Copilot for Connected Products and Machines: 12-Wk Proof of Concept
The Copilot for Connected Products and Machines of Cluster Reply, a company of the Reply Group, addresses market challenges by enabling faster, data-driven decision-making across various company roles. It offers personalized customer service, streamlined technical support, and enhanced product insights through a unified data layer and advanced GenAI capabilities. This solution empowers businesses to improve customer satisfaction, reduce service costs, and drive product innovation in the competitive connected products market.
Market challenges solved:
In the challenging market of connected products and machines, the call for personalized and proactive customer services is becoming of prime importance along with the capacity of generating insights from collected data to drive competitive advantage.
Cluster Reply’s copilot for connected products and machines enables faster business decision-making by gaining access to accurate information swiftly, even without in-depth knowledge of the systems they reside on or waiting on other departments or external suppliers.
Key benefits:
In response to the high demand for personalized and proactive customer service, Cluster Reply offers a range of innovative solutions, impacting every company role engaged with the connected product or machine:
- Customer service obtain enhanced and personalized interactions based on data and insights, providing immediate and accurate solutions, elevating customer satisfaction and trust by reducing response time and shifting resources dedicated to customer support to value-added mansions. Our copilot can also empower traditional chatbot providing autonomous dedicated support to customers.
- Field and technical services and R&D gain immediate and automated access to knowledge in sparse documentation and data in fragmented company systems, which allows service workers to get the information they need in a more accurate and timely way, without engaging colleagues and suppliers. These lead to lower stress on the personnel and higher satisfaction for available tools, reduced manhours per ticket and service costs.
- Sales and marketing team become able to enhance product sales performance through detailed usage data analysis. By delving into customer usage patterns and analyzing trends, businesses sales users can proactively anticipate market shifts creating upsell and cross-sell opportunities.
- Product managers and R&D can pinpoint what drives product success and identify areas for improvement starting from telemetry data. This insightful approach ensures that features resonating with customers are prioritized, anticipating customer needs, driving product innovation and market competitiveness.
Cluster Reply Copilot for Connected Products and Machines
Our Copilot accelerates the implementation of complex Generative AI architectures, being able to access knowledge, but also to integrate multiple data sources, providing complex reasoning and acting over target business and IT systems.
Our solution accelerator provides:
- Unified Data Layer: Utilizing data lakes (e.g., Microsoft Fabric, Databricks), direct API integrations (e.g. CRM, ERP, etc), document management (e.g. SharePoint) and Azure IoT Hub to capture and analyze device data to the cloud, it organizes a comprehensive knowledge by indexing documents. It can also perform action over external systems impersonating the user.
- Copilot Core: Combines Azure AI Search for improved search capabilities in large volumes of structured and unstructured data with Azure OpenAI to analyze telemetry data, technical and commercial documents, images, and videos, generating insights and instructions. User feedback is collected for system improvement.
- Conversational interfaces allow users to interact with the Copilot through any touchpoint, from classic ones such as web and mobile apps delivered by Azure App Service and Microsoft Power Apps to chatbot and speech to text interaction in mobile devices, Microsoft Teams and Microsoft HoloLens.
What’s included
Proof of Concept timeline:
- New services identification (1-2 weeks, optional): Workshops with Product Owners and IT stakeholders to identify most valuable use cases and select the prioritized one.
- New service discovery (1-2 weeks): analyze selected Added Value Service in detail, available data, expected customer and company benefits and metrics for success.
- Implementation (6-8 weeks): environment preparation and Added Value Service implementation.
- Testing and tuning (1-2 weeks): Added Value Service testing and fine tuning of expected results.
- Use case(s) scaling (following weeks): roadmap for Generative AI scaling in connected machines and products and Equipment As A Service business model.