Summary We built a Databricks Genie agent for our own PMO at CloudFronts, running on Dynamics 365 data held in a Databricks lakehouse. Project managers ask a question in plain English and get an answer back across resource utilization, time tracking, billing and milestones, tickets and cases, and project status. This blog covers what the agent does, what email sentiment analysis shows that the numbers do not, and how it works inside Microsoft Teams. Table of Contents Introduction The Challenge The Solution See It in Action Business Impact Frequently Asked Questions Conclusion Introduction This started inside our own PMO — the Billing and Delivery Excellence function at CloudFronts. We learned about a project risk when someone escalated it. The warning signs came earlier than that, in email threads and internal notes, but reading every thread across every project each week was not work anyone could take on. The rest of the picture was split across systems. Billing held the invoice that had passed its due date, delivery held the milestone that had moved, support held the ticket that had been open for weeks. No one screen put those next to each other, so the PMO opened each project every week and compiled the status by hand. So we built the agent for ourselves first: a Databricks Genie agent running on Dynamics 365 data held in a Databricks lakehouse, which project managers query in plain English. The Challenge Dynamics 365 Project Operations holds everything a project manager needs — resource assignments, logged hours, billing milestones, project budgets, and delivery timelines. The data is there. The challenge is that getting specific answers from it still requires navigating multiple modules, running reports manually, and in many cases, exporting to spreadsheets to piece things together. This created a set of questions that were surprisingly hard to answer: Identifying which resources are overutilized or sitting idle requires pulling allocation data and comparing it manually against actual hours logged Understanding whether a project is at risk means cross-referencing milestone progress, budget consumption, and team capacity — a process that can take hours Billing questions — what has been invoiced, what is pending, what is approaching a milestone — require moving between finance and project views that are not always aligned Status updates for leadership need to be manually compiled, often pulling from data that was accurate yesterday but may have shifted today The result is that project managers operate on a lag — making decisions based on reports that reflect the past, not the present, and spending time producing those reports instead of acting on them. The Solution — A Genie Agent Built on Databricks and D365 Project Operations We built a Genie agent on Azure Databricks, connected to Dynamics 365 Project Operations. Project managers can now ask questions in plain English and get answers drawn directly from their project data — without building a single report. The agent is designed around the areas that matter most to project managers on a daily basis: a. Resource UtilizationThe agent can answer questions about who is overallocated, which resources have capacity available, and how utilization is trending across the team or a specific project. What previously required pulling allocation reports and comparing them against timesheets can now be answered in a single question. b. Time TrackingProject managers can ask which team members have not logged hours for the week, where hours are being spent versus what was planned, and whether a specific project is tracking within its estimated effort. The agent surfaces this from logged timesheet data in D365. c. Billing and MilestonesThe agent connects billing milestone data with project progress, allowing project managers to ask what is due for invoicing, which milestones are approaching, and whether any billing triggers are at risk of being delayed. This brings finance and delivery into the same conversation. d. Tickets and CasesThe agent surfaces open tickets and cases linked to a project — how many are open, which are overdue, how they are distributed across team members, and whether any are blocking delivery. Project managers can ask for a snapshot of issue health across one or multiple projects without navigating case queues manually. e. Email Sentiment AnalysisOne of the more telling signals of how a project is going is often hiding in the inbox. The agent analyses email communication patterns and sentiment across project stakeholders — flagging when tone is shifting, when a client’s responses are becoming shorter or more urgent, or when concerns are being raised repeatedly. This gives project managers an early, qualitative read on relationship health before it shows up in a formal escalation. f. Project StatusInstead of assembling a status report, a project manager can ask for a summary of where a project stands — budget consumed, milestones completed, risks flagged, and remaining timeline. The agent compiles this from D365 data and presents it in plain language, ready to share or act on. The conversation does not stop at one question. A project manager can ask a follow-up — drill into a specific resource, filter by project phase, or compare two projects side by side — and the agent follows the thread, refining its response at each step. Available directly in Microsoft TeamsThe Genie agent is also available as a Databricks App inside Microsoft Teams — meaning project managers do not need to switch tools to get answers. They can ask questions about their projects, resources, and billing directly from the Teams interface they already work in every day. See It in Action Weekly Work Summary — Time Tracking in ActionA project manager asks Genie for a summary of work completed last week. The agent returns a full breakdown — total hours logged, billable vs non-billable split, project-wise distribution, and key observations — in seconds. Case Detail View — Tickets and Cases in ActionA project manager asks for details on a specific case. The agent surfaces the full case record — status, owner, priority, activity timeline, and a follow-up alert — without the manager needing to … Continue reading From Project Reporting to Project Intelligence: How AI is Transforming Project Management →

Predicting the Demand: Automating Demand Forecasting in Dynamics 365 Business Central Using Azure Logic Apps, Data Lake, and Databricks Summary Knowing how many products to keep in warehouses is tough for manufacturers and distributors. During busy seasons, customer orders can jump 10 times higher than normal. When teams rely on manual spreadsheets, they often run out of products or buy too much and run out of storage space. This article explains a simple, automated solution built with Microsoft Dynamics 365 Business Central, Microsoft Azure, and Azure Databricks. Table of Contents The Problem: Swings in Customer Demand The 5-Step Solution Overview How Data is Collected and Cleaned (Medallion) How the Prophet Forecasting Model Works Clear Decision-Making with Forecasted Metrics Real Benefits for Businesses Frequently Asked Questions What You Will Learn Why manual spreadsheets and static inventory numbers fail when demand spikes. How Azure Logic Apps fetches data from Business Central automatically by using scheduled triggers. How raw records are organized into Bronze, Silver, and Gold layers. How the Prophet model forecasts demand using yearly trends and weekly patterns. How dynamic safety stock gives purchasing teams clear replenishment recommendations. 