Foundation Models: Unlocking Efficiency with AI using Microsoft Fabric Shiva Kumar HP January 2, 2025

Foundation Models: Unlocking Efficiency with AI using Microsoft Fabric

Introduction

In the fast-paced business environment, effective communication is essential for ensuring smooth operations. Whether it’s handling order modifications, tracking shipments, or addressing maintenance requests, email remains a vital channel for information exchange. However, managing the sheer volume and variety of emails can overwhelm teams, resulting in delays, errors, and inefficiencies in managing critical tasks.

The answer lies in harnessing Artificial Intelligence (AI) and Natural Language Processing (NLP) to automate email processing. By leveraging foundation models—advanced, pre-trained AI systems renowned for their language understanding capabilities—organizations can revolutionize their email workflows. These models can intelligently classify emails, extract key details such as order IDs, delivery dates, and priorities, and integrate effortlessly with existing systems. This enables faster, more accurate responses, driving efficiency and precision in decision-making processes.

The Challenge: Managing Service Requests Efficiently

Businesses often face challenges such as:

  • High volume of email communication.
  • Manual classification and resolution of service requests or order-related incidents.
  • Inefficient resource utilization, causing delays in response time.
  • Limited scalability of traditional systems.

Addressing these pain points requires an intelligent system that can classify emails, extract relevant details, and automate actions based on the extracted information.

What Are Foundation Models?

Foundation models are large, pre-trained AI models designed to understand, generate, and predict across multiple domains and tasks.

They are trained on diverse datasets and can be adapted for specific applications through fine-tuning. These models form the basis for many modern AI applications, including natural language processing, computer vision, and more.

The Significance of Foundation Models

Foundation models are pivotal in transforming how organizations leverage AI. Their scalability, efficiency, and versatility make them a cornerstone for modern AI-driven solutions.

  1. Scalability: One model can handle diverse applications, ranging from text analysis to image recognition.
  2. Efficiency: Organizations save time and costs by fine-tuning pre-trained models rather than training from scratch.
  3. Versatility: Foundation models are adaptable across industries such as healthcare, finance, and logistics.
  4. Cross-Domain Learning: They leverage vast datasets to perform well even on tasks outside their training domain.

How a client used AI in Email Management for Manufacturing with Microsoft Fabric to boost efficiency and drive results

The customer previously relied on a rule-based incident management system to classify incoming order emails. This approach required significant human intervention, making the process labor-intensive and prone to inconsistencies. Maintaining the system demanded continuous effort to ensure accuracy and operational efficiency.

Acuvate solved this challenge by leveraging AI-powered Foundation Models to automate email classification, streamlining the process and minimizing manual dependencies.

Microsoft Fabric is a unified data and analytics platform that simplifies the end-to-end implementation of foundation models. Its integrated ecosystem offers tools for data ingestion, transformation, model fine-tuning, and deployment, all within a single platform.

Architecture:

This architecture shows how emails flow from the Oracle Service Cloud into Microsoft Fabric for ingestion, transformation, and storage. Azure OpenAI processes and classifies the data while ensuring security and governance with Azure tools.

Process Flow:

The workflow highlights how Azure OpenAI extracts key details, updates incidents, and generates customer responses. APIs integrate seamlessly for real-time updates like shipment details and delivery schedules.

By combining Microsoft Fabric and Azure OpenAI, this solution offers a cutting-edge system for managing service requests and maintaining order data. Let’s break it down:

1. Incident and Information Management

Incoming emails are processed through an Incident Management System such as Oracle Service Cloud. These emails contain various types of requests like order updates, delivery schedule changes, and incident reports.

2. AI-Powered Classification and Information Extraction

Using Azure OpenAI, the system:

  • Classifies emails into categories (e.g., service requests, order modifications).
  • Extracts critical information such as order ID, request type, delivery date, and customer details.
  • Automates updates to incident logs or service records.

3. Data Ingestion and Transformation with Microsoft Fabric

The data flows through Microsoft Fabric’s ingestion pipelines and notebooks for further transformation. Key components include:

  • Pipelines: Automating data ingestion and transformation processes.
  • Notebooks: For custom logic and fine-tuning classification or extraction processes.
  • Data Activator: Triggering real-time actions based on incoming data.

4. Response Generation and Incident Resolution

Once the data is processed:

  • GPT Models (Azure OpenAI) generate templated responses, ensuring timely and accurate communication with customers.
  • APIs update units, fetch shipment details, and calculate delivery slot availability.
  • A new response is created and sent to the user while updating the incident records.

Process Flow Overview

1. Reading Incidents from Oracle Service Cloud

  • Input: Emails containing customer queries, service requests, or incident details are received and stored in Oracle Service Cloud.
  • Processing: The system reads the emails and extracts the content for preprocessing.
  • Purpose: This step ensures seamless integration with the incident management system and acts as the entry point for all customer communications.

2. Preprocessing of Email

  • Function: Emails are cleaned and pre-processed to remove irrelevant information such as signatures, formatting artifacts, and noise.
  • Tech: Basic text cleaning techniques or NLP preprocessing tools (e.g., tokenization, stemming, and removal of stop words).
  • Outcome: Produces a clean, structured text representation of the email for further analysis.

3. Classification of Email and Incident Update

  • Using Azure OpenAI:
    • Emails are classified into predefined categories such as order modification, service request, or general inquiry.
    • The classification helps determine the appropriate next steps.
  • Incident Update:
    • The system automatically updates the incident record in Oracle Service Cloud with the classification and related metadata.
  • Purpose: Speeds up incident handling by eliminating manual categorization.

4. Prompt for Service Request or Order Modification

  • Prompt Design:
    • Azure OpenAI is used to prompt the system for the next action based on the email’s context. For example:
      • “What order ID is being referenced?”
      • “What is the requested modification or delivery change?”
  • Purpose: Guides the system to extract relevant details and proceed with subsequent actions.

