On-device Real-time AI platform for
Media & Entertainment
Boost user engagement with truly real-time personalized search, copilot and more.
Avoid breaking the bank on cloud costs with on-device AI.
NimbleEdge executes real-time ML pipelines on users' mobile devices, from in-session data capture to ML inference
This unlocks rapid, cost-effective and privacy-preserving ML for a wide variety of session-aware use-cases for media and entertainment apps, including personalized feeds, search, recommendations and more



GenAI Augmented Real-Time Personalized Search
Supercharge content discovery with GenAI driven personalized search that truly understands users' real-time intent
upto 20% improvement in search ranking metrics
GenAI based In-App Co-Pilot
Delight customers with an in-app AI assistant with full in-session context, capable of real-time responses
upto 10% increase in title page views


Real-time Personalized Recommendations
Serve highly personalized recommendations with real-time user interactions fully baked in (e.g. likes, search queries)
6-10% improvement in ranking metrics
Real-time Personalized Feed
Deliver tailored feeds that respond to in-session user interactions (e.g. likes, search queries) in real-time
8-10% increase in user likes

Real-time on-device content moderation
Use on-device machine learning to deliver rapid, cost-efficient content moderation
<100ms end-to-end latency
>50% lower costs vs. cloud

Streamline the complete AI lifecycle with NimbleEdge platform

GenAI Models


Event Ingestion

Unleash the power of personalized, real-time AI on device
Truly real-time, privacy-preserving personalization at a fraction of the cost on cloud
Massive model performance improvement (>5%)
Incorporate real-time user behavior in your ML models, and instantly improve model accuracy

Enormous cloud cost savings (>50%)
Slash cloud costs by eliminating the need for cloud resources for real-time data processing and ML inference

Minimal end-to-end latency (<50ms)
Incorporate user inputs in ML systems instantly, going from event capture to inference in milliseconds

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