1. The Problem: Swings in Customer Demand Most manufacturers and distributors face a big challenge: customer demand is not steady throughout the year. Some months are quiet, while other months bring huge surges in orders. Season Months Demand Level What Happens Peak Busy Season June – August 8x – 10x Surge Huge spike in customer orders. Suppliers take longer to deliver, risking major stockouts. Mid-Year Rush January 3x – 4x Normal Quick wave of replacement orders and new account setups. Spring Planning March – May 2x Normal Customers use annual budgets to place advance orders for summer projects. Regular Season Off-Peak Months 1x Baseline Standard, steady daily orders. Why Traditional Methods Fail: Static Rules: Standard ERP rules use fixed inventory numbers all year. These are too small for busy seasons (causing stockouts) and too large for slow seasons (wasting money). Longer Supplier Delays: When everyone orders at once during peak seasons, suppliers take weeks longer to deliver parts. Full Warehouses: Storing large boxes during slow months takes up valuable warehouse space and ties up cash. Manual Spreadsheet Errors: Planning teams spend hours copying and pasting data into Excel spreadsheets without automated forecasting tools. “You don’t need to replace your ERP system. By adding automated cloud forecasting with Azure and Databricks to Dynamics 365 Business Central, past sales history turns into clear, actionable purchasing foresight.” 2. The 5-Step Solution Overview To solve this, we created an automated pipeline that connects daily ERP transactions to cloud forecasting and delivers clear inventory planning targets. How the Automated Flow Works 1 Dynamics 365 Business Central Holds daily sales, purchases, items, and warehouse records. ↓ 2 Azure Logic Apps (Scheduled Ingestion) Fetches data from Business Central automatically by using scheduled triggers without slowing down the ERP system. ↓ 3 Azure Data Lake (Cloud Storage) Stores all historical files securely in one central place. ↓ 4 Azure Databricks (Prophet Model) Cleans the data, runs Prophet forecasting models, and calculates the forecasted buffer stock needed for every item. ↓ 5 Visual Reports in Power BI Forecasted demand and recommended safety stock are displayed in Power BI reports for clear decision-making. 3. How Data is Cleaned & Organized (Bronze, Silver, Gold) In Azure Databricks, data moves through three simple stages known as the Medallion Architecture: Bronze Layer Raw Data Stores exact copies of daily files directly from Business Central (sales, purchases, items, warehouses). Keeps a complete, untouched history so nothing is ever lost. Silver Layer Cleaned Data Fixes missing dates, removes duplicates, and standardizes item numbers across all warehouses. Separates real customer orders from internal warehouse transfers. Gold Layer Forecasting Results Combines daily sales into clear trends and calculates forecasted stock targets for each product. Ready to feed interactive Power BI reports for planners and stakeholders. 4. How the Prophet Forecasting Model Works The Prophet forecasting model analyzes four key factors from past sales: The 3 Things the Model Learns: Overall Growth: Is customer demand growing year over year? Yearly Seasons: Which months have huge order spikes, and which months are quiet? Weekly Patterns: Do customers place most orders on weekdays compared to weekends? By combining these patterns, the system calculates the recommended safety stock for every item and warehouse: Forecasted Safety Stock: The recommended buffer quantity to keep on hand to protect against unexpected surges or supplier delivery delays. 5. Clear Decision-Making with Forecasted Metrics Instead of relying on guesswork in disconnected spreadsheets, supply chain planners have clear, data-driven targets calculated by Azure Databricks. These forecasted metrics give purchasing and warehouse managers actionable recommendations: Projected Demand: Forward-looking estimates of how many units customers will need in upcoming months. Early Order Timing: Clear signals on when to order from suppliers before peak seasons begin. Warehouse Stock Balancing: Guidance on how much inventory to position across regional warehouse hubs. 6. Real Benefits for Manufacturers Order 6–8 Weeks Ahead Purchasing teams get early warnings before big busy seasons, allowing them to book orders before supplier queues fill up. Balanced Warehouses Items are placed in the right regional warehouses closest to where customers will buy them. More Warehouse Space Bulky products arrive only when needed, keeping aisles clear and reducing expensive storage costs. Data-Driven Planning No more spending days building complicated formulas in Excel. Machine learning provides reliable demand curves and inventory targets. 7. Frequently Asked Questions (FAQ) 1 Will this slow down Business Central for daily users? No. Data is copied automatically during quiet nighttime hours into Azure. All calculations happen in the cloud, so Business Central stays fast and responsive for everyday business. 2 Why use the Prophet model instead of standard ERP reorder rules? Standard ERP rules use one fixed number for the entire year. The Prophet model automatically adapts to upcoming seasons, supplier lead times, and sales trends. 3 How do planning teams use these calculated metrics? Planning and purchasing teams access these forecasted metrics directly through interactive Power … Continue reading Predicting the Demand: Automating Demand Forecasting in Dynamics 365 Business Central Using Azure Logic Apps, Data Lake, and Databricks →

Summary Clean ERP data sitting in a data lake doesn’t answer the question every operations leader eventually asks: where exactly is our process breaking down? We built an automated pipeline that connects a client-facing web portal, Azure Table Storage, and Azure Databricks to a leading process mining platform, turning validated ERP data into a living view of how work actually flows. The pipeline is fully status-driven: every record is tracked from submission through processing to completion, with no manual exports or spreadsheet hand-offs. Purchase order data is modeled through a medallion architecture and delivered to the process mining platform, where AI-driven analysis automatically surfaces bottlenecks and deviations from the expected process. Business impact: process owners moved from static, after-the-fact reporting to a near real-time, evidence-based view of process performance. Table of Contents 01  About the Customer 05  The Six-Step Pipeline 02  The Challenge 06  Architecture Overview 03  The Solution 07  Business Impact 04  AI-Driven Process Mining 08  FAQs About the Customer Customer Spotlight A Leading Digital Transformation Partner — Europe Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles. The Challenge Standardized, clean data answers “what happened.” It rarely answers “why is this taking so long” or “where exactly is this process breaking down.” The business kept running into the same limitations: 1Why do purchase orders take longer to close in some regions than others? 2Which approval step is quietly adding the most delay to the process? 3How do we get validated ERP data into a process analysis tool without manual exports every time? 4How do we know, at any point in time, what has been processed, what’s pending, and what failed? 