Key Takeaways
- Plainsight Technologies focuses on making computer vision accessible and scalable through its open-source framework, OpenFilter.
- OpenFilter excels in large-scale deployments, particularly in sectors like retail, logistics, and manufacturing.
- Plainsight differentiates itself from competitors by offering a modular, open-source approach that enhances developer productivity and ease of maintenance.
Kit Merker is a technology industry leader with over 20 years of experience building software products. He serves as CEO of Plainsight Technologies and previously held senior positions at Nobl9, JFrog, Google and Microsoft.
Tell us about your company.
Plainsight is focused on making computer vision accessible and scalable for everyone. Our core technology is OpenFilter, an open-source framework that lets you build, deploy, and manage computer vision applications using modular components we call “filters.” A filter is essentially an abstraction that combines code and models, packaged as an app. You can string these filters together to create pipelines, and because they’re containerized, you can deploy them pretty much anywhere Docker runs. The idea is to provide a universal way to describe, manage, and scale vision workloads, moving from prototyping to production seamlessly. Our team comes from a background in distributed and cloud systems. My CTO was an early engineer on Google Dataflow, and I was an early product manager on Kubernetes, so we’ve brought that operational rigor to vision workloads. We’ve battle-tested this technology internally and with customers, and now we’re open sourcing it to benefit the broader community.
What problems are you solving?
The biggest challenge in computer vision is the gap between prototyping and production. Many vision projects get stuck after the prototype phase because scaling and maintaining them is incredibly difficult. There aren’t enough vision engineers, and the infrastructure is complex and expensive. OpenFilter addresses this by providing a scale-agnostic way to describe and deploy vision applications. Developers can go from a working prototype to production without a complete rewrite, and the modular approach means updates, maintenance, and scaling are much simpler. We also help reduce infrastructure and inference costs by allowing smarter resource allocation and workload pooling. Ultimately, we’re unlocking latent demand for vision by making it easier and cheaper to build and deploy real-world applications.
What application areas are your strongest?
OpenFilter shines in large-scale, complex deployments. If you have hundreds or thousands of cameras, large amounts of data, or distributed environments, think retail chains, logistics, or manufacturing, our platform really stands out. It’s also great for building complex vision pipelines involving object detection, tracking, segmentation, and classification. The system integrates with a wide range of data sources, including RTSP streams, webcams (except on Mac), and IoT frameworks like MQTT. While you can use it for small projects, its real value comes when you need to scale, manage costs, and handle continuous updates across many locations or devices.
What keeps your customers up at night?
Our customers are concerned about how to take vision solutions from prototype to production, manage costs, especially GPU and inference costs, and keep everything updated as requirements evolve. Integration with existing data sources and business logic is another big pain point, as is the shortage of skilled vision engineers. They need to be able to scale quickly, manage infrastructure efficiently, and ensure their systems are maintainable over time. The complexity and cost of building and maintaining these systems is what keeps them up at night.
What does the competitive landscape look like and how do you differentiate?
The main competition we see is from homegrown solutions, where teams stitch together open-source libraries like OpenCV or YOLO with custom code. These systems are often brittle and hard to maintain, especially at scale. There are some commercial products out there, but few offer the open-source, modular, and scalable approach that OpenFilter does. Our differentiation comes from the filter abstraction, which lets you combine code and models into reusable, composable units. This makes it easy to move from prototype to production without rework, and the same abstractions work at any scale. We also offer both open-source and commercial support, with the commercial version adding features like supply chain security, telemetry, and proprietary model training. Our approach dramatically improves developer productivity and makes maintenance and scaling much easier.
What new features or technology are you working on?
We’re actively expanding model support. Right now we support PyTorch, but we plan to add other architectures. We’re also working on community edition Docker images to simplify deployment, and adding more downstream data connectors like Kafka, Postgres, and MongoDB. The commercial offering includes enhanced telemetry, supply chain security, and proprietary model training for advanced use cases. Looking ahead, we see potential to extend OpenFilter to other data modalities like audio, text, and geospatial data, and to integrate with agentic and generative AI systems for pre- and post-processing.
How do customers normally engage with your company?
Customers engage with us in several ways. Developers and vision engineers can download and use OpenFilter directly as open source, experimenting with their own models and data. Organizations that need enterprise features or support can license our commercial offering, VisionStack. We also work with services partners to deliver custom solutions and support for complex deployments. Community contributions are encouraged, and we’re building a community around reusable filters and best practices. For those moving from prototype to production, we provide expertise, patching, and support to help them succeed.
Is there anything else you want readers to know?
The biggest “aha” moment for me in computer vision was realizing the gap between supply and demand. There’s enormous latent demand for vision solutions, but the cost and complexity have limited adoption to only the highest-ROI projects. We believe the filter abstraction is the innovation that will unlock this value and democratize computer vision. By making it easier, cheaper, and more consistent to build and deploy vision applications, we hope to see much broader adoption and innovation in the field.
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