1-Executive Summary
Engineered an automated intelligence pipeline to convert unstructured CRM data into actionable business insights. By integrating Power Automate, Azure AI, and Power BI, this solution enables the CRM Support team to identify case drivers and recurring issues that were previously obscured by high volume and fragmented reporting.

2-The Challenge

-Visibility Deficit: The CRM Support team lacked clarity on the primary factors contributing to case volume and the specific nature of recurring support efforts.

-Inadequate Legacy Tools: Static reports and Excel-based workflows were unable to handle large data exports or analyze unstructured customer email text.

-Language & Structure Barriers: Critical customer feedback remained siloed in various languages and unstructured formats, preventing reliable downstream analysis.

3-Technical Methodology & Innovation

-Orchestrated Data Extraction: Developed a Power Automate workflow that loops through Salesforce cases in controlled batches to prevent system timeouts and allow non-technical users to adjust scope parameters.

-NLP & Translation Pipeline: Automated the cleaning, structuring, and English translation of customer emails directly within the flow using specialized AI prompts.

-Intelligent Categorization: Integrated an Azure-based AI model to assign multi-level categories to each case, uncovering deep insights into recurring patterns.

-Dynamic Intelligence Layer: Leveraged Power BI to host an interactive dashboard that visualizes urgency, trends, and volume drivers. Embedded AI capabilities provide data-driven recommendations for strategic adjustments directly within the UI.

4-Business Impact

-Real-Time Analytics: Replaced manual, hour-intensive reporting with a fully automated solution where trend analysis is available instantly via a simple refresh.

-Proactive Support Strategy: Shifted the team from reactive fire-fighting to proactive optimization, allowing for process improvements and increased customer satisfaction.

-Accessibility: Utilized low-code technologies to make advanced AI-driven data extraction accessible without the need for extensive custom development.

1-Executive Summary
Engineered an end-to-end AI diagnostic ecosystem that automates fault identification in frequency drives. By integrating Deep Learning with Cloud Automation, the project transitioned complex manual diagnostics into a scalable, remote-access solution, significantly reducing commissioning lead times and the need for physical laboratory intervention.

2-The Challenge
-Technical Complexity: Diagnostic workflows for identifying fault behavior from voltage signals traditionally required high-level expert knowledge and physical lab access.

-Scalability Constraints: Manual analysis was unable to account for the thousands of scenarios across multiple power sizes and diverse environmental conditions.

-Operational Bottlenecks: Dependence on specialized personnel led to delays in field support and commissioning.

3-Technical Methodology & Innovation
-High-Fidelity Synthetic Data: Developed a simulation framework to generate over 10,000 voltage signals, encompassing six distinct fault types across three power sizes—achieving a data scale impossible through manual collection.

-Deep Learning Classification: Trained a robust neural network on simulated signals aligned with Danfoss technical documentation to ensure precision in fault identification and corrective recommendations.

4-Architecture & Integration:

-Orchestration: Implemented a Copilot Studio workflow to trigger diagnostic sequences upon signal upload.

-Conversational Interface: Integrated a Microsoft Teams Copilot to provide a user-friendly frontend.

-Hybrid AI Strategy: Utilized Generative AI for the conversational interface while anchoring all technical guidance in the Deep Learning model to prevent hallucinations and ensure engineering accuracy.

5-Business Impact
-Accelerated Commissioning: Reduced fault identification time from hours of expert intervention to minutes of automated, remote analysis.

-Support Efficiency: Lowered the dependency on specialized diagnostic teams, allowing senior experts to focus on edge cases while the AI handles high-volume requests.

-Scalable Engineering: Created a repeatable prototype for AI-driven engineering, providing a foundation for future expansion into additional fault types and hardware configurations.

1-Executive Summary:
Developed a predictive analytics framework for the Danish football club Viborg FF to optimize VIP guest attendance and engagement. By leveraging machine learning and multi-source data integration, this project provides the commercial department with actionable insights to maximize high-value guest participation and enhance corporate hospitality ROI.

2-Data Engineering & Integration:
The solution synthesizes diverse datasets to capture a holistic view of attendance drivers:
-Performance Metrics: Historical match and league data via Superstats.dk.
-Environmental Variables: Comprehensive weather data from DMI.dk.
-Internal Operations: Proprietary empirical data provided by Viborg FF’s commercial team.

3-Technical Methodology
To ensure high model interpretability and predictive accuracy, the project utilized advanced statistical modeling and feature selection techniques:
-Model Selection: Implemented Ridge Regression, Lasso Regression, and Best Subset Selection to manage multi-collinearity and refine variable importance.
-Performance Metrics: Models were rigorously evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) for accuracy, alongside $R^2$ to assess explanatory power.
-Validation: Focused on ensuring the robustness and generalizability of the models to predict future attendance trends across varying match-day scenarios.

4-Business Impact
-Strategic Optimization: Empowered the commercial department to identify the specific variables that influence VIP attendance.
-Value Creation: Enabled proactive engagement strategies, ensuring resources are allocated effectively to maintain high attendance levels among the club’s most significant commercial partners.

1. Project Overview

DealDesk AI is a multi-agent AI solution developed for Danfoss to streamline the APR-ETO/CTO (Engineer-to-Order / Configure-to-Order) process. The project aims to eliminate manual proposal bottlenecks, reducing lead times from 3 days (72 hours) to just 60 minutes.

2. Core Architecture & Workflow

The system operates using two specialized AI agents that coordinate through a central data repository:
Agent 1: Enquiry Intake Agent
Input: Processes customer inputs such as email enquiries, meeting notes, and tender documents.
Function: Identifies and structures data into predefined variables. It uses Adaptive Cards to allow users to verify and prefill information.
Output: Generates a structured Excel file, serving as the Single Source of Truth (SSOT). This file contains all component parameters and utilizes simple formulas to optimize token efficiency.

Agent 2: Specification Agent

Input: Retrieves data from the Excel SSOT and cross-references it with technical knowledge files, manual converter lists, or MPC servers.
Function: Automatically populates technical fields (e.g., cooling requirements or fuse types) based on selected drive components.
Generation: Uses an AI prompt to map this data into a Word template using placeholders. The structured data is also stored in a Dataverse table (or Salesforce) via Power Automate.

3. Key Deliverables

Excel SSOT: A verifiable central source for all technical enquiry data.
Technical Specification: A customer-facing Word document generated in official Danfoss branding.
Salesforce Integration: Automatic record creation and administrative filing via API or Power Automate.

4. Technical Stack

Platform: Microsoft Copilot Studio.
Workflow Automation: Power Automate.
Data Management: Excel (SSOT), Dataverse, and Salesforce.