END-TO-END SERVICE

END-TO-END SERVICE

A single Edge Device is able to send alerts and notifications and to calculate even complex KPIs based on on the local observation of a context. This is what we call Stand Alone mode.

For large volumes installations, devices could be connected together to exchange informations in a peer-to-peer mode. In those cases we can offer a Full managed End-to-End Service. The customer won’t have to worry about managing the network and we are able to provide dedicated and configurable Service Level Agreements.

For more complex scenarios, Edge devices, besides supporting local Deep Learning models, are also capable to capture other traditional IoT features that can be streamed via MQTT and analyzed on-the-fly by our service platform. This is required when more complex contexts must be analyzed leveraging both Vision on the Edge and IoT capabilities. We can provide also integrations with existent IoT Platforms leveraging four basic system features:

  • Edge APIs: local APIs from where you can retrive local KPIs calculations and push alerts based on the single device “context” observation
  • Device Management: device registry and device management tools
  • Dashboard Device composer: to compose single device or single entitities tracking views and monitor a specific context
  • Advanced BI: for more complex KPIs calculations that must be performed over the network of devices also with real-time requirements
platform_aim2
platform_aim2

A single Edge Device is able to send alerts and notifications and to calculate even complex KPIs based on on the local observation of a context. This is what we call Stand Alone mode.

For large volumes installations, devices could be connected together to exchange informations in a peer-to-peer mode. In those cases we can offer a Full managed End-to-End Service. The customer won’t have to worry about managing the network and we are able to provide dedicated and configurable Service Level Agreements.

For more complex scenarios, Edge devices, besides supporting local Deep Learning models, are also capable to capture other traditional IoT features that can be streamed via MQTT and analyzed on-the-fly by our service platform. This is required when more complex contexts must be analyzed leveraging both Vision on the Edge and IoT capabilities. We can provide also integrations with existent IoT Platforms leveraging four basic system features:

  • Edge APIs: local APIs from where you can retrive local KPIs calculations and push alerts based on the single device “context” observation
  • Device Management: device registry and device management tools
  • Dashboard Device composer: to compose single device or single entitities tracking views and monitor a specific context
  • Advanced BI: for more complex KPIs calculations that must be performed over the network of devices also with real-time requirements

FEATURES