Neuromorphic
Neuromorphic provides a smart camera with on-device vision-language models that interpret scenes in real time for robotics applications. The hardware-software stack connects via a single Ethernet cable and appears as a ROS node or TCP server, enabling natural-language queries like "scene graph as JSON" without cloud dependency. This eliminates the need for custom vision pipelines in unstructured environments.
- Robotics
- Artificial Intelligence
- Hardware
- Industrial Automation
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Every robotics team writes its own vision pipeline from scratch, and the result still fails when the scene drifts off-script. This makes autonomy fragile and slows deployment, because perception—not motion—becomes the bottleneck in real-world environments.
Solution
Neuromorphic provides a smart camera that understands the world, purpose-built in-house as a complete hardware and software stack. The camera runs compressed vision-language models on edge compute, so it can interpret scenes locally without cloud dependency. Users interact with the camera using natural language prompts like "scene graph as JSON" or "flag any humans nearby," and the output streams directly into their robotics stack. The device connects over a single Ethernet cable and appears as a ROS node or TCP server, requiring no dedicated vision team to integrate. This approach is designed for the unstructured environments where robots actually operate.
Target Audience
The primary customers are robotics teams and manufacturers who need reliable perception in unstructured environments and want to avoid building and maintaining custom vision pipelines.
Features
- In-house designed hardware with custom sensor integration for robotic perception tasks
- Purpose-built edge compute enabling on-device inference without external processing
- Compressed vision-language models that support natural-language scene queries and structured output (e.g., JSON scene graphs)
- Standard interface via one Ethernet cable, exposed as a ROS node or TCP server for drop-in integration
- Real-time scene understanding for tasks such as detecting nearby humans, counting pallets, and generating semantic maps