Blogger CloudFrontsbloggerEdit Profile Summary Expense management processes in enterprise project environments often require strict documentation controls to ensure financial accuracy and compliance. One common requirement is the mandatory attachment of receipts when submitting expense entries, especially for specific expense categories such as airfare, accommodation, or high-value reimbursements. My blog describes how a project-driven organization streamlined its expense submission workflow using Canvas Apps integrated with Dynamics 365 Project Operations. The solution I had implemented automates the validation and submission process for expense entries while ensuring that receipt files are attached before submission. By combining form validation logic in the Canvas App with backend automation using Power Automate, the organization eliminated the manual process of attaching receipts and creating notes in the system. The result was a seamless, user-friendly expense submission experience that enforces compliance while significantly improving operational efficiency. Table of Contents 1. Customer Scenario 2. Solution Overview 3. Understanding the Expense Data Structure 4. Canvas App Validation Logic 5. Expense Creation and Submission Process 6. Automating Receipt Handling with Power Automate 7. Enforcing Mandatory Receipts for Specific Categories 8. Business Impact 9. Solution Walkthrough 10. Final Thoughts 1. Customer Scenario A Texas based Cyber Security organization managing multiple client engagements relied on Dynamics 365 Project Operations to track project expenses incurred by consultants and field staff. While the system allowed users to create draft expense entries, the process of submitting those expenses required additional manual steps. To submit an expense, users had to: Create the expense entry in draft mode. Upload the supporting receipt manually. Navigate to the expense record. Create an associated Expense Receipt record. Attach the receipt file under Notes (Annotations). Convert the file into the required document format stored in the system. Update the expense status to Submitted. This workflow introduced several challenges: Users frequently forgot to attach receipts. Manual creation of Notes records was error-prone. Finance teams had to follow up for missing documentation. Expense approvals were delayed due to incomplete submissions. Employees found the process unnecessarily complex. The organization needed a simpler, controlled way to ensure receipts were always attached when expenses were submitted, without requiring users to understand the underlying system structure. 2. Solution Overview To address these challenges, a custom expense submission experience was built using Canvas Apps integrated working in conjunction with D365 Project Operations. The solution introduced a dynamic expense entry submission interface where users can: Create expense entries Upload receipt files Submit expenses directly from the app Figure: Canvas App interface enabling dynamic expense entry submission with receipt attachment. Figure: Canvas App interface enabling dynamic expense entry submission with receipt attachment. Behind the scenes, the application automatically: Creates the expense entry in Dataverse Generates the related Expense Receipt record Uploads the receipt file to Notes (Annotations) as a document Updates the expense status to Submitted Figure: A Submitted Expense Entry Record. Figure: The Expense Receipt Record + Receipt PDF Annotation associated with the Expense, without which Submission won’t have been possible. This automation completely overrides the manual procedure of attaching receipts and creating notes, ensuring the process is both compliant and seamless for users. 3. Understanding the Expense Data Structure Within Dynamics 365 Project Operations, expense documentation follows a structured relationship model. The hierarchy looks like this: Expense ↓ Expense Receipt ↓ Notes (Annotation) ↓ Blob/Base64 File Storage of Expense Receipt. Figure: Implementation of the Expense Entry -> Expense Receipt -> Annotation -> Receipt File Workflow. In this structure: The Expense record stores the financial transaction. The Expense Receipt record acts as a container for receipt documentation. The Notes (Annotation) entity stores the actual file. The receipt file is stored as Base64 binary data (blob). While this structure is technically sound, it requires multiple manual steps when performed directly by users. The custom Canvas App abstracts this complexity and handles it automatically. 4. Canvas App Validation Logic To ensure all required data is captured before submission, the Canvas App includes dynamic validation logic. The application checks whether essential fields are populated before allowing the expense to be saved or submitted. These validations include fields such as: Transaction Date Project Expense Category Reimbursable Indicator External Comments Unit Quantity Unit Price If any required field is missing, the user receives an immediate notification explaining what needs to be completed. Example logic used in the application: If( Or( IsBlank(DatePicker2_5.SelectedDate), IsBlank(Project_Combobox_5.Selected), IsBlank(ExpenseCategory_Combobox.Selected), IsBlank(Dropdown1.Selected.Value), IsBlank(External_Input_7.Text), IsBlank(Project_Combobox_6.Selected), IsBlank(NumberInput2.Value), IsBlank(NumberInput2_1.Value) ), Notify(“Required fields are missing.”, NotificationType.Error) ) This validation ensures data completeness before the expense record is created. 