AI at the edge.
Ready for deployment.
ScribblPad Edge brings local AI models, inference runtimes and AI applications into one environment for deploying intelligence on edge servers, gateways and on-premises infrastructure.
Run AI where your data lives. Deploy close to your users, devices and operations.
Designed for local inference, private deployments and heterogeneous edge computing.
Edge hardware has arrived. Making it useful is the challenge.
Edge devices can collect and process enormous amounts of data. But deploying AI on them often means assembling a stack of hardware drivers, inference runtimes, models, dependencies and application services.
Without a unified software layer
- Different hardware needs different configurations.
- Model compatibility and optimisation become engineering projects.
- Deployments are hard to reproduce across devices.
- Monitoring and maintaining multiple installations adds operational overhead.
With ScribblPad Edge
- A consistent environment for deploying supported AI workloads.
- Model and runtime configuration suited to supported hardware.
- Local inference services that applications can call.
- A foundation for repeatable deployments across compatible systems.
One platform. From edge hardware to AI applications.
Five capabilities, stacked from silicon to service.
Management
Configure, monitor and maintain deployments.
Services
Expose deployed models through application interfaces and APIs.
Runtime
Execute inference using compatible runtimes and hardware.
Models
Manage supported models and deployment configurations.
Hardware
Identify available compute resources and supported accelerators.
Deploy AI where it matters.
Industrial AI
Run supported inference workloads near production equipment, processing inspection images, sensor data and operational information locally.
Private enterprise AI
Deploy local language models and inference services on compatible on-premises infrastructure for internal applications and knowledge workflows.
Retail and branch deployments
Explore local processing for video analytics, document workflows and other AI applications across distributed locations.
Edge gateways
Bring inference closer to connected devices and data sources, reducing dependence on continuous cloud connectivity for supported workloads.
These are target use cases to validate, not claims that every workload is currently supported.
Keep processing close to your data.
Data control
Keep processing within your chosen infrastructure, subject to your application’s data flows.
Lower network dependence
Avoid sending every inference request to a remote service.
Responsive applications
Reduce network round trips where local inference is suitable.
Predictable infrastructure
Use dedicated compute for workloads that run repeatedly.
Deployment flexibility
Choose infrastructure by performance, power, cost and operational needs.
Cloud AI is a service. Edge AI brings intelligence into your infrastructure.
Built for heterogeneous compute.
AI infrastructure won’t be limited to a single chip or device category. ScribblPad Edge aims to provide a consistent software experience across supported CPU and accelerator configurations, with an architecture that can evolve as hardware requirements change.
Dashed outlines mark potential platform support still to be validated. Additional hardware will be listed only after it has been tested.
From a single edge device to a fleet.
Develop
Configure and test an AI workload on a supported system.
Deploy
Package the model, runtime and application dependencies.
Integrate
Connect the inference service to your application or data pipeline.
Operate
Monitor performance, logs and resource use.
Expand
Replicate the validated deployment across compatible systems.
Remote fleet management, automated updates and central orchestration are planned capabilities.
Built for the people deploying AI.
Edge AI developers
Build and deploy inference applications.
System integrators
Deliver repeatable AI deployments for customers.
Industrial technology providers
Integrate AI into existing equipment and workflows.
Enterprise IT teams
Run selected AI workloads within controlled infrastructure.
Hardware manufacturers
Offer a ready-to-configure software environment alongside edge hardware.
Bring AI closer to your data.
From a single edge system to a wider deployment, ScribblPad Edge aims to simplify the software stack behind local AI. Build with us. Deploy with confidence.