Insights in context, delivered.
Capture insights, and act on them. Can your telematic devices do that? If not, maybe it’s time to look into Flybeam.
We work with any telematics, any IoT, and even (especially?) businesses deploying sensors from multiple vendors. Flybeam’s clients move past dots on the map, to proactive action.
Our Edge AI inference and purpose-built AI models help enterprises significantly reduce operating costs, lowering Non-productive Time (NPT), Non-Billable Revenue (NBR), and operating costs in some cases by as much as 50% over a full year period.
Large enterprises typically deploy diagnostic sensors from multiple vendors. Flybeam’s proprietary Model Context Protocol layer lets them tap data from disparate vendors, standardized and in context. For example, customers can pull data from Samsara, Verizon and Orbcomm devices into the same AI model, bridge across data silos, and act on insights.
Oil and Gas Predictive Maintenance Model
Cold Chain Spoilage Prediction
Rental Vehicle Valuation Model
Flybeam: Edge AI for Sensors
Flybeam taps diagnostic and environmental data such as temperature and vibration to uncover signals which are predictive of issues. We ingest data on the edge, and apply context on the cloud, to deliver results for Global 50 clients.
What is a Model Context Protocol? Why is it important?
An MCP is a tool which lets you bring data into AI models in context, and standardized. AI models are starved for this context, and enterprises are eager to bring the data from disparate sensors into AI models to drive insights. Your AI is only as good as the data going into it.
At Flybeam, we’re experts who understand the API endpoints of major telematics endpoints. We’ve built applications on top of APIs at multiple telematics companies, so we know exactly how they work. With that knowledge, we are able to provide the context for APIs across the telematics and IoT sector.
As it turns out, APIs are very similar across different telematics vendors. They have different names and protocols for accessing the data, but generally, they describe the same things. For example, one API might offer a “latitude/longitude” while the other offers “lat-long.” We can bridge that gap, and bring it all in just the same.