5. Expense Creation and Submission Process Once validation passes, the Canvas App uses a Dataverse Patch operation to create or update the expense record. The logic dynamically calculates financial values such as the subtotal based on quantity and unit price. Example logic: Set( varSavedExpense, Patch( Expenses, Defaults(Expenses), { ‘Transaction Date’: DatePicker.SelectedDate, Project: Project_Combobox.Selected, ‘Expense Category’: ExpenseCategory_Combobox.Selected, Quantity: Value(NumberInputQuantity.Value), ‘Unit Price’: Value(NumberInputPrice.Value), Subtotal: Value(NumberInputQuantity.Value) * Value(NumberInputPrice.Value) } ) ); ‘SubmitExpense(CanvasApp)’.Run( varSavedExpense.Expense, First(fileupload.Attachments).Name, First(fileupload.Attachments).Value ); Notify( “Expense submitted successfully.”, NotificationType.Success ) This creates the draft expense entry in the system. 6. Automating Receipt Handling with Power Automate After the expense entry is saved, the Canvas App triggers a Power Automate flow. The flow receives: Expense record ID File name Receipt file content The flow then performs the following steps automatically. Step 1: Create Expense Receipt Record A new Expense Receipt record is created and linked to the expense entry. Step 2: Upload Receipt as Note The receipt file is stored as a Note (Annotation) associated with the expense receipt. This note contains: File Name Document Type Base64 encoded file content MIME type Step 3: Update Expense Status Finally, the expense record status is updated from Draft to Submitted. This ensures the expense becomes available for approval workflows and financial processing. 7. Enforcing Mandatory Receipts for Specific Categories An important requirement D365 PO is ensuring that certain expense categories cannot be submitted without receipts. Examples include: Airline tickets Travel expenses Accommodation High-value reimbursements The Canvas App logic ensures that a receipt file must be attached before submission is triggered. If the … Continue reading Dynamic Expense Entry Submission with Receipt Attachments in Power Apps: A Practical Implementation for a Texas-Based Operational Technology Security Organization →

Summary 1. Built a unified “My Allocations” dashboard in Microsoft Dynamics 365 Project Operations for a project-based professional services organization. 2. Consolidated resource assignments, weekly hour breakdowns, and time entry management into a single custom view. 3. Eliminated the need to navigate between separate Project, Time Entry, and Calendar views for day-to-day tracking. 4. Enabled Practice Managers to switch between team members and instantly review billability without leaving the page. 5. Added inline time entry creation, submission, recall, and deletion all from one interface. 6. Introduced a consolidated calendar view showing all time entries across projects in a single month-at-a-glance layout. 7. Reduced clicks and screen-switching for both individual contributors and managers tracking team billability. Table of Contents Introduction The Business Problem The Solution One Dashboard for Allocations and Time Entries Real-Time Hours Consumption Tracking Inline Time Entry Management The Consolidated Calendar View The Practice Manager View, Billability at a Glance Security and Role-Based Access Business Impact Frequently Asked Questions Conclusion 1. Introduction For teams running project-based delivery on Microsoft Dynamics 365 Project Operations, a simple question, “How many hours are left on this task, and did I log time for it today?”, often takes far more clicks than it should. Resource assignments live in one view, time entries live in another, and consumption summaries require yet another. For project managers tracking billability across an entire team, the problem multiplies with every resource. To solve this, a custom “My Allocations” dashboard was built directly into Dynamics 365 as a web resource, bringing project assignments, weekly hour breakdowns, hours consumption, and time entry management into a single screen. No tab switching, no re-navigation; just one view that adapts to whether you’re an individual contributor or a manager overseeing a team. 2. The Business Problem In a typical Dynamics 365 Project Operations setup, resource assignments, time entries, and consumption reporting exist as separate entities, each with its own view or form. A consultant checking their weekly workload has to open one screen for assignments, another to log time, and a third to check whether they’re over or under budget on a task. For Practice Managers, this friction compounds. Reviewing billability across a team means repeating this multi-screen process for every resource, switching context, re-filtering views, and manually piecing together a picture of who’s on track and who isn’t. The Objective: Build a single, role-aware dashboard where any user can see their assignments, track hours consumed versus planned, and manage time entries; managers can do the same for any resource on the team, without leaving the page. 3. The Solution The “My Allocations” dashboard was designed around one core idea: everything a resource or manager needs for day-to-day tracking should live on one screen. Instead of navigating between the Project entity, the Time Entry list, and separate consumption reports, users get a consolidated view that surfaces assignments, hours, and entry management side by side. The sections below walk through each part of the dashboard and explain how it removes a specific step from the old multi-screen workflow. Figure 1: My Allocations dashboard overview showing summary cards, toolbar, and project hierarchy. 