At Plainsight, we focus on making computer vision development more practical and accessible. Traditionally, building CV applications required deep expertise, specialized tools, and long development cycles. Our work aims to simplify that process by providing a framework that helps developers turn raw camera feeds into structured, usable data without having to reinvent complex vision systems.
In a recent discussion, Plainsight CTO, Venky Ranganathan, and Principal Software Engineer Abhijit Bhatnagar walked through what Plainsight offers today and how OpenFilter and the new MCP Server are shaping the next generation of vision-based applications.
For many years, even simple applications, such as reading license plates from a parking lot camera, required significant custom code and a deep understanding of vision algorithms. Plainsight streamlines this effort by giving developers a set of modular building blocks called Filters. Using community Filters from the OpenFilter Hub or Filters they create themselves, developers choose exactly what visual information they want to extract. Each Filter includes the code, data, and models necessary to perform a specific task, and the system continues to refine those models as new data comes in.
Instead of dealing with low-level vision routines, developers can focus on building the logic of their applications. The pipeline model at Plainsight ties these components together so the output from one stage naturally flows into the next. Whether identifying a region of interest, classifying objects, or transforming outputs into structured records, the system manages the heavy lifting. These pipelines can run anywhere, from Kubernetes clusters to cloud environments, giving teams flexibility in how they deploy their solutions.
As generative AI becomes more integrated into everyday development workflows, Plainsight and OpenFilter are embracing it through the OpenFilter MCP Server. The Model Context Protocol allows AI agents to interact with external tools and resources, effectively extending what they can do beyond their original training.
With the MCP Server, an AI agent can help create or configure vision pipelines directly from natural-language instructions. That means developers no longer need specialized knowledge of OpenFilter’s internal APIs. Instead, they can simply tell their IDE or agent what they want to build, and the system generates the required components behind the scenes. Over time, we plan to make this integration even smoother by publishing the MCP server to public repositories so it can be automatically discovered and added to development environments.
One of the longstanding challenges in computer vision is how applications are measured. Engineers often rely on metrics like accuracy or recall, which expose the internal workings of a model. While useful for development, these metrics have limited value for operators who rely on the system day-to-day. They need a much clearer signal—something as simple as whether the application is ready for use.
Plainsight’s test infrastructure addresses this by providing a straightforward pass-or-fail result that reflects whether a model is meeting expectations. Tests can run at any point in the pipeline and can be defined by the user or drawn from a prebuilt set designed for common vision tasks. This approach brings the discipline of traditional software engineering into the machine-learning space. Concepts like development, staging, and production environments, along with clear deployment checks and observability, begin to look much more unified and predictable.
Example: The comparison image shows a side-by-side analysis of two object detection models (SAM-V2 and SAM-V3) tasked with identifying the iconic arches of the Pacific Science Center in the Seattle skyline. The ground truth confirms that arches should be detected throughout the video. SAM-V2 (Current) correctly detects the arches consistently with 0 errors, while SAM-V3 (Proposed) fails to detect the arches during a segment from 2-8 seconds, resulting in 1 disagreement with ground truth. The verdict panel shows FAIL: 0 vs 1 errors, indicating that the proposed SAM-V3 model performs worse than the current SAM-V2 model for this arch detection task. The real-time details panel on the right displays the current prediction state for each model alongside the expected ground truth value, making it easy to identify exactly when and where the proposed model diverges from expected behavior.
Today, machine-learning development lacks the consistent process that guides traditional software engineering. Plainsight’s pipeline lifecycle model aims to bring that consistency to CV. By emphasizing clear environments, defined deployment stages, and strong monitoring practices, it becomes much easier for teams to build, test, and evolve their models with confidence. The result is a workflow that feels more like standard software development—just with the added step of incorporating a model into the pipeline.
Developers interested in exploring this ecosystem can visit plainsight.ai to learn more about the platform or explore open-source filters and resources at openfilter.io. Our GitHub repositories offer examples, code, and opportunities for community collaboration.
As we continue expanding metrics, telemetry, and AI-assisted development tooling, user feedback plays a key role in shaping what comes next. The more people build with Plainsight and OpenFilter, the faster the entire ecosystem evolves.
If you're interested in watching the full interview you can see it here. Stay tuned for more from FilterLab!