NimbleEdge utilizes cutting edge technology to enable
Hyper- Personalization

NimbleEdge integrates edge computing, federated learning (FL) and differential privacy with its cloud-based PaaS to offer companies an end-to-end solution for hyper-personalization driven by real-time ML.

Reduce Cloud dependency

Take advantage of the compute power of edge devices like smartphones

NimbleEdge’s technology swings the pendulum back from the centralization of compute in the cloud to take advantage of the compute power of edge devices like smartphones. Before NimbleEdge, executing real-time AI/L pipelines required complex, data intensive processing and network intensive communications  resulting in exorbitant cloud costs.


Edge Computing

Edge computing is a computational architecture that locates data processing and storage on edge devices like today’s smartphones.
There is a constant upgrade in capabilities of edge devices i.e. smartphones. Yet, many apps still rely on the cloud application architectures rather than leveraging the computational power of the phone.

tech edge computing
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~1000x Faster

Today’s smartphones are almost a thousand times faster than the mid-’80s Cray-2 Supercomputer, several multiple times faster than the computer onboard NASA’s Perseverance Rover currently exploring Mars and - perhaps most significantly - faster than the laptops most of us are carrying around today.

edge computing

Operational complexity is simplified by intelligently balancing the ML workloads

Distributing the workload of data handling to the device enables real-time hyper-personalized ML engagement because the ML pipelines are executing locally. Latency is reduced to human imperceptible levels. Network activity is minimized. User privacy is protected. PII is not transmitted to the cloud. Operational complexity is simplified by intelligently balancing the ML workloads - inference and training - between the NimbleEdge cloud service and the edge device.

Federated Learning

Federated Learning (FL), also called collaborative learning, is an innovative ML technique that enables the training of AI models on user devices without requiring the user device to share its local data. Essentially, each user has their own personally trained model - a million models for a million users - that uses a vast pool of heterogeneous data available at the edge.

federated learning

Federated Learning offers better generalization patterns across different models resulting in more relevant results.

The NimbleEdge service aggregates the locally trained models and excludes any PII data from being transmitted. The central server combines local models to create an updated global model that is sent back to the edge, enabling machine learning without sharing data while ensuring privacy. Federated Learning offers better generalization patterns across different models resulting in more relevant results.

fedreated learning

Differential Privacy

Differential privacy is a rigorous mathematical definition of privacy. An algorithm is differentially private if from its output, you cannot discern if an individual’s data was or was not part of the original dataset.

By adding “random noise” to a dataset, a differentially private algorithm can publicly share information about the dataset by describing patterns of groups in the dataset while withholding information about individuals.

Differential Privacy

No compromise on individual privacy with Differential Privacy.

NimbleEdge has implemented differential privacy to help organizations develop ML-based recommendation models that  collect and share aggregate data while maintaining individual privacy.

Use Cases

Leverage the Intelligent Edge for Your Industry


Betterment in transaction success rate through hyper-personalized fraud detection
Fraud detection models that try to flag fraudulent transactions (applies to all the FinTech apps)
Speed & Reliability issues with transactions in non-real time ML systems on the cloud limit personalization levels, as it operates with Huge Costs of running Real-Time ML systems on the Cloud
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Increase in models’ performances lead to a rise in Conversion carts with higher order size
Search & Display recommendation models for product discovery for new and repeat orders Personalized offers and pricing
The non Real-time/Batch ML processing doesn't serve highly fluctuating or impulsive customer interests. Organizations need real-time ML systems but it is impossible to implement and scale them on the cloud with even five times the average cloud cost.
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See uplifts in game retention metrics like gaming duration, completion, game cross-sells and LTV
Contest SelectionMatchmaking and Ranking Cross-contests recommendationPersonalized offers and pricing
As a result of cloud’s limited infrastructure in providing scalability with respect to ML model deployments and processing in real-time, gaming apps adopt non real-time/batch processing that negatively affects click-through rates, game duration, completion, cross-sells, and lifetime value of players.
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Savings in the privacy budget with privacy preserving encryption algorithms
Personalized Search recommendations (Exercises, Nutrition, Services, Products)
User engagement metrics, customer acquisition and retention, NPS, and other business app metrics suffer. On-device/Edge processing can be a great solution but the data processing capacity is inherently limited due to resource constraints of edge devices.
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Travel & Stay

Increase in average booking value with new and repeat customers with higher NPS & savings in cost of acquisition
Travel & Stay
Search/Service recommendation models  + Personalized offers and pricing
NimbleEdge’s HOME runs real-time ML - Inference & Training - on-device, ensuring performance uplifts in Search/Service recommendation and Personalized offers/pricing models at 1/5th of the cost to run them on the cloud.
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NimbleEdge Platform manages both orchestration and execution capabilities for on-device ML

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Edge pipelines with
on-the-fly updates
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