3.1 One Dashboard for Allocations and Time Entries At the top of the dashboard, summary cards provide an instant overview of active projects, active weeks, allocated hours, and assigned tasks. Instead of navigating through multiple Project Operations entities, users immediately understand their workload from a single screen. Each project expands into its assigned weeks, while every week further expands into a detailed day-by-day breakdown of allocated work. This hierarchical layout lets users drill down naturally, from the project level to weekly allocations and finally to individual daily assignments, without leaving the dashboard. By consolidating this information into one interface, the dashboard eliminates repetitive navigation and significantly reduces the time required to understand upcoming work. Figure 2: Project hierarchy showing projects, weekly allocations, and day-level task breakdown. 3.2 Real-Time Hours Consumption Tracking A dedicated Hours Consumption panel gives resources and managers real-time visibility into task progress. For every task, the dashboard displays planned hours, approved hours, submitted hours awaiting approval, remaining hours, and overall consumption using an intuitive progress indicator. Color-coded progress bars immediately communicate project health. Blue indicates healthy consumption, orange highlights tasks approaching their allocated budget, and red clearly identifies tasks that have exceeded planned effort. This removes the need to generate reports or manually compare planned and actual effort across multiple Project Operations views. Figure 3: Hours Consumption panel displaying planned, consumed, submitted, and remaining hours with visual progress indicators. 3.3 Inline Time Entry Management Every task includes built-in actions that allow users to create new time entries or review existing ones without leaving the dashboard. Instead of opening the standard Time Entry entity, users can complete the entire process from the same interface. The entry form captures all required information including work date, duration, role, and external comments. Users may either save entries as drafts or immediately submit them for approval depending on their workflow. Existing entries can also be reviewed, recalled, resubmitted, or deleted directly from the dashboard, significantly reducing navigation while simplifying daily time tracking. Figure 4: Inline Time Entry form used for creating and submitting project hours. Figure 5: Task calendar displaying existing time entries grouped by day and submission status. 3.4 The Consolidated Calendar View Beyond task-specific calendars, the dashboard provides a consolidated monthly calendar that displays every time entry recorded across all assigned projects. Users no longer need to inspect individual tasks separately to understand their monthly workload. Each calendar day displays the total hours logged together with the number of recorded entries. Color indicators provide an instant visual summary of each day’s dominant submission status. Selecting a day immediately displays every recorded time entry beneath the calendar, making monthly reviews significantly faster for both consultants and managers. Figure 6: Consolidated monthly calendar showing all time entries across projects for the selected resource. 3.5 The Practice Manager View – Billability at a Glance For Practice Managers, the dashboard extends beyond personal allocations by introducing … Continue reading Building a Unified My Allocations Dashboard in Microsoft Dynamics 365 Project Operations for an Industrial Cybersecurity Company in Texas →

This article demonstrates how to build a reusable Generic Data Deletion Utility in Microsoft Dynamics 365 Business Central that allows administrators and developers to safely delete individual records from any table using primary key values. Instead of creating separate utilities for different tables, this generic solution leverages RecordRef, FieldRef, and KeyRef to dynamically access Business Central tables at runtime. Summary Developed a generic data deletion utility for Microsoft Dynamics 365 Business Central. Enabled administrators to delete records from supported Business Central tables without creating table-specific code. Used RecordRef, FieldRef, and KeyRef to dynamically identify primary keys at runtime. Provided lookup functionality for selecting Business Central tables through the standard Object List. Added confirmation prompts before deletion to reduce accidental data loss. Designed the solution as a Processing Only report for administrative maintenance activities. Created a reusable framework that can be extended for future data maintenance utilities. Table of Contents 1. Introduction 2. The Business Problem 3. The Solution 3.1 Selecting the Business Central Table 3.2 Providing the Primary Key Values 3.3 Dynamically Identifying the Record 3.4 