Connected Device Intelligence for Reliable MedTech OperationsConnected Device Intelligence for Reliable MedTech Operations

The client is a global MedTech company specializing in advanced aesthetic technologies and professional skincare. Its patented treatment devices and consumables support more than 30,000 estheticians across over 90 countries.

As a US-based, FDA-registered manufacturer of medical equipment and consumables, the client operates in a market where device uptime, supply continuity, provider confidence, and consistent treatment delivery directly influence recurring revenue and long-term customer value.

As the client’s connected device footprint expanded, support teams had to interpret telemetry, maintenance history, software versions, consumable usage, treatment context, and location activity across a growing provider network. The information existed across multiple systems, but it did not form a coordinated operational view. As signal volumes increased, manual review became slower and less consistent, making it harder to identify which events required immediate action.

Low flow, clogs, noise, delayed service, inconsistent maintenance, and stockouts disrupted treatment delivery and reduced provider confidence. The client needed a connected intelligence layer that could correlate device and operational data, identify the likely cause of an issue, recommend the next action, and route it to the appropriate provider, support team, or supply function.

Key Challenges

The client needed to improve how device issues, supply requirements, and provider support requests were identified and resolved across its expanding network.

1. Delayed Fault Detection: Device issues were often identified after they affected treatment, increasing downtime and dependence on field service teams.
2. Disconnected Replenishment Decisions: Consumable demand was not consistently linked to device usage and treatment activity, creating replenishment delays and stockout risks.
3. Limited Fleet-Level Visibility: Maintenance activity, software versions, device movement, and operating conditions were difficult to track across clinic locations.
4. Fragmented Provider and Support Workflows: Providers and support teams lacked guided diagnostics, contextual recommendations, secure remote access, and coordinated escalation workflows for resolving device and treatment-related issues.

The client has worked with Coditas for more than five years across its connected device ecosystem. During that period, Coditas developed detailed knowledge of device behavior, provider workflows, service operations, consumable demand, and reliability requirements across a global clinic network.

From the early stages of the engagement, both teams recognized that the connected device foundation would eventually need an intelligence layer as device volumes, treatment context, and service workflows expanded. Coditas combined its platform knowledge with secure cloud engineering, MedTech product experience, Multi-Agent Systems, and User-Centric Agentic Experience Design to define the next stage of the connected device ecosystem.

Coditas mapped how device issues moved across telemetry, provider reporting, service triage, maintenance, and replenishment. The assessment showed that device connectivity alone could not support the next stage of operations. Telemetry, treatment context, service history, and inventory signals needed to be interpreted together before teams could act.

The team created a connected operational foundation using digital twins, secure remote access, role-based controls, and shared dashboards. Rules and predictive models surfaced anomalies, estimated consumable demand, and prioritized devices requiring attention.

Coditas then structured the intelligence layer around specialized agents. A Device Health Agent interpreted telemetry, fault patterns, and maintenance history to recommend diagnostic steps. A Supply Intelligence Agent proposed replenishment actions using device activity, treatment volumes, inventory levels, and lead times. A Provider Guidance Agent used Amazon Nova Pro and retrieval through Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless to support camera-assisted skin assessment, treatment planning, session summaries, and post-treatment recommendations.

Amazon Bedrock AgentCore routed requests and governed access to device, service, inventory, and ticketing tools. Amazon Bedrock Guardrails constrained unsupported responses, while AWS Step Functions enforced review points before treatment, service, or replenishment actions proceeded. The platform recorded each recommendation, its supporting evidence, the reviewer's response, and the final action. Quality checks measured retrieval relevance, diagnostic agreement, agent routing, and user acceptance or override patterns.

Technologies

Application and Experience Layer: Java, Flutter, and React

Connected Device and Data Layer: AWS IoT Core, Amazon Kinesis, Amazon S3, AWS Lambda, AWS Glue, and Amazon Athena

AI and Orchestration Layer: Amazon Nova Pro, Amazon Bedrock Knowledge Bases, Amazon OpenSearch Serverless, Amazon Bedrock AgentCore Runtime, Amazon Bedrock AgentCore Gateway, Amazon Bedrock Guardrails, Amazon SageMaker AI, AWS Step Functions, Amazon EventBridge, and Amazon CloudWatch

Analytics and Data Science: Power BI, Python, and R

The connected platform helped the client detect device issues earlier, reduce dependence on on-site service, align consumable replenishment with actual usage, and improve the provider experience across its global network.

• 30% increase in revenue from the client’s IoT-enabled device line during the first 12 months after rollout compared with the preceding 12 months.
• >25% increase in supply sales through usage-based demand forecasting and replenishment triggers during the first 12 months after rollout compared with the preceding 12 months.
• >60% reduction in on-site field service visits per 1,000 connected devices through remote diagnostics and device management during the first 12 months after rollout compared with the preceding 12 months.
• 20% reduction in average device downtime per service incident through earlier issue detection, guided diagnostics, and faster intervention during the first 12 months after rollout compared with the preceding 12 months.
• 25% reduction in average service and device management cost per connected device through remote service workflows and a scalable cloud data foundation during the first 12 months after rollout compared with the preceding 12 months.
• Provider satisfaction increased from 3.5 to 4.8 (on a 5-point scale), based on matched baseline and 12-month follow-up surveys completed by 250 providers.

“Coditas has been a dependable partner, adapting to our needs with professionalism, deep expertise, and the commitment required to meet critical deadlines.”

— Senior Director, Digital Products, Global MedTech Aesthetics Company

The engagement changed how the client managed a growing connected device business. Device reliability, service planning, consumable availability, and provider support could be coordinated through one operating model instead of separate reactive processes.

The platform created a scalable foundation for expanding intelligent workflows across device operations while preserving clear ownership over treatment, service, and replenishment decisions.