The core of our offering is a discrete sensor that leverages multiple inputs (primarily an imaging sensor + PIR-based motion sensing), which feed into a neural network model that executes inference directly on the device. This allows us to do powerful processing on inexpensive hardware.
Our machine-learning stack is built around Tensorflow, which we use in two ways: 1) for inference (we embed Tensorflow directly on a Raspberry Pi), and 2) training new models in the cloud. New models can be pushed remotely to the devices over-the-air to make the sensors “smarter”.
While our sensors are currently trained to count people, our vision is to evolve into a 100% passive "super-sensor" that can be configured to detect thousands of different types of events. Examples that we've explored include things like detecting falls (e.g. during an emergency), counting assets (equipment, furniture, cars), and monitoring equipment usage (for preventative maintenance).
We're happy to chat and would love to hear your thoughts. Some things we've worked on that might be interesting to discuss: rapid-prototyping for hardware (Raspberry Pis +ESP8266), machine-learning, computer-vision, building automation, BLE, B2B sales, keeping sane while drawing bounding boxes, or anything else that comes to mind!
We look forward to your feedback!
Dan + Kelby
