Products
What are the company’s core products and features? Merker explained:
“The main concept is a factory where we build vision intelligence filters for businesses to quantify and measure their inventory, produce, customers, and other real-world business entities. We make the filters available to customers via our Filter Repository. This lets customers attach custom AI models trained on their private image data, and run them on their cloud or edge devices in their environment. We also provide management tools to configure, optimize, report, and improve over time. Because our filters are self-contained vision AI applications, they are easy to roll out and update over time.”
Challenges Faced
After asking Merker about the challenges he faced in his sector of work, he acknowledged:
“There are a few areas that are hurting AI adoption. First, a lot of companies are rushing to offer AI so they can be part of the trend. Second, AI providers are harvesting customer data and trying to monetize it to other parties, often without permission or compensation. Third, the pace of change is causing fear, uncertainty, and doubt and leading to indecision and delays, or worse, spending a fortune on consultants. Finally, point solutions that are incredibly targeted are gaining traction, however, these solutions are creating management and data silos within enterprises which make it really tough to integrate later.”
Evolution Of Plainsight’s Technology
How has the company’s technology evolved since launching? Merker noted:
“Plainsight Technologies vision filters really are a brand new product. The underlying technology was developed over several years but had been sold and packaged in custom services projects. We are now investing in significant enhancements to the core code assets and building a new delivery model that is a product-focused, subscription-based, software solution that can be hosted in the cloud or run on-premises or at the edge. This new model means customers are able to quickly adopt and scale without the long project delays and cost overruns associated with custom projects.”
Significant Milestones
What have been some of the company’s most significant milestones? Merker cited:
“We are working with several customers on the new filter runtime and are looking forward to a bigger product launch later this year. And we have a long list of filters that we will build to serve the demands of the market.”
Customer Success Stories
After asking Merker about customer success stories, he highlighted:
“JBS, which is one of the world’s largest producers of beef uses our technology to to count 40,000 head of cattle per day, with 99.9% accuracy. This is much better than a human could do at a fraction of the cost. And vision intelligence filters never take vacations. We are also working with a variety of industries including marine biology, car washes, movie theaters, agriculture, wildfire detection, real estate, and more. Each filter solves a unique business problem, but the computer vision capabilities are reusable which makes it incredibly scalable for us to serve a variety of verticals.”
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Merker assessed:
“The computer vision market broadly is enormous, and estimated to grow to some $50 billion by 2030. But our focus is on making sure we have the best product that can easily help businesses today that don’t have AI or machine learning expertise, and that are traditionally underserved by silicon valley startups and management consulting firms.”
Differentiation From The Competition
What differentiates the company from its competition? Merker affirmed:
“First of all, we don’t take customer data to train and resell models. Customers get to keep their data. The other thing I would say is that we found a healthy balance between offering niche or targetted use cases and offering a platform. We don’t require AI or ML expertise to get value, and so we sell to business stakeholders primarily while making it easy for IT teams to integrate us into their environment. I think our focus on pre-built and customizable filters means you get the benefits of building blocks with the flexibility of custom solutions without the headaches.”
Future Company Goals
What are some of the company’s future company goals? Merker concluded:
“Fill the gap between cameras and spreadsheets for as many customers as we can.”