Confirming and Deleting the Record 3.5 Security and Permissions 4. Implementation 5. Business Impact 6. Frequently Asked Questions 7. Conclusion 1. Introduction Organizations in the pharmaceutical manufacturing industry frequently perform data cleanup activities during implementation, testing, data migration, and ongoing production support. Deleting specific records from Microsoft Dynamics 365 Business Central tables often requires custom-built utilities or direct database interventions, making the process time-consuming, less flexible, and difficult to maintain. To address this requirement, a Generic Data Deletion Utility was developed for a leading pharmaceutical manufacturing organization using Microsoft Dynamics 365 Business Central. By leveraging RecordRef, FieldRef, and KeyRef, the solution enables authorized users to dynamically locate and delete records from any Business Central table using primary key values without requiring table-specific deletion logic. This article explains the architecture of the solution, the AL programming concepts used, and how the framework provides a reusable and controlled approach for administrative data maintenance across standard and custom Business Central tables. 2. The Business Problem During implementation, testing, data migration, and production support activities, the organization frequently required the deletion of specific records from various Microsoft Dynamics 365 Business Central tables. Since each table has its own structure, fields, and primary key definitions, performing these deletions typically required custom-built utilities or temporary development efforts. This table-specific approach increased development effort, reduced operational efficiency, and made routine data maintenance activities more complex. The organization required a single reusable framework that could dynamically work with multiple Business Central tables without requiring separate deletion logic for each table. The Objective: Develop a generic data deletion utility that enables authorized users to safely identify and delete records from any Business Central table using primary key values while maintaining control and minimizing the risk of accidental data loss. 3. The Solution To simplify administrative data maintenance, a Generic Data Deletion Utility was developed in Microsoft Dynamics 365 Business Central. The solution provides a centralized utility that enables users to delete records from multiple tables without requiring separate deletion programs for each table. The tool allows authorized users to select a Business Central table, provide the required primary key values, locate the corresponding record dynamically, and delete it after user confirmation. The design supports both standard and custom Business Central tables while providing a flexible and reusable approach for controlled data management. The following sections describe the key components of the solution and explain how each feature contributes to making the tool dynamic, secure, and easy to maintain. 3.1 Selecting the Business Central Table The first step in the deletion process is selecting the Business Central table that contains the record to be removed. The request page provides fields for the Table Number and Table Name, allowing the user to identify the required standard or custom table. Instead of requiring users to manually remember table numbers, the Table No. field provides a drill-down option. This opens the standard All Objects with Caption page and filters the available objects to display only Business Central tables. TableObjects.SetRange( “Object Type”, TableObjects.”Object Type”::Table); if PAGE.RunModal( PAGE::”All Objects with Caption”, TableObjects) = Action::LookupOK then begin TableId := TableObjects.”Object ID”; TableCaption := TableObjects.”Object Caption”; end; Once a table is selected, the solution stores its object ID in the Table No. field and automatically displays the corresponding table name. This helps users verify that the correct table has been selected before providing the primary key values. The Table Name field is kept non-editable because its value is automatically retrieved from the selected Business Central table. Figure 1: Filtered Data Deletion request page for selecting the table and entering primary key values 3.2 Providing the Primary Key Values After selecting the required table, the user must provide the primary key values that uniquely identify the record to be deleted. Since each Business Central table has its own primary key structure, the solution supports primary key. The first key value is entered in the Primary Key field, while the Key 2 and Key 3 fields are available for tables that use multiple fields as part of their primary key. This enables the tool to locate records accurately regardless of the table structure. For example, a Customer record requires only the Customer No., whereas a Sales Line record requires multiple values such as the Document Type, Document No., and Line No. By supporting multiple key fields, the same utility can work across a wide range of standard and custom Business Central tables. Figure 2: Entering the primary key values required to uniquely identify a Business Central record. 3.3 Dynamically Identifying the Record Unlike conventional deletion utilities that are developed for a single table, this solution dynamically identifies records irrespective of the selected Business Central table. It uses the RecordRef, KeyRef, and FieldRef data types to work with table metadata at runtime, making the solution completely generic. After the user selects a table and enters the primary key values, the tool opens the selected table dynamically, retrieves its primary key definition, and applies … Continue reading Simplifying Record Management in Microsoft Dynamics 365 Business Central with a Generic Data Deletion Utility for Titan Labs →