5Can this insight be generated automatically, instead of requiring a manual investigation every quarter? The Solution We extended the existing Databricks-based data platform with an automated, status-driven delivery layer connecting a client web portal, Azure Table Storage, Azure Databricks, and a leading process mining platform, orchestrated end-to-end with minimal manual intervention. Status-Driven Orchestration Every record carries a live status, from initial submission through sync completion, tracked in Azure Table Storage. Automated Bulk Processing Azure Logic Apps trigger the pipeline through APIs, so batches of records are processed without manual intervention. Reusable Databricks Framework The same medallion pipeline used for data standardization models Purchase Order data for process mining. AI-Driven Process Analysis The process mining platform’s AI reconstructs the real, as-executed process and highlights bottlenecks automatically. The Six-Step Pipeline Here’s how a single record moves from submission to a fully synced, process-mining-ready state: ⚙ Client Web PortalEnd-to-end data pipeline · Azure + Databricks 6 steps 🌐 1) Website Input The user submits data via the client web portal, a form or API request initiates the pipeline. ↓ 🗃 2) Azure Table Sync Incoming data is written and synced into Azure Table Storage. ↓ 📁 3) Status Filter Records from Azure Table are filtered where status matches: ✓ Perfect🕑 Queue ↓ ⚡ 4) Databricks Pipeline The framework is executed through the Databricks pipeline, processing all filtered records in batch. ↓ 🔄 5) Azure Table Update Once the Databricks sync completes, status is updated in Azure Table: Queue→✓ Synced ↓ 📊 6) UI Reflection Synced data is reflected back to the client web portal UI for the end user. Architecture Overview Once records reach the “Synced” state, the same medallion architecture used for data standardization models Purchase Order Details and Purchase Order Lines and delivers them into the process mining platform: ERPExtracts Row-header files → Bronze Raw landing → Silver Cleansed & standardized → Gold Business-ready models → DeltaLake Parquet delivery → ProcessMining AI-driven analysis Because the framework is configuration-driven, the same architecture can extend to additional ERP data lake sources, SFTP feeds, or other cloud storage without a redesign. AI-Driven Process Mining Analysis With Purchase Order Details and Purchase Order Lines modeled and delivered on a reliable, automated cadence, the process mining platform’s AI reconstructs the real, as-executed purchase order process directly from the underlying event data. Instead of relying on assumptions about how the process should work, process owners see how it actually works: where orders stall, which approval paths deviate from the intended flow, and where cycle time is quietly being lost. “A purchase order may look fine on paper, but the process data tells you exactly where it got stuck, and that gap surfaces automatically.” Business Impact Before After Manual exports required to analyze process performance Fully automated, status-driven pipeline from intake to process mining No visibility into where a record stood in processing Live status tracking from submission through sync completion Process bottlenecks discovered through manual investigation AI-driven analysis surfaces deviations and delays automatically Static, after-the-fact process reporting Near real-time, evidence-based process visibility One-off integration effort per process area Reusable framework, extendable to other business processes Frequently Asked Questions Does this require a specific process mining platform? No. The pipeline delivers modeled, business-ready data through Delta Lake and Parquet, which can be connected to most modern process mining platforms. How often is data refreshed in the process mining platform? The pipeline is designed for batch processing on a defined schedule, and can be tuned toward near real-time delivery depending on business needs and source system constraints. Can this be extended beyond Purchase Order data? Yes. Because the framework is configuration-driven, the same approach can extend to other process areas such as order-to-cash or procure-to-pay. What happens if a record fails validation? Records that don’t meet the status criteria simply remain in a pending state and are not passed downstream, so failures are visible and traceable rather than silently dropped. Conclusion Clean data is the foundation, but process visibility is where the business … Continue reading From ERP Data to Process Mining Insights: Building an Automated Pipeline for Real-Time Process Visibility →

Summary Enterprises running large, multi-country ERP systems often extract data that is technically complete but practically unusable, split across duplicate language columns and encoded with undocumented numeric values. We built a self-service data platform on Azure so that business users, not just data engineers, could define, validate, and process ERP extracts without writing a single line of code. The solution resolves two of the most common ERP data problems: a single field like “Item Description” spread across nine language-specific columns, and reference fields like “Order Status” stored only as numeric codes. A custom web portal puts business users in control of table specifications, validation rules, and processing status, while Azure Databricks and Delta Lake quietly do the heavy lifting behind the scenes. Business impact: dozens of ERP tables moved from raw, multi-language, code-heavy extracts to a single, trusted, human-readable data layer, without adding headcount to the data engineering team. Table of Contents 01  About the Customer 05  Self-Service Data Onboarding 02  The Challenge 06  Medallion Architecture 03  The Solution 07  Business Impact 08  FAQs 09  Conclusion About the Customer Customer Spotlight A Leading Digital Transformation Partner — Europe Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles. The Challenge Most organizations extracting data from a large ERP system successfully get the data out. The problem isn’t extraction, it’s making that data mean something the moment it lands. Business and IT teams found themselves asking the same questions on repeat: 1Why does the same field appear nine times, with a different value in each column? 2What does “Order Status= 3” actually mean, and who is the source of truth for that mapping? 3How much manual translation and lookup work happens before a single report can be trusted? 4Can business users resolve these issues themselves, without waiting weeks on an IT backlog? 5How do we scale this across dozens of tables without writing dozens of one-off scripts? Two problems came up again and again, and both are far more common across ERP implementations than most leadership teams realize. Multi-Language Columns Because the ERP system was configured for every Order Status the business operates in, a single logical field such as “Item Description” existed as up to nine separate columns, one per language: English, French, German, Spanish, and more. Reports built directly on top of the raw extract had no reliable way of knowing which column to use for which record. In practice, this meant a plant manager in France could open a report and see item names in German, while a sales report for the Spanish market silently pulled blank fields because the Spanish-language column hadn’t been populated for that record. The data was all there; it just wasn’t usable without someone manually deciding, table by table, which language column to trust. Undocumented Domain Values Reference fields like Country, Currency, and Order Status were stored as raw numeric codes rather than readable labels, for example Order Status: 1 = Completed , 2 = In Progress, 3 = Shipped. These mappings lived inside ERP configuration screens, not in the extracted data itself. That meant every