Why We Open Sourced Sentinel Core
Modern surveillance is no longer just about cameras and recording. It is about AI systems operating across physical environments — understanding movement, identifying patterns, searching vast volumes of footage instantly, and supporting real-time decision-making. As these systems evolve, the architecture beneath them matters more than ever.
When we built Sentinel, we approached it as an AI-native system from day one. It was never meant to be a legacy video platform with machine learning added later. The foundation was designed around real-time detection, semantic search, vector indexing, and air-gapped deployment. Over time, as the core architecture matured, we made a deliberate decision: the foundational layer should not remain closed.
We open sourced Sentinel Core because foundational infrastructure benefits from participation.
Accelerating the Ecosystem
AI-driven physical security systems are still early. Universities are experimenting. Independent developers are building tools. System integrators are looking for modern stacks they can extend. By releasing the core engine — including real-time processing, semantic search, detection pipelines, and dashboard architecture — we are enabling engineers and researchers to build on top of a serious foundation instead of starting from scratch.
Foundational vs. Managed
This is not about giving away a finished product. Sentinel as a full platform continues to evolve, and enterprise-grade deployments require hardened modules, operational tooling, advanced integrations, and structured implementation. Those layers remain part of our managed and commercial roadmap. Open sourcing Sentinel Core is about accelerating capability development at the architectural level, not exposing operational infrastructure.
The Compound Interest of Progress
We believe modern AI systems for physical environments will improve faster when the core building blocks are accessible. When developers can experiment with semantic video search. When academic institutions can test detection models in real-world pipelines. When integrators can extend architecture instead of being locked into proprietary stacks. Progress compounds.
Sentinel will continue to expand — across behavioral analysis, dashboard capabilities, edge deployment models, and performance optimization. As the system matures, we will continue to open source selectively where it strengthens the ecosystem and accelerates development.
Building resilient AI infrastructure for physical environments requires an architectural baseline that is transparent and extensible. We are releasing the core engine to establish a technical standard for the community to build upon.
Sentinel Core is now open for development on GitHub.