For Apps with 1M+ Daily Active Users

On-device ML platform for real-time personalization in mobile apps

Infrastructure layer for ML teams to leverage compute from end-user devices, and deliver real-time personalization that delights users and boosts conversion

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Deliver real-time personalization without worrying about Cloud costs

Cloud costs
User privacy
Latency
Scalability
Slash cloud costs by >50%

Reduce cloud costs for real-time ML by 50%+, with data processing and inference executed end-to-end on users' mobile devices

Privacy-preserving by design

Enhance privacy posture by minimizing user data sent to cloud servers with on-device ML

Ensure minimal latency (<50ms)

Deliver rapid, truly real-time predictions (<50ms end-to-end latency) with ML inference execution on user smartphones

Effortless user traffic scaling

Handle rapid traffic surges with zero incremental operational complexity using on-device machine learning

Use Cases

Unlock the power of on-device, real-time personalization in your mobile apps

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E-Commerce
E-COMMERCE
Read your users' mind with real-time personalized recommendations, search results, and ads

Boost conversion and average order value by delivering tailored user experiences, which adapt in real-time based on in-session user behavior

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Nimble Edge Use Cases Graphical Representation
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Gaming
Gaming
Elevate user experience with real-time personalized recommendations and robust cheating detection

Improve gamer engagement and cut dropoff with game experiences personalized in real-time to incorporate in-session user behavior

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Nimble Edge Use Cases Graphical Representation
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MEDIA & Entertainment
MEDIA & Entertainment
Deliver engaging user experiences with session-aware feeds, search, and instant content moderation

Increase engagement by serving highly relevant content that rapidly adapts to in-session user interactions (e.g. clicks, likes)

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Nimble Edge Use Cases Graphical Representation

Manage both on-device ML execution and orchestration with NimbleEdge platform

Fully managed on-device ML execution
Edge pipelines with on-the-fly updates
Seamless over-the-air model updates
Unlimited ML inference per user
Learn more about NimbleEdge platform
red sun

Leverage real-time ML to deliver hyper-personalization for performance uplift — without the cloud cost burden and privacy risk.

The X-Factor Your Business Needs

ROI & SCALE

The ROI & Scale are always on the rise, all while there is a continued reduction in Operational Complexities, Privacy Risk, Fraud, API Calls etc.As a result, the gap only widens with NimbleEdge’s X Factor.

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Nimble Edge Home Platform

Maximize Your Gains With NimbleEdge

Infinite Scalability,
Made Simple

With edge new customers does not increase cloud costs because compute cycles and training execution happens locally. This simplifies scaling your infrastructure by 2-3X with predictability in cost and infinitely scalable deployments.

Unparalleled
Hyper-Personalization

Federated Learning (FL) gives each customer their own unique individualized Machine Learning (ML) model which makes real time, more accurate and more relevant recommendations.

While Reducing...

Privacy
Compliance Costs

Smart phones have the computing horsepower to execute and train Machine Learning (ML) models on the devices themselves, providing relevant recommendations without sharing the PII with cloud. Save up-to 80% of your compliance cost with privacy-preserving algorithms.

Spiraling
Cloud Costs

Utilizing the edge means less frequent updating of the central model, fewer compute cloud cycles, less data storage and less data transfer.

Achieving real-time ML at scale is made possible by harnessing the power of the Intelligent Edge.

Leverage the Intelligent Edge with the HOME Platform

Edge Data Warehouse and Processor

On-device managed database and query engine providing real-time persistent information at latencies of <1 millisecond.

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Edge Inference Engine

Fully compatible with existing ML models (PyTorch, Tensorflow, LightGBM, XGBoost, ONNX, and Numpy) – no more rewriting models for the edge.

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Edge Federated Learning

Privacy-preserving on-device training suite, enabling enterprises to train 100 Million+ individualized ML models across 100 Million+ user devices.

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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.

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Unlock real-time personalization with NimbleEdge on-device ML Platform

On-device Data Warehouse

Managed on-device database and query engine to capture persistent raw data like user inputs in real-time (e.g. in-session clicks, purchases, likes)

On-device Data Processing

Python-like scripting engine to easily define on-device feature computation (e.g. rolling-window aggregates) from real-time user interactions

Feature-store Syncing

Continuously cloud-synced, in-memory data replica to incorporate relevant global features stored in cloud during execution of on-device inference

On-device ML Inference

Rapid, low-resource usage inference execution at scale for thousands of device models, with no need to rewrite ML models for on-device deployment

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FAQs

What is the product and what does engagement look like?

How is the NimbleEdge subscription priced?

Describe the first 90 days of interacting with NimbleEdge as a team?

Our data science team has an existing portfolio of models currently in-use. Are we limited to models that NimbleEdge produces or will the NimbleEdge HOME product support a combination of both?

The risk and cost of storing PII in the cloud is well understood. How does NimbleEdge reduce both privacy risk and cost?

Will my CISO and team be concerned that existing security policies will be impacted? 

How will I leverage NimbleEdge Federated Learning into our modeling process?

Data privacy and security

NimbleEdge is SOC2 Type-2 and ISO 27001:2022 certified, and user data privacy and security is a massive priority for us

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