Plainsight, the leader in automated infrastructure for data-centric AI pipelines, today announced the launch of OpenFilter, an open source project designed to simplify and accelerate the development, deployment, and scaling of production-grade computer vision applications. Released under the Apache 2.0 license, OpenFilter introduces a novel “filter” abstraction, merging code and AI models into modular components that empower developers to assemble vision pipelines efficiently. Plainsight will showcase OpenFilter at the Embedded Vision Summit, booth #518, with CEO Kit Merker presenting on May 22 in a session titled “Beyond the Demo: Turning Computer Vision Prototypes into Scalable, Cost-Effective Solutions.”
Revolutionizing Vision AI Deployment
“OpenFilter has revolutionized how we deploy vision AI for our manufacturing and logistics clients,” said Priyanshu Sharma, Senior Data Engineer at BrickRed Systems. “Its modular filter architecture lets us build and customize pipelines for automated quality inspection and real-time inventory tracking without rewriting core infrastructure. This flexibility enables us to deliver robust, scalable solutions while drastically cutting development time and operational complexity.”
OpenFilter tackles key enterprise challenges by optimizing GPU inference costs with frame deduplication and priority scheduling, and by shortening deployment timelines from weeks to days. Its extensible architecture is designed to evolve beyond vision, adapting easily to audio, text, and multimodal AI applications, positioning OpenFilter as a foundational platform for scalable, agentic AI systems.
Bridging the Prototype-to-Production Gap
Traditional computer vision projects often face delays due to fragmented tools and scalability issues. OpenFilter addresses these with:
- Open Source Core: Apache 2.0 licensed runtime and pre-built filters for tracking, cropping, segmentation, and more.
- Filter Runtime: Seamless management of video inputs (RTSP, webcams, image files), processing workflows, and output routing to databases, MQTT, or APIs.
- Modular Pipelines: Easily assemble reusable filters for object detection, deduplication, alerts, and other tasks into flexible workflows.
- Flexible Deployment: Deploy filters across CPUs, GPUs, or edge devices to optimize resource use and costs.
- Broad Model Support: Integrate popular frameworks like PyTorch, OpenCV, or custom models such as YOLO, avoiding vendor lock-in.
OpenFilter Use Cases
- Manufacturing: Automated quality control, defect detection, fill level monitoring.
- Retail & Food Service: Drive-through analytics, item counting, inventory tracking.
- Logistics & Supply Chain: Vehicle tracking, inventory automation, workflow optimization.
- Agriculture: Precision farming and livestock monitoring via drone/camera data.
- Security: People counting, surveillance automation, safety enforcement.
- IoT & Edge Computing: Event detection and real-time alerting.
Industry Endorsements and Vision
“Filters are the building blocks for operationalizing vision AI,” said Andrew Smith, Plainsight CTO. “Developers can snap reusable components together to scale from prototypes to production, making computer vision feel like software engineering rather than science experiments.”
Chris Aniszczyk, CTO of CNCF, commented, “OpenFilter’s modular design and Apache 2.0 license make it a leap forward for open source, enabling organizations from agriculture to retail to unlock vision AI’s potential at scale.”
Plainsight CEO Kit Merker added, “OpenFilter is the AI industry’s awaited abstraction, enabling anyone to transform camera data into real business value faster and more cost-effectively. Treating vision workloads as modular filters empowers developers to build, scale, and update applications with the agility of modern cloud software. This is about democratizing computer vision and unlocking the next wave of AI-driven transformation.”