downstream report, dashboard, or spreadsheet needed its own copy of the same lookup table, manually kept in sync. When a code changed or a new Order Status was added in the ERP, there was no guarantee every report using it would be updated at the same time, which meant leadership could be looking at the performance chart that was quietly wrong. The Solution Rather than writing custom transformation logic for every table (a solution that ages badly the moment a new table or region gets added), we designed a configuration-driven pipeline built on Azure Databricks, fronted by a self-service web application that puts control directly in the hands of business and functional users. Self-Service Web Portal Business users upload table specifications, review validation results, and queue tables for processing, entirely through a browser. Medallion Architecture Azure Databricks and Delta Lake refine raw extracts through Bronze, Silver, and Gold layers, without table-specific code. Automated Language Resolution Multi-language columns are detected and normalized automatically based on the specification, not hardcoded per table. Centralized Domain Mapping Numeric and coded reference values are resolved against a single, maintained lookup layer instead of scattered spreadsheets. Self-Service Data Onboarding: No Databricks Knowledge Required The centerpiece of the solution is a custom web application that lets a business or functional analyst, not a Databricks engineer, onboard a new ERP table from start to finish. Here’s what that looks like in practice: A business user uploads an Excel-based table specification defining the expected columns, data types, which fields are multi-language, and which fields are domain-coded and how to decode them. The portal validates the specification instantly, flagging missing mandatory columns or mismatches before any data is processed, so problems are caught at the source rather than three reports downstream. Once validation passes, the same user queues the table for processing with a single click. No notebook to open, no cluster to configure, no code to write or review. Behind the scenes, that specification feeds a generic, reusable Databricks framework that already knows how to apply the correct language resolution and domain-value decoding rules, so engineering effort doesn’t scale linearly with the number of tables. In effect, the portal turns “add a new ERP table to the analytics environment” from a data engineering request into a form a finance or operations analyst can complete in minutes, while still enforcing the same rigor and consistency a hand-built pipeline would require. Medallion Architecture on Databricks Once a table is queued through the portal, Azure Databricks takes over: Bronze: Raw ERP extracts are landed as-is, preserving … Continue reading How a Self-Service Data Portal Solved Multi-Language and Domain Value Chaos in ERP Data →

Summary A Netherlands-based non-profit sustainability certification organisation reduced manual certification configuration time from hours to mere seconds using Microsoft Power Apps, implemented by CloudFronts. CloudFronts configured a multi-level assessment framework — Scope, Category, Requirement, Criteria — to automate 100% of assessment generation based on user-selected certification types and versions. The solution integrated Microsoft Power Apps with Azure Blob Storage to provide a secure, centralised repository for thousands of pieces of certification evidence, linked directly to each requirement record. Microsoft Dynamics 365 Customer Service was configured to streamline global applicant inquiries with automated case routing across Marketing, Finance, and Info queues. Business impact: eliminated manual configuration errors, provided real-time progress visibility for global applicants, and established a scalable digital foundation for global circular economy standards. Table of Contents 01 Summary 02 Introduction 03 The Business Problem 04 The Solution 05 Implementation 06 Business Impact 07 FAQs 08 Conclusion Introduction In a world where manufacturers and brands are under increasing pressure to prove the sustainability credentials of their products, the rigour and speed of certification processes can directly determine an organisation’s ability to scale its global mission. For certification bodies operating across multiple geographies, managing assessments, evidence, and applicant communication through fragmented manual processes is a bottleneck that no amount of headcount can solve. For one Netherlands-based non-profit at the forefront of the global circular economy movement, this bottleneck was real and growing. Their certification programme, built on a rigorous multi-level standard covering material safety, circularity, and responsible production, was being administered through massive Excel files, disconnected email threads, and manual document searches. This blog documents how CloudFronts partnered with this organisation to replace those fragmented processes with a unified, automated certification platform built on Microsoft Power Apps, Azure Blob Storage, Dynamics 365 Customer Service, and Power Automate, reducing certification setup time from hours to under thirty seconds. The Business Problem The organisation operates as the leading global authority on circular economy certification, serving manufacturers and brands worldwide. Their certification programme evaluates products across categories like Material Health and Circularity, across multiple standard versions — v3.1 and v4.1 — each with its own hierarchy of scopes, categories, requirements, and criteria. Before partnering with CloudFronts, this complexity was managed almost entirely by hand: Each new certification application required assessors to manually configure assessment structures from sprawling Excel files with hundreds of rows, a process that took two to four hours per applicant. Supporting evidence such as product test reports, material declarations, and third-party certificates was stored without a structured system, making retrieval slow and validation unreliable. Neither applicants nor internal assessors had real-time visibility into application status or outstanding requirements, creating persistent communication delays. Managing different certification versions across different scopes manually made scaling the programme globally nearly impossible without proportionally growing the team. The organisation needed a platform that could encode their complex certification logic, automate the heavy lifting of assessment generation, and give every stakeholder a single, reliable view of the certification pipeline. The Solution CloudFronts implemented a comprehensive digital certification ecosystem anchored by a custom Microsoft Power Apps application — the Certification Manager. The platform automates the core logic of the certification standard end-to-end, from application intake through assessment generation, evidence management, and case resolution. Key Components Microsoft Power Apps Core Certification Manager application handling applications, multi-level assessments, and the applicant-facing UI. Azure Blob Storage Secure, centralised repository for all certification evidence, linked directly to individual requirement records. Dynamics 365 Customer Service Configured to streamline global applicant inquiries with automated case routing across Marketing, Finance, and Info queues. Microsoft Power Automate Automation layer handling document upload workflows and notification triggers throughout the certification lifecycle. How It Works, At a Glance The centrepiece of the