EasyControl on AWS
Bring knowledge, device actions and operational data into the same management workflow. EasyControl combines its device management platform with AWS services to help teams support distributed fleets and carry out controlled AI-assisted operations.
Logistics, restaurants, retail and fitness equipment bring different device challenges. These anonymized customer examples show how teams manage distributed devices, applications and remote support.
Customer identities are withheld. These device management examples are not evidence of a customer's AWS or AI deployment.
Operators need clear instructions, support teams need context, and administrators need control over what reaches a device. The solution connects AI assistance to existing MDM workflows instead of creating a separate device control path.
Explore AI and automationRetrieve relevant product manuals and FAQs before generating an answer. Maintain the knowledge base as device models, operating procedures and common support questions change.
Turn an operational request into device management steps using the command library. MDM Service checks the proposed commands and applicable policies before sending instructions to managed devices.
Generate queries and reports from a user's request. Tenant and user access checks define the permitted data scope before the platform retrieves business information.
Use device outcomes and support context to suggest what to check next. Failed tasks remain visible for investigation, retry planning and support escalation rather than disappearing after the first attempt.
The management service remains responsible for device commands. AI supports interpretation and task preparation, while the existing management channel carries validated instructions to endpoints.
An authenticated user asks a question, requests a report or describes a device management task in the management console.
MDM AI Service retrieves relevant knowledge and uses Amazon Bedrock with agent and tool integration to prepare a response, query or proposed task.
MDM Service applies tenant permissions, command validation and policy checks. AI-generated output does not bypass the platform's management controls.
The device receives approved instructions through its MDM channel. Execution results and operational records support follow-up and review.
Four anonymized examples, focused on device challenges, management workflows and qualitative outcomes. AWS and AI deployment details are not attributed to these customers.
Inventory and unattended devices
Distributed, unattended inventory and self-service devices made on-site maintenance difficult. EasyControl brought remote monitoring, troubleshooting, kiosk restrictions and policy delivery into one management workflow.
Teams can locate device issues remotely and distribute system and application updates without arranging a technician visit for every software change.
POS and self-service terminals
POS, kiosk and other store devices needed consistent applications, visible status and timely support. EasyControl combined fleet monitoring with remote access, application delivery and centralized policies.
Operations teams gained a clearer view of store endpoints and a remote path to investigate faults, helping reduce dependence on manual checks at each location.
Mixed iOS and Android store fleets
Front-of-store iOS devices and back-of-store Android PDAs needed consistent configuration and frequent application updates. EasyControl brought both platforms into one console, with shared Wi-Fi and security policies and scheduled application delivery outside business hours.
Teams can coordinate application versions across stores and bring new locations and device models into the same management process.
Android-screen training equipment
Distributed fitness equipment needed remote maintenance, controlled application use and coordinated updates. EasyControl combined multi-app kiosk restrictions, remote troubleshooting, messages and OTA system and application delivery.
Teams can keep devices focused on training applications and handle maintenance and software updates remotely.
Useful AI needs clear boundaries. Knowledge access, data queries and device commands must follow the same management responsibilities as the rest of your fleet.
Explore security and trustCheck the user's permitted data and management scope before processing queries or device actions.
Use the device command library, allowlists and MDM policy checks to constrain what the platform can send.
Keep operational logs and device outcomes available for support, investigation and audit review.
Update manuals, FAQs and prompt versions using support feedback, human review and observed operation results.
The solution builds AI assistance around existing MDM service and device communication paths. Your device types, integration needs and deployment boundaries should be reviewed before a rollout.
AI prepares proposed tasks. MDM Service remains responsible for permission checks, command validation, policy enforcement and device delivery.
Product manuals, FAQs and support learnings are maintained as source material. Updates and human review help the system reflect current operating procedures.
No. Model selection, enabled tools, data location and integration scope depend on the customer's requirements. Confirm the actual service and deployment scope during solution design.
Bring your device and OS inventory, rollout regions, hosting requirements, identity and data access rules, and the support or reporting workflows you want to improve.
Explore Marketplace options or discuss your deployment with our team.
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