Plainsight unveils OpenFilter to simplify vision AI pipelines
Plainsight has launched OpenFilter, an open source project designed to simplify and accelerate the development, deployment, and scaling of production-grade computer vision applications.
OpenFilter is available under the Apache 2.0 licence and is designed to help enterprises build, deploy, and manage vision AI pipelines using modular, reusable components, referred to as "filters". These filters combine code and AI models into building blocks for assembling custom vision pipelines.
The project aims to address key challenges that organisations face when implementing AI-driven computer vision in production environments, such as cost, scalability, and the complexity of infrastructure integration.
Priyanshu Sharma, Senior Data Engineer at BrickRed Systems, explained the practical benefits seen in manufacturing and logistics implementations. "OpenFilter has revolutionised how we deploy vision AI for our manufacturing and logistics clients. With its modular filter architecture, we can quickly build and customise pipelines for tasks like automated quality inspection and real-time inventory tracking, without having to rewrite core infrastructure. This flexibility has enabled us to deliver robust, scalable solutions that meet our clients' evolving needs, while dramatically reducing development time and operational complexity," Sharma said.
Plainsight claims that OpenFilter's features - including frame deduplication and priority scheduling - lower GPU inference costs, while its abstractions are intended to shorten deployment timelines from weeks to days. The system's extensible architecture is designed to future-proof investments, offering compatibility not only with computer vision but also adaptable extensions for audio, text, and multimodal AI use cases.
OpenFilter aims to bridge a common gap in computer vision adoption, where projects can stall due to fragmented tooling and difficulties in scaling from prototype to production. The platform includes several features: a core runtime available as open source, pre-built filters for tasks such as object tracking and image segmentation, and a pipeline management system that can handle various video inputs like RTSP streams, webcams, and image files. It enables routing of processed data to destinations including databases, MQTT brokers, or APIs.
The system is designed to support deployment across a wide range of hardware, from CPUs and GPUs to edge devices, allowing for resource optimisation in different environments. OpenFilter supports broad model integration, letting users deploy models from frameworks such as PyTorch and OpenCV, or custom models like YOLO, without vendor lock-in.
Typical use cases for OpenFilter span a variety of sectors. In manufacturing, the platform can be used for automated quality inspection, defect detection, and fill-level monitoring. Retailers and food service operations may use it for drive-through analytics or inventory tracking, while logistics operators could automate vehicle tracking or workflow processes. Additional applications include precision agriculture, surveillance, people counting, and event detection for IoT and edge environments.
Andrew Smith, CTO of Plainsight, commented on the broader aim for OpenFilter's architecture. "Filters are the building blocks for operationalising vision AI," Smith said. "Instead of wrestling with brittle pipelines and bespoke infrastructure, developers can snap together reusable components that scale from prototypes to production. It's how we make computer vision feel more like software engineering - and less like science experiments."
Chris Aniszczyk, CTO of CNCF, endorsed the open source nature of OpenFilter, saying, "OpenFilter is a leap forward for open source, giving developers and data scientists a powerful, collaborative platform to build and scale computer vision AI. Its modular design and permissive Apache 2.0 license make it easy to adapt solutions for everything from agriculture and manufacturing to retail and logistics, helping organisations of all types and sizes unlock the value of vision-based AI."
Kit Merker, CEO of Plainsight, described the broader ambition for OpenFilter in the industry. "OpenFilter is the abstraction the AI industry has been waiting for. We're making it possible for anyone - not just experts - to turn camera data into real business value, faster and at lower cost," Merker said.
"By treating vision workloads as modular filters, we give developers the power to build, scale, and update applications with the same ease and flexibility as modern cloud software. This isn't just about productivity, it's about democratising computer vision, unlocking new use cases, and making AI accessible and sustainable for every organisation. We believe this is the foundation for the next wave of AI-powered transformation."
Plainsight has made OpenFilter available to the public under the Apache 2.0 licence and offers an Early Access Programme for enterprises interested in a commercial version of the platform.