solution is a version-driven automation engine. When an assessor creates a new certification application and selects the standard version and scope, the backend logic automatically generates the complete assessment structure — all categories, requirements, and criteria — without any manual configuration. What previously took hours now takes under thirty seconds. A custom HTML-based interface within Power Apps provides visual progress indicators, allowing assessors to track completion rates across requirements at a glance. All supporting evidence is stored in Azure Blob Storage and linked directly to the specific requirement record it supports, creating a fully auditable, ISO 17065-compliant evidence trail. Implementation 1 Step 1 Certification Scheme Definition and Version Logic The foundation of the platform is the Certification Scheme Definition module. CloudFronts built a backend logic engine that stores the full structure of each certification version including all scopes, categories, requirements, and criteria as configuration data rather than hardcoded templates. When a user selects a version and scope combination, this engine automatically pulls the correct downstream structure and generates it on the application record. Updates to global standards can be deployed instantly by updating the configuration, with no changes to the application logic required. The four-level assessment hierarchy: Scope, Category, Requirement, Criteria is the structural backbone of the entire certification standard, now encoded directly into the platform. 2 Step 2 Automated Assessment Generation Once the version and scope are selected on a new application, the platform’s automation engine generates the full assessment structure in under thirty seconds, replacing a manual Excel-driven process that previously took two to four hours per applicant. The generated assessment is displayed through a custom HTML interface inside Power Apps, with visual progress indicators showing completion rates at the category and requirement level. Assessors can immediately see which requirements are outstanding, which have linked evidence, and which are ready for review. 3 Step 3 Evidence Management via Azure Blob Storage A core architectural decision was to decouple evidence storage from the Power Platform’s native Dataverse storage. CloudFronts integrated Azure Blob Storage as the document repository, with each uploaded file linked directly to the specific requirement record it supports within Power Apps. This approach delivers high-performance scalability for large evidence files while significantly reducing long-term storage costs compared to storing files natively in Dataverse. Power Automate flows handle upload automation and trigger notifications … Continue reading How a Netherlands-Based Nonprofit Achieved Global Scalability with Microsoft Dynamics 365 CRM and Power Platform →

Summary This blog highlights how Azure Logic Apps was used to overcome a critical API limitation encountered during the integration of Zoho People with FNO for payroll management. During the implementation for a global manufacturing hardware enterprise, we discovered that Zoho’s API allows a maximum of 200 records to be fetched in a single request. While this limitation may not impact smaller organizations, it creates significant challenges for enterprises managing large employee datasets. To address this issue, a scalable Azure Logic Apps solution was developed that dynamically retrieves records in batches, consolidates the results, and returns a complete dataset for downstream processing. This blog explains: Table of Contents 1. Customer Scenario During the implementation of a payroll integration between Zoho People and FNO, employee master data needed to be synchronized automatically to support payroll processing. The organization maintained a large workforce within Zoho People, and payroll operations depended on accurate employee data being transferred to downstream systems. As the integration design progressed, a significant limitation was identified within Zoho’s API framework. The API could return a maximum of 200 records per request. For organizations with hundreds or thousands of employees, this restriction created a challenge in retrieving complete employee datasets efficiently. 2. Business Challenge The integration required access to the full employee dataset from Zoho People. However, the following challenges emerged: Limited API Response Size Zoho’s API only returns 200 records per request. Large Employee Dataset The organization maintained significantly more than 200 employee records. Manual Pagination Not Feasible Static API calls would require manual intervention or complex custom development. Scalability Concerns As employee counts continued to grow, the solution needed to support future expansion without requiring redesign. The objective was to create a scalable and automated mechanism capable of retrieving all employee records regardless of volume. 3. Integration Architecture The solution architecture follows a simple but highly scalable pattern. Process Flow 4. Configuration Steps Step 1: Add HTTP Trigger Step 2: Initialize Variables Step 3: Do Until Loop Step 4: HTTP Request Action Step 5: Output Variable Step 6: Compose Variable Step 7: Append to Array Variable Step 8: Set Variable Step 8: Increment Variable Step 9: Add Response Trigger 5. Why Azure Logic Apps? Azure Logic Apps was instrumental in creating a flexible and efficient solution. Key capabilities that made Logic Apps the ideal choice included: Dynamic Variable Management Allows runtime manipulation of counters and arrays. Scalable Workflow Execution Supports large datasets without requiring custom application development. Native API Integration Provides seamless connectivity with REST-based services. Low-Code Development Accelerates implementation and simplifies maintenance. Enterprise Reliability Offers monitoring, logging, and error-handling capabilities required for production environments. 6. Outcome The final solution successfully overcame Zoho’s API record limitation. The Logic App automatically: This approach ensured the success of the Zoho-FNO integration while maintaining scalability for future business growth. 7. Business Impact 1] Fully Automated Data Retrieval Employee data is retrieved without manual intervention. 2] Improved Scalability The solution can support organizations with thousands of employee records. 3] Reduced Development Complexity Logic Apps eliminated the need for extensive custom coding. 4] Faster Integration Processing Data retrieval occurs efficiently through automated pagination. 5] Improved Reliability Built-in monitoring and error handling improve operational stability. 6] Future-Proof Architecture The solution continues to perform effectively as employee counts grow. To conclude, Integration projects often reveal platform-specific limitations that require creative problem-solving. In this implementation, Zoho’s 200-record API limitation had the potential to impact payroll synchronization for a growing workforce. By leveraging Azure Logic Apps, we developed a scalable and automated solution capable of dynamically retrieving and consolidating employee data regardless of record volume. The solution not only resolved the immediate challenge but also established a reliable and future-ready integration framework capable of supporting continued organizational growth. For organizations facing similar API limitations, Azure Logic Apps provides a powerful platform for building scalable, low-code integration solutions that simplify complex data processing requirements.

