On-device ML platform for real-time personalization in
mobile apps
Delight users and boost conversion with real-time machine learning that runs on users' mobile devices
Create session-aware personalized experiences without breaking the bank on cloud infra costs
Deliver Hyper-Personalization without worrying about Scalability
Factoring in the impact to market cap in addition to near term savings, scaling companies can justify nearly any level of work that will help keep cloud costs low.
data protection
Hyper-personalization does not need to come at the risk of collecting and exposing sensitive consumer data. Keeping real-time ML processes at the edge safeguards PII.
speed drive
Leveraging the edge enables high volumes of real-time hyper-personalized recommendations without incurring latency problems.
Unlock the power of on-device, real-time personalization in your mobile apps
NimbleEdge on-device ML platform provides both orchestration and execution capabilities for real-time personalization
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.