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Techday NZ
22-05-2025
- Techday NZ
Plainsight unveils OpenFilter to simplify vision AI pipelines
Plainsight has launched OpenFilter, an open source project designed to simplify and accelerate the development, deployment, and scaling of production-grade computer vision applications. OpenFilter is available under the Apache 2.0 licence and is designed to help enterprises build, deploy, and manage vision AI pipelines using modular, reusable components, referred to as "filters". These filters combine code and AI models into building blocks for assembling custom vision pipelines. The project aims to address key challenges that organisations face when implementing AI-driven computer vision in production environments, such as cost, scalability, and the complexity of infrastructure integration. Priyanshu Sharma, Senior Data Engineer at BrickRed Systems, explained the practical benefits seen in manufacturing and logistics implementations. "OpenFilter has revolutionised how we deploy vision AI for our manufacturing and logistics clients. With its modular filter architecture, we can quickly build and customise pipelines for tasks like automated quality inspection and real-time inventory tracking, without having to rewrite core infrastructure. This flexibility has enabled us to deliver robust, scalable solutions that meet our clients' evolving needs, while dramatically reducing development time and operational complexity," Sharma said. Plainsight claims that OpenFilter's features - including frame deduplication and priority scheduling - lower GPU inference costs, while its abstractions are intended to shorten deployment timelines from weeks to days. The system's extensible architecture is designed to future-proof investments, offering compatibility not only with computer vision but also adaptable extensions for audio, text, and multimodal AI use cases. OpenFilter aims to bridge a common gap in computer vision adoption, where projects can stall due to fragmented tooling and difficulties in scaling from prototype to production. The platform includes several features: a core runtime available as open source, pre-built filters for tasks such as object tracking and image segmentation, and a pipeline management system that can handle various video inputs like RTSP streams, webcams, and image files. It enables routing of processed data to destinations including databases, MQTT brokers, or APIs. The system is designed to support deployment across a wide range of hardware, from CPUs and GPUs to edge devices, allowing for resource optimisation in different environments. OpenFilter supports broad model integration, letting users deploy models from frameworks such as PyTorch and OpenCV, or custom models like YOLO, without vendor lock-in. Typical use cases for OpenFilter span a variety of sectors. In manufacturing, the platform can be used for automated quality inspection, defect detection, and fill-level monitoring. Retailers and food service operations may use it for drive-through analytics or inventory tracking, while logistics operators could automate vehicle tracking or workflow processes. Additional applications include precision agriculture, surveillance, people counting, and event detection for IoT and edge environments. Andrew Smith, CTO of Plainsight, commented on the broader aim for OpenFilter's architecture. "Filters are the building blocks for operationalising vision AI," Smith said. "Instead of wrestling with brittle pipelines and bespoke infrastructure, developers can snap together reusable components that scale from prototypes to production. It's how we make computer vision feel more like software engineering - and less like science experiments." Chris Aniszczyk, CTO of CNCF, endorsed the open source nature of OpenFilter, saying, "OpenFilter is a leap forward for open source, giving developers and data scientists a powerful, collaborative platform to build and scale computer vision AI. Its modular design and permissive Apache 2.0 license make it easy to adapt solutions for everything from agriculture and manufacturing to retail and logistics, helping organisations of all types and sizes unlock the value of vision-based AI." Kit Merker, CEO of Plainsight, described the broader ambition for OpenFilter in the industry. "OpenFilter is the abstraction the AI industry has been waiting for. We're making it possible for anyone - not just experts - to turn camera data into real business value, faster and at lower cost," Merker said. "By treating vision workloads as modular filters, we give developers the power to build, scale, and update applications with the same ease and flexibility as modern cloud software. This isn't just about productivity, it's about democratising computer vision, unlocking new use cases, and making AI accessible and sustainable for every organisation. We believe this is the foundation for the next wave of AI-powered transformation." Plainsight has made OpenFilter available to the public under the Apache 2.0 licence and offers an Early Access Programme for enterprises interested in a commercial version of the platform.
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