Summary We built an AI-powered meeting briefing solution on top of Dynamics 365 CRM that assembles a complete, structured briefing – customer details, meeting history, email insights, professional profile, and open action items – with a single button click, delivered in under 30 seconds. Powered by Azure Functions and Azure OpenAI GPT-4.1, the solution sits inside the existing CRM workflow with zero change to the tools the team already uses. Built for private equity and asset management firms where missing context isn’t just inefficient – it’s a credibility cost. Table of Contents 01Summary 02Customer 03Challenge 04Solution 05Architecture 06Briefing 07Intelligence 08Enterprise 09Benefits 10Why PE About the Customer Customer Overview Our customer is a US based private equity investment firm focused on partnering with and growing businesses over the long term. With a portfolio of investments and a strong emphasis on operational excellence and value creation, the firm required an AI-powered solution to streamline client preparation, consolidate information from multiple sources, and enable investment professionals to make more informed decisions before meetings. The Challenge Data Exists. Context Doesn’t. Modern CRM platforms are excellent at storing information. On their own, they’re not built to assemble it into something a person can use in the next fifteen minutes. It’s 8:45 AM. Fifteen minutes before an important client call, a relationship manager has four tabs open – CRM, Outlook, LinkedIn, and a folder of old meeting notes – trying to reconstruct the relationship before the call starts. Nothing here is missing. It’s just scattered. And the fifteen minutes meant for preparing get spent finding what to prepare instead. As firms manage more relationships – more portfolio companies, more LPs, more prospects – this problem doesn’t stay the same size. It grows with every account added to the book. A typical prep routine still looks like this: 01Reviewing CRM records for account history 02Reading through recent email threads 03Searching old meeting notes for what was actually discussed 04Looking up a contact’s current role and background 05Trying to remember what was promised – and what’s still open Each source holds something useful on its own. None of them, alone, tells the whole story – and stitching them together by hand is what actually eats the morning. The result is inconsistent prep, lost productivity, and context that depends entirely on who happens to be covering the account that day. Today 01Manually researching each contact across CRM, LinkedIn, and email 02Meeting notes scattered across CRM records, shared drives, and inboxes 03Generic AI summaries that miss relationship context 04Key background often missed before the meeting even starts With AI-Powered Briefings 01A structured briefing generated in the team’s own template, on demand 02Contact details, professional background, and CRM data pulled automatically 03Past meetings and email threads summarized with real context 04The team arrives prepared in seconds, not hours Turning CRM Data Into Meeting Intelligence One Click. A Complete Briefing. We built an AI-powered meeting briefing solution that sits directly on top of Dynamics 365 CRM — adding a single button to the record teams already work from, with no change to the existing workflow underneath it. 01 Click The relationship manager clicks “AI Meeting Insights” on the contact or prospect record – the trigger for everything that follows. 02 Gather Azure Functions orchestrates the retrieval: CRM history, grouped email threads, past meeting notes, and the contact’s public professional profile. 03 Analyze Azure OpenAI GPT-4.1 reads everything chronologically, identifying what was discussed, what’s outstanding, and the tone of recent exchanges. 04 Deliver A formatted briefing, built in the team’s existing template, lands in minutes — a progress indicator keeps the user informed while it runs. In practice, the full sequence — from button click to finished document – runs in under 30 seconds. Solution Architecture How It Works — End to End From a single button click in Dynamics 365 to a formatted briefing delivered in under 30 seconds. Inside the Document What’s Actually in the Briefing Instead of a generic summary, the output is built around the fields teams actually need before walking into a conversation. 01 Customer & Contact Details Pulled directly from the CRM record — no re-typing, no re-checking. 02 Previous Meeting Summary Condensed from historical notes into a concise interactions record. 03 Recent Email Insights Key topics and sentiment from the latest correspondence. 04 Business Overview A short, current description of the company and its context. 