As businesses scale, payroll complexity grows bringing challenges around employee data, attendance, compensation structures, and compliance. Manual processes not only consume valuable time but also increase the risk of costly errors. The Zoho People-FNO integration transforms payroll into a streamlined, automated process, ensuring accurate salary calculations, seamless data synchronization, and complete transparency across HR and finance operations. We recently implemented this solution for a global manufacturing hardware enterprise, enabling them to automate payroll workflows, eliminate manual data reconciliation, improve payroll accuracy, and reduce administrative overhead. The integration provided a scalable foundation for managing a growing workforce while maintaining compliance and enhancing the employee experience through faster, more transparent payroll processing. For organizations focused on operational efficiency and sustainable growth, this integration delivers measurable business value from day one. Understanding the Architecture of Zoho and FNO Integration The integration between Zoho People and FNO involves a clear, structured workflow. Below is an overview of the steps involved: This architecture ensures a smooth flow of data between Zoho and FNO, simplifying payroll management for businesses of all sizes. Key Advantages of Zoho and FNO Integration The integration between Zoho People and FNO streamlines payroll management, providing several key benefits: Effortless Payroll Management for Growing Businesses To conclude, efficient payroll management is essential for any growing business. By integrating Zoho People with FNO, businesses can automate payroll processes, ensure accurate calculations, and provide employees with easy access to their payslips. The seamless data flow, real-time updates, and reduced manual intervention significantly improve operational efficiency and transparency. If you’re ready to optimize your payroll system, now is the time to take action. Embrace the Zoho and FNO integration to simplify your processes, reduce errors, and create a transparent payroll system that benefits both your employees and your organization. Contact us today to learn how this integration can simplify your payroll management process. Reach out at transform@cloudfronts.com.

Sustainability certification is one of the most operationally demanding programs a nonprofit can run. It is not just a badge on a product, it is a multi-year, multi-stakeholder process involving manufacturers, independent assessment bodies, scoring frameworks, document issuance, and public transparency requirements. When you are managing thousands of products across global industries, the cracks in a manual, spreadsheet-driven operation show up fast. This is exactly the situation a Netherlands-based nonprofit found itself in. The organization administers a globally recognized product sustainability certification program, assessing products across five dimensions: material health, product circularity, clean air and climate protection, water and soil stewardship, and social fairness. Products move through certification levels Bronze, Silver, Gold, and Platinum across a lifecycle that spans application, third-party assessment, issuance, and periodic recertification every three years. As certification volumes grew, so did the operational complexity. Disconnected tools, manual document preparation, and no single place to track everything meant the team was spending more time managing the process than running it. Rather than bolt on yet another external tool, the organization made a deliberate architectural choice: build the entire certification management platform inside Microsoft Dynamics 365, extend it with Azure Function Apps for automation, and expose public APIs for ecosystem transparency. The Goal Build a unified, scalable certification lifecycle management system inside Dynamics 365 that automates document generation, manages logo assets, and exposes public APIs for published certification data — all without introducing new platform dependencies. The Business Problem To understand what was built, you first need to understand what was broken. The organization’s operational teams were trying to answer some fairly fundamental questions every single day — What is the current certification status of a given product? Which products are approaching their recertification deadline? Which assessment body certified a product and when? Is the certificate document ready for issuance? None of these questions had a reliable, centralized answer. Certification records lived across disconnected spreadsheets and email threads, which meant any “current” view of a product’s status was only as accurate as the last person who updated a row. Certificate documents were manually composed for every issuance a slow, error-prone process that created formatting inconsistencies and delayed the experience for certified manufacturers. Logo assets were managed informally, with no version control or consistent delivery process. No Single Source of Truth Certification records scattered across spreadsheets and email threads with no reliable current view. Manual Document Creation Every certificate composed by hand slow, inconsistent, and a bottleneck manufacturers felt directly. Zero Public Transparency External stakeholders relied on manually updated static pages with no programmatic access to live data. Unscalable Operations Growing program volumes with no automation meant every new product added to the manual workload. The Solution Architecture The platform was designed around one principle: build close to where the operational data already lives, and automate at the right trigger points rather than everywhere at once. The solution runs on three deliberate layers. Critically, this architecture avoided over-engineering entirely — no separate data warehouse, no heavy ETL pipeline, no dedicated certification SaaS platform requiring its own licensing and maintenance. Everything runs inside the Microsoft ecosystem. 1 Data Layer — Custom Dynamics 365 Tables Purpose-built Dataverse tables that mirror the certification domain exactly, products, certification events, assessment bodies, category scores, and logo assets all in a single relational, auditable structure. 2 Automation Layer — Azure Function Apps, Dynamics Plugins Two event-driven Function Apps sit alongside the CRM one for certificate document generation, one for logo package delivery, both triggered by real state changes in the certification lifecycle, not a schedule. 3 Transparency Layer — Public REST APIs Public-facing APIs expose published certification data to external stakeholders, brands, retailers, regulators, and third-party platforms without any manual data exchange with the organization. Custom Dynamics 365 Data Model The data model is the foundation everything else rests on. Rather than forcing certification concepts into standard CRM entities that were never designed for this domain, the team built purpose-specific custom tables inside Dataverse that mirror how the certification program actually works. Product data Core product records, variants, and identifiers — the foundational layer that everything else references. Application handling Applications, assessments, category and requirements assessments — all managed within accounts. Assessment bodies and related workflows live here too. Public-facing entities Public tables for products, certifications, certificates, and product variants — the data layer that powers external visibility and API exposure. Together, these layers gave the organisation a complete, relational view of every certified product across its full lifecycle — all within a single operational platform. Certificate Document Generation via Azure Function App Before this system existed, every certificate document was