5. Information Extraction

  • Key Details Extracted:
    • Order Information: Order ID, units requested, delivery date.
    • Customer Details: Name, transaction ID, and shipment-specific IDs.
    • Request Type: The specific service request type (e.g., delivery rescheduling, additional units).
  • Tech:
    • Azure OpenAI’s natural language processing capabilities identify and extract the required fields.
    • APIs fetch shipment or customer data to enrich the extracted information.
  • Outcome: Structured data that can be used to fulfil the request or modify the order.

6. API Integration for Data Retrieval and Updates

  • Fetching Data:
    • APIs connect to backend systems for:
      • Retrieving shipment details (e.g., shipment ID, customer name, sales order).
      • Checking delivery slot availability based on requested changes.
    • Example: If the user requests order modification, the system checks real-time availability and updates the order and checks delivery slot availability.
  • Updating Systems:
    • Order and incident updates are sent back to Oracle Service Cloud via APIs.
    • New shipment or modification details are logged in the system for traceability.

7. Response Generation

  • Automated Response Creation:
    • Azure OpenAI generates a response based on:
      • Templates for response formatting.
      • User-provided context (e.g., changes in delivery slots, order confirmation).
      • Real-time data fetched from APIs.
  • Example Response:
    • “Dear [Customer Name], your order ID [12345] has been successfully updated. The revised delivery date is [MM/DD/YYYY]. Please contact us for further assistance.”
  • Purpose: Ensures clear, consistent, and prompt communication with customers.

8. Creating a New Thread in the Incident Management System

  • Action: The system creates a new thread within the corresponding incident in Oracle Service Cloud, ensuring that all communication is logged.
  • Outcome: Maintains a complete communication history for tracking and audit purposes.

9. Sending Email to the Customer

  • Automated Dispatch:
    • The generated response is sent back to the customer via email.
    • This ensures the customer is promptly informed of the request status or resolution.
  • Tools: Email service providers integrated with Oracle Service Cloud or Microsoft Fabric.

10. Logging and Monitoring

  • Incident Update: The system records the final response and any updates in Oracle Service Cloud.
  • Monitoring:
    • Tools like Azure Monitor and Application Insights track email volume, classification accuracy, and response time.
    • Alerts and logs help ensure smooth operation and identify bottlenecks.

Technical Highlights of the Flow

Azure OpenAI

Powers classification, information extraction, and response generation.

APIs

Enable real-time communication between systems for shipment details, delivery slots, and incident updates.

Microsoft Fabric

Orchestrates data flow, transformation, and secure storage.

Governance

Azure Key Vault and Active Directory ensure security and compliance with organizational policies.

This structured flow ensures a seamless experience for customers and efficient handling of service requests, significantly reducing manual intervention and operational overheads.

Key Features of the Solution

Foundation Models for Intelligence

Foundation Models, like GPT, play a pivotal role in email classification, information extraction, and response generation. They ensure:

  • Contextual understanding of email content.
  • Accurate extraction of structured information.
  • Seamless integration with APIs for dynamic responses.

Centralized Data Management with Microsoft Fabric

Fabric provides a unified platform with components like OneLake and Azure SQL for storing and managing order and incident data. Security is ensured with Active Directory, Azure Key Vault, and App Insights.

Scalability and Automation

Microsoft Fabric pipelines ensure that the system scales with the growing volume of emails and service requests. Data transformation and response generation happen in real-time, reducing manual intervention.

Business Impact

By implementing this AI-powered email management solution, manufacturers can:

  1. Reduce Manual Workload: Automating email classification and response generation.
  2. Enhance Customer Experience: Faster response times and accurate updates.
  3. Improve Operational Efficiency: Seamless integration with incident management systems and scalable data pipelines.
  4. Unlock Insights: With Azure Monitor and App Insights, gain actionable insights into email patterns and incident trends.

Future Trends in Foundation Models

As foundation models continue to evolve, several trends are shaping their future:

  1. Edge Deployment: Optimizing models for edge devices to reduce latency.
  2. Multi-Language Support: Enhancing adaptability across languages and cultures.
  3. Energy Efficiency: Developing lightweight models for reduced computational costs.
  4. Self-Supervised Learning: Minimizing dependency on labeled datasets.

Foundation models represent the pinnacle of scalable AI, offering immense opportunities for innovation across industries. Platforms like Microsoft Fabric enable organizations to seamlessly deploy and leverage these models, ensuring efficiency and adaptability. As the AI landscape evolves, embracing foundation models will be crucial for staying competitive in a rapidly advancing world.

Conclusion

Leveraging Microsoft Fabric and Foundation Models like Azure OpenAI can transform the way manufacturing companies handle email communications, service requests, and order management. This end-to-end system ensures scalability, automation, and actionable insights, paving the way for a more efficient and customer-centric future in manufacturing.

Ready to unlock the power of AI in your operations? Get started with Microsoft Fabric and Azure OpenAI today!

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AI-powered email managementAutomated workflowsBusiness efficiency with AIEmail classificationFoundation ModelsMicrosoft FabricService request automation
Shiva Kumar HP Shiva Kumar HP

System Analyst at Acuvate, brings over six years of expertise in machine learning, machine vision, generative AI, and data modernization. With a deep understanding of Microsoft Azure, he specializes in designing and implementing end-to-end solutions that drive organizational growth and innovation. By leveraging Azure’s robust resources, Shiva helps businesses unlock valuable insights, optimize costs, and streamline operations. His dual expertise in data modernization and AI technologies enables organizations to enhance their digital capabilities, improve business efficiency, and stay competitive through cutting-edge technologies in an increasingly data-driven world.

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