05 Meeting Attendees Who’s expected in the room, drawn from calendar and CRM data. 06 Professional Profile Reference Publicly available background on the contact, added automatically. The Intelligence Behind the Briefing Structured, Not Just Summarized What makes this different isn’t simply the use of Generative AI — it’s the structure behind it. Rather than summarizing documents in isolation, the model reads a customer’s engagement history chronologically: completed and upcoming meetings, attendees, recent conversations, sentiment, and open action items, resolved into one coherent picture. Every section of the briefing is mapped back to a defined source. Customer details come from CRM. Meeting summaries come from meeting records. Communication insights come from email. Profile information is referenced separately and flagged as such. That traceability is what keeps the output contextual and consistent, rather than a plausible-sounding but generic AI summary. It’s also reviewed, not just trusted. Fields the model is less confident about are flagged for a human to check, and the team approves AI-generated sections before a briefing goes out. The goal isn’t to remove judgment from the process — it’s to remove the search that used to come before it. Built for the Enterprise Enterprise-Ready by Design This runs inside the systems and controls teams already trust — not alongside them. 01 Role-Based Access Visibility follows existing Dynamics 365 security roles — no new permission model to manage. 02 Data Protection Data is encrypted in transit, credentials sit in a secure Azure vault, and only the fields needed reach the model. 03 Graceful Failure Handling A failed run is flagged clearly, prior briefing data stays intact, and the user can … Continue reading How AI-Powered Meeting Briefings Are Transforming Client Preparation for a Boston based Private Equity Firm →

How to Use Trace Parser in Microsoft Dynamics 365 Finance & Operations Article  ·  Cloudfronts Summary Trace Parser is a Microsoft diagnostic tool that analyzes execution traces captured from Dynamics 365 Finance & Operations (D365 F&O) to help troubleshoot performance issues without traditional debugging. It records detailed information on X++ method execution, SQL queries, call stacks, execution time, user sessions, database interactions, and RPC calls. Traces are captured directly from the D365 F&O application UI, saved as .aet files, and then opened and analyzed in the Trace Parser desktop application. The tool’s Sessions, Call Tree, SQL Statements, and Timeline views make it possible to pinpoint slow forms, long-running SQL queries, and inefficient X++ code. A real-world example shows Sales Order posting time reduced from 40 seconds to 8 seconds after identifying and fixing a looped validation method using Trace Parser. Following best practices — short, targeted traces and before/after comparisons — makes analysis faster and results more reliable. Table of Contents 01 Summary 02 Introduction 03 What is Trace Parser? 04 Why Use Trace Parser? 05 When Should You Use Trace Parser? 06 Prerequisites 07 Capturing and Opening a Trace 08 Understanding the Trace Parser Interface 09 Analyzing Performance Issues 10 Common Performance Problems 11 Best Practices, Limitations & Tips 12 Real-World Example 13 Conclusion Introduction Performance issues and unexpected system behavior can be challenging to troubleshoot in Microsoft Dynamics 365 Finance & Operations (D365 F&O). While debugging X++ code is useful during development, it is often not possible in Sandbox or Production environments. This is where Trace Parser becomes an invaluable diagnostic tool. Trace Parser captures detailed execution information, allowing developers and support engineers to analyze application performance, identify slow processes, review SQL queries, and understand the execution flow of X++ code. In this blog, you’ll learn what Trace Parser is, when to use it, how to capture a trace, and how to analyze the results effectively. What is Trace Parser? Trace Parser is a Microsoft diagnostic tool used to analyze execution traces generated by D365 Finance & Operations. It records detailed information about: X++ method execution SQL queries Call stacks Execution time User sessions Database interactions RPC calls Unlike traditional debugging, Trace Parser helps analyze issues after they occur by reviewing a captured trace file. Why Use Trace Parser? Trace Parser is commonly used to: Investigate slow forms and reports Identify long-running SQL queries Analyze batch job performance Detect inefficient X++ code Find excessive database calls Troubleshoot performance bottlenecks Understand application execution flow When Should You Use Trace Parser? Consider using Trace Parser in scenarios such as: A form takes too long to open. A report is running slowly. A batch job is consuming excessive time. A custom process performs poorly after deployment. Users report intermittent performance issues. You need to identify the exact SQL query causing delays. Prerequisites Before capturing a trace, ensure you have: Access to the D365 F&O environment Permission to use Trace functionality Trace Parser installed (typically on a development VM) A reproducible scenario Capturing and Opening a Trace 1 Step 1 Enable Tracing In D365 Finance & Operations: Sign in to the application. Click the Question Mark icon. Open the Trace tab. Click Start Trace. The system will begin recording user activities. Tip: Only capture the specific business process you want to analyze. Long traces create large files and are harder to analyze. 