created by hand. Someone would take a template, fill in the product details, format it, check it, and send it. For an organization issuing certificates across thousands of products, this was not just slow — it was a source of constant inconsistency and a bottleneck that manufacturers felt directly. The Azure Function App for certificate generation eliminated this entirely. Here is how it works end to end: How It Works ⚡ Trigger Certification record reaches the correct status in Dynamics 365 → 🔍 Fetch Pulls record + product data via Dynamics 365 Web API → 📄 Generate Selects correct template, populates all fields, generates document → 🔗 Store & Link Saves document and links it back to the certification record What this means in practice is that certificate issuance is now consistent, fast, and entirely hands-off for the operational team. Formatting is guaranteed every time because the template logic is defined once and applied uniformly. The function also runs independently of the CRM interface — making it resilient and reusable across multiple trigger scenarios, including bulk recertification processing. The impact: A task that previously required manual effort for every single issuance now requires none. Eliminated entirely. Logo Image Generation via Azure Function App A certified product comes with more than a document — it comes with the right to use the certification mark. For manufacturers, that logo is a commercial asset. It goes … Continue reading How a Netherlands-Based Non-Profit Transformed Certification Management with Dynamics 365 and Azure Functions →

Summary We implemented an automated cost control process for our internal AI Sales Insights solution running in Azure. Azure Budget Alerts were connected with Automation Runbooks to automatically stop resources once spending crossed a defined threshold. The setup reduced manual monitoring and helped us maintain tighter control over Azure consumption. The solution used native Azure services with Managed Identity authentication for secure automation. The same approach can be extended to Azure Virtual Machines, App Services, Databases, and other Azure resources. Azure Budget Alerts integrated with Automation Runbooks for automated cost control Table of Contents 1. Why We Needed This 2. Solution Overview 3. Azure Services Used 4. Step 1: Create the Azure Budget 5. Step 2: Create an Action Group 6. Step 3: Create the Automation Runbook 7. Step 4: Enable Managed Identity 8. Step 5: Assign Required Permissions 9. Step 6: Test the Complete Flow 10. Business Impact 11. Frequently Asked Questions 12. Conclusion Why We Needed This Our AI Sales Insights solution uses Azure services such as Function Apps and AI processing components to generate and process sales insights. Since the environment runs on a Pay As You Go subscription, costs can increase unexpectedly if resources continue running after active usage hours or during extended testing cycles. Initially, we were monitoring costs manually through Azure Cost Management dashboards and email alerts. However, this approach had a few limitations: Alerts were sometimes noticed too late Resources continued running even after thresholds were reached Development environments stayed active longer than needed Manual intervention was required every time We wanted a simple solution where Azure could automatically take action when spending crossed a defined limit. Solution Overview We implemented the following workflow: Azure Budget continuously monitors subscription spending A Budget Alert gets triggered when the threshold is reached The alert calls an Azure Action Group The Action Group triggers an Automation Runbook The Runbook authenticates using Managed Identity The Runbook automatically stops the Azure resource This gave us a lightweight and reliable automation setup for controlling costs. Azure Services Used Service Purpose Azure Cost Management Monitor Azure spending Azure Budget Alerts Detect threshold breaches Azure Action Groups Trigger automation Azure Automation Execute PowerShell Runbooks Managed Identity Secure authentication Step 1: Create the Azure Budget We started by creating a budget inside Azure Cost Management. Azure Budget configuration for automated cost monitoring Budget Configuration Setting Value Budget Amount ₹800 INR Reset Frequency Monthly Forecast Alert 90% Actual Cost Alert 100% One important thing we learned during implementation is that Azure budget calculations are not always real time. If automation starts only at 100%, additional usage may already occur before the alert executes. Because of this, setting automation thresholds slightly earlier, usually around 85% to 90%, is safer for production environments. Step 2: Create an Action Group Next, we created an Azure Action Group. The Action Group acts as the bridge between the Budget Alert and the Automation Runbook. Set Action Type as Automation Runbook Select the Automation Account Link the target Runbook Once configured, Azure automatically executes the Runbook whenever the budget alert is triggered. Step 3: Create the Automation Runbook Inside Azure Automation, we created a PowerShell Runbook using Runtime Version 5.1. The Runbook dynamically accepts Azure resource values instead of hardcoded names, making it reusable across multiple environments. Dynamic PowerShell Script # Variables $ResourceGroupName = “{Your Azure Resource Group Name}” $FunctionAppName = “{Your Azure Function App Name}” # Authenticate using System Managed Identity try { Connect-AzAccount -Identity Write-Output “Successfully authenticated with Managed Identity” } catch { Write-Error “Failed to authenticate: $_” exit 1 } # Stop Azure Function App try { Stop-AzFunctionApp ` -Name $FunctionAppName ` -ResourceGroupName $ResourceGroupName ` -Force Write-Output “Function App stopped successfully” } catch { Write-Error “Failed to stop Function App: $_” exit 1 } Step 4: Enable Managed Identity To avoid storing credentials inside the script, we enabled System Assigned Managed Identity for the Automation Account. This allowed the Runbook to authenticate securely against Azure without usernames, passwords, or secrets. This approach is cleaner, more secure, and easier to maintain. Step 5: Assign Required Permissions After enabling Managed Identity, we assigned RBAC permissions to the Automation Account on the target resource. The following roles were used: Contributor Website Contributor Function App Contributor Without proper permissions, the Runbook may authenticate successfully but still fail while stopping resources. Step 6: Test the Complete Flow Before connecting everything to the Budget Alert, we tested the Runbook manually. Verify Managed Identity authentication Confirm the Azure resource stops correctly Validate RBAC permissions Test Action Group execution Once everything worked successfully, the Runbook was connected to the Budget Alert. Business Impact Area Before After Cost Monitoring Manual Automated Resource Shutdown Manual Automatic Response Handling Manual Action Required Automatic Resource Shutdown Cost Governance Reactive Proactive This setup helped us reduce unnecessary runtime costs and eliminated the need for continuous manual monitoring of our AI Sales Insights environment. Conclusion This implementation helped us build a simple and practical cost governance process for our internal AI Sales Insights solution. Instead of relying only on dashboards and email notifications, Azure now takes automated action whenever spending crosses the configured threshold. As AI workloads continue growing, automating cloud cost control will become increasingly important for maintaining predictable and manageable Azure spending. We hope you found this blog useful. If you would like to discuss similar Azure automation and cost optimization solutions, feel free to connect with us at transform@cloudfronts.com .

