NimbleEdge HOME
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About
NimbleEdge is an AI platform that delivers real-time personalized experiences on-device, addressing the limitations of cloud infrastructure for large-scale AI applications. It offers a suite of tools including on-device GenAI models, continuous deployment, event ingestion, model execution, and monitoring. The platform aims to improve user experience and boost revenue, reduce cloud costs, handle traffic surges, and enhance user privacy by performing computations and training locally on user devices. NimbleEdge supports various industries including e-commerce, gaming, and media & entertainment, providing GenAI-powered features like search, copilot, and recommendations. Their HOME platform includes an edge data warehouse, inference engine, federated learning, and a feature store for real-time inferencing.
Platform
Features
• on-device ai models
• advanced monitoring and analytics
• edge feature store & data orchestration plugins
• edge federated learning
• edge inference engine
• edge data warehouse and processor
• session-aware event ingestion
• continuous deployment and modeling
FAQs
What is the product and what does engagement look like?
NE HOME is a PaaS for deploying and managing your ML to the SDK, which has been integrated into your mobile app on the edge.
How is the NE subscription priced?
NE PaaS is a consumption-based pricing plan, based on the number of API requests made to the NE SDK embedded in your mobile app. It is not based on data rate, user interaction or user activity or other data points required by the model to generate a result. Standard and Premium packages are available. Standard includes the SDK for inference and NE Cloud service. Premium includes Standard level features plus SDK training and additional NE consulting services. Pricing begins at 1000 requests to the SDK API.
Describe the first 90 days of interacting with NE as a team?
There will be three primary phases: * Providing your team access to the NE Cloud and SDK. * The NE engineering team partners with your ML and App teams to integrate your model of choice into the SDK. Which includes any training required because of new data points the mobile device has which previously was not sent to the cloud using the NE Simulator. * Deploy the new NE enabled app to your customer base following your SDLC defined release process and schedule.
Our data science team has an existing portfolio of models currently in-use. Are we limited to models that NE produces or will the NE HOME product support a combination of both?
Both. NE has a data science team available to collaborate with your data science and app experts. However, once your first model is deployed to the edge, your data scientist and app experts will have the baseline knowledge to deploy additional models using the holistic NE HOME platform – resulting in a continuous cycle of improved customer experiences.
The risk and cost of storing PII in the cloud is well understood. How does NE reduce both privacy risk and cost?
Very simply, we safeguard data which could be identified as PII, which the model requires in the device by not sending it to the central cloud. This allows you to take advantage of existing Android and IOS data protection and security your app has already factored. The NE SDK enabled model will be able to make hyper-personalized recommendations, which rely on PII in the device – avoiding the cloud, risk, and unnecessary costs.
Will my CISO and team be concerned that existing security policies will be impacted?
Since the SDK will be embedded into your app, the existing security measures implemented by the app team should not be impacted. If your compliance and risk teams identify a vulnerability in the NE SDK, the NE team will address the finding. Since less PII data is now being sent to the cloud, your security posture is improved. Regarding cloud security, NE only uses native public cloud service and security measures.
How will I leverage NE Federated Learning into our modeling process?
Now that you have a single model deployed across all apps in-use by your customer’s model performance, not data, is collected across the deployed customer base. NE Federated learning will take this performance data, modify the model and deploy when you are ready. Bottom line, the user experience of many enables continual user experience for all. Major benefit is 1-1 modeling per user in the millions of users.
Pricing Plans
Premium
USD1000.00 / per request• SDK for inference
• NimbleEdge Cloud service
• SDK training
• NimbleEdge consulting services
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