On-device ML platform for real-time personalization in
mobile apps
Delight users and boost conversion with real-time personalized experiences, using machine learning executed on users' mobile devices
Deliver real-time personalization without worrying about Cloud costs
Reduce cloud costs for real-time machine learning by >50%, with machine learning executed end-to-end on users' mobile devices
Enhance privacy posture by minimizing user data sent to cloud servers with on-device machine learning
Deliver rapid real-time predictions (<50ms end-to-end latency) with ML inference execution on user smartphones
Handle rapid traffic surges with zero incremental operational complexity using on-device machine learning
Unlock the power of on-device, real-time personalization in your mobile apps
Manage both on-device ML orchestration and execution with NimbleEdge platform
Create the Intelligent Edge with the NimbleEdge Platform
Edge Data Warehouse and Processor
On-device managed database and query engine providing real-time persistent information at latencies of <1 millisecond.
Edge Inference Engine
Fully compatible with existing ML models written in PyTorch, Tensorflow, LightGBM, XGBoost, ONNX, and Numpy. No more rewriting models for the edge.
Edge Federated Learning
Privacy-preserving on-device training suite, enabling enterprises to train 100 Million+ individualized Machine Learning (ML) models across 100 Million+ user devices.
Edge Feature Store & Data Orchestration Plugins
A continuously cloud-synced, in-memory processed data replica that precomputes features <80 microseconds – 1000x faster than central cloud feature store, for real-time inferencing.