What happens when a global manufacturing giant needs to modernize its operations without grinding critical business processes to a halt? The answer is not a rip-and-replace approach – it is a carefully engineered integration strategy that lets modern and legacy systems co-exist, communicate, and complement each other. Are you planning an ERP upgrade but worried about what happens to the legacy systems your operations depend on? If so, this is for you. One of North America’s leading commercial vehicle manufacturers faced exactly this dilemma. With decades of investment in legacy financial and warehouse management systems, a hard cutover to a new ERP was not an option. Yet the need for a modern, scalable platform was undeniable. Their solution? Introduce Microsoft Dynamics 365 Finance & Operations (D365 F&O) as the new operational backbone – while keeping their legacy systems in play for financial control – and build robust, bi-directional integrations to bridge both worlds. At CloudFronts, we had the privilege of architecting and implementing those integrations. This blog walks you through three core data flows: Spot Purchase Orders, Advance Shipment Notices (ASN), and Goods Receipt Notes (GRN) – and what it really takes to make a modern ERP talk to a legacy system without missing a beat. Why Replace When You Can Integrate? Legacy systems in large manufacturers are not just old software. They carry years of financial logic, vendor relationships, and compliance configurations that are too risky to discard overnight. Replacing them introduces enormous operational and compliance risk. Doing nothing, however, is not an option either. The approach our client took – and one we increasingly recommend for manufacturers, distributors, and large enterprises – is a co-existence model: This means the business gets the agility of a modern ERP on day one, without putting financial operations at risk. The three integrations do the heavy lifting. Architecture at a Glance Before diving into each integration, it helps to understand the overall data flow pattern and the Azure services involved: Component Role D365 F&O System of record for purchasing and receiving operations Legacy System Retains financial control, inventory management authority Azure Logic Apps Parent-child middleware: orchestrates, transforms, and routes data Azure Blob Storage Checkpoint management for reliable incremental processing Azure Table Storage Full execution logs for traceability, audit, and failure replay The three integrations work in concert: Integration 1: Spot Purchase Orders — D365 F&O to Legacy Business Problem A Spot Purchase Order is an ad-hoc purchase order raised outside of long-term contracts — often for urgent material procurement. Spot POs are created and managed in D365 F&O by procurement teams. However, the legacy system is the system of financial record, meaning every Spot PO created in D365 must be reflected in the legacy system for financial commitment tracking and vendor payment processing. Without integration, this would require manual re-entry – a process prone to error, delay, and duplication. How the Integration Works Parent Logic App – Spot PO Orchestrator The primary Logic App runs on a scheduled recurrence and uses a checkpoint mechanism stored in Azure Blob Storage to fetch only incremental changes – purchase orders created or modified since the last successful run. This ensures efficiency and prevents reprocessing of already-handled records. The workflow determines the operation type required for each PO: For each scenario, the Logic App fetches enriched data from multiple D365 F&O OData entities and constructs a structured JSON payload tailored for the legacy system’s API. ⚙ Tech Note: OData Entities Used PurchaseOrderHeaders, PurchaseOrderLinesV2, PurchaseLineDataEntities, WHSPurchLines, StatusCustomDatas Child Logic App – SendRequest (Reusable) Rather than embedding API communication logic directly in the orchestrator, we separated it into a reusable child Logic App. This child app receives the constructed payload, retrieves an OAuth 2.0 Bearer token, and executes the HTTP POST call to the legacy system’s API endpoint. This modular design pays dividends during maintenance: any change to authentication logic or API communication is made once in the child app and automatically applies to all parent integrations. Failed Record Handler Every enterprise integration needs robust failure recovery. When an API call fails: Sample Payload – Spot PO Create Sample JSON Payload: {   “userId”: “JSMITH”,   “order”: “456789”,   // Last 6 digits of D365 PO number   “vendor”: “VEND001”,   “receiptLoc”: “SITE01”,   “vendorOvrdCd”: “14”,   “lineItems”: [{     “orderLine”: “001”,     “item”: “ITEM001”,     “openQty”: 10,     “deliveryDate”: “061526”,  // MMddyy format for legacy compatibility     “comment”: “MPSSYS order – JSMITH” }] } } ✓ Business Impact: Zero manual re-entry of purchase orders between systems. Every Spot PO created or changed in D365 F&O is automatically reflected in the legacy system within minutes. Integration 2: Advance Shipment Notices — Legacy to D365 F&O Business Problem An Advance Shipment Notice (ASN) is a notification sent by the legacy WMS to the receiving system, informing it of an incoming shipment before it physically arrives. D365 F&O needs to receive ASNs to create Inbound Load Headers and Load Lines – enabling warehouse teams to prepare for receiving. Without this integration, receiving teams in D365 would be blind to incoming shipments until trucks arrived at the dock – eliminating any opportunity for advance dock scheduling, labor planning, or inventory pre-positioning. The Hybrid Integration Approach This integration presented an interesting technical challenge: the standard D365 F&O Inbound ASN V5 API supports a well-defined XML format, but the business required additional fields beyond what the standard API supports. The solution was a two-step Hybrid ASN Integration approach: ⚙ Tech Note: API Endpoint Pattern Insert:  POST {{BASE_URL}}/api/connector/enqueue/{{ACTIVITY_ID}}?entity=Inbound ASN V5 Enrich:  PATCH on InboundLoadHeaders and WHSASNWorkData Smart Insert vs. Update Determination To handle scenarios where an ASN might be re-sent for corrections or resynchronization, the integration includes a check before processing: This idempotent design prevents duplicate inbound loads from being created when the legacy system re-sends an ASN. One nuance worth noting: in D365’s standard ASN structure, the LoadId, ShipmentId, and LicensePlateNumber must carry the same value. The legacy system’s outbound ASN payload is configured to honour this requirement – ensuring clean data entry … Continue reading How a Top North American Commercial Vehicle Manufacturer Connected D365 F&O with Legacy Systems Without Disrupting Operations →