2 Step 2 Reproduce the Issue Perform only the actions related to the issue, for example: Open the problematic form Run the report Execute the batch job Perform the slow business process Avoid unrelated activities during tracing. 3 Step 3 Stop the Trace Once the scenario is complete: Return to the Trace tab. Click Stop Trace. Save the generated trace file (.aet). This file contains all recorded execution details. 4 Step 4 Open Trace Parser Launch the Trace Parser application on your development machine. Go to File → Open Trace. Choose the saved .aet file. Trace Parser will import and process the trace, which may take a few minutes depending on the file size. Open Trace dialog” src=”https://www.cloudfronts.com/wp-content/uploads/2026/07/1-image4.png”> Understanding the Trace Parser Interface After loading the trace, you’ll see several sections: SessionsDisplays all captured user sessions. Useful for identifying the correct user, filtering traces, and analyzing specific requests. Call TreeShows the hierarchy of X++ method calls, including which methods were executed, parent-child relationships, and time spent in each method. SQL StatementsDisplays all SQL queries executed during the trace, useful for identifying long-running queries, missing indexes, repeated calls, and excessive SELECTs. TimelineShows the execution flow over time, making it easier to identify performance spikes, waiting periods, and expensive operations. Analyzing Performance Issues When reviewing a trace, focus on: 1 Focus Area 1 Long-Running Methods Sort methods by execution time. Look for: High execution duration Frequent method calls Recursive methods 2 Focus Area 2 SQL Execution Time Check: Query duration Number of executions Table scans Repeated queries Repeated SQL queries often indicate inefficient code. 3 Focus Area 3 Excessive Database Calls Example — instead of calling CustTrans::find() inside a while select loop over custTable, consider reducing repeated database calls using joins, caching, or optimized queries. 4 Focus Area 4 Nested Loops Deep nested loops can significantly impact performance. Optimize by: Reducing iterations Using set-based operations Minimizing database access inside loops Common Performance Problems Identified by Trace Parser # Issue Recommendation 1 Repeated SQL queries Cache data or combine queries 2 Long-running methods Optimize business logic 3 Excessive RecIds lookups Use joins where appropriate 4 Full table scans Review indexes and filtering 5 Nested loops Refactor using set-based operations 6 Slow report execution Optimize queries and data providers Best Practices, Limitations & Tips Best Practices Capture only the required scenario. Keep traces short. Test in a Sandbox or development environment whenever possible. Compare traces before and after code changes. Archive traces for future reference. Avoid tracing during peak business hours unless necessary. Limitations Trace Parser is a powerful tool, but it has some limitations: Large trace files require more time to process. … Continue reading How a Leading North American Commercial Vehicle Manufacturer Optimized Sales Order Posting Using Trace Parser →

Microsoft Fabric Part 2 — Data Transformation, Real-Time Reporting and AI for D365 Summary With D365 data landed in Bronze (covered in Part 1), this blog covers the next three stages — transformation through a Medallion architecture, Direct Lake reporting in Power BI, and conversational AI via the Fabric Data Agent. Two generic PySpark notebooks — bronze_to_silver and silver_to_gold — transform raw Bronze data into cleansed Silver and business-ready Gold tables without entity-specific code. A Direct Lake semantic model built on the Gold layer gives Power BI real-time reporting without an import step or scheduled refresh cycle. The built-in Fabric Data Agent, grounded on the Gold layer, gives business users natural-language access to governed data — no separate AI platform, no additional licensing. The result is a complete data and AI layer — ingestion, transformation, reporting, and conversational AI — built on a single platform, driven by configuration, and designed to grow. Table of Contents 01 Quick Recap — Part 1 02 Technical Deep-Dive 03 Business Impact 04 Frequently Asked Questions 05 Conclusion Quick Recap — Part 1 In Part 1, we covered how a config-driven ingestion framework on Microsoft Fabric pulls data from any D365 Finance & Operations entity and lands it into the Bronze layer of a Fabric Lakehouse — using just two generic pipelines and a master CSV config file. No entity-specific code, no new pipeline per entity, and reliable incremental upsert loading driven entirely by configuration. Bronze is the raw layer — data arrives exactly as it comes from D365, unmodified. That is intentional. The Bronze layer is not for reporting. It is the foundation — a reliable, auditable record of everything that came in. What happens next is where the data becomes useful. Where Part 2 picks up Bronze Layer (raw — covered in Part 1)  →  Silver Layer (cleansed)  →  Gold Layer (business-ready)  →  Power BI (Direct Lake reporting)  +  Fabric Data Agent (conversational AI) This part walks through how two generic PySpark notebooks transform Bronze data into clean, business-ready Gold tables, how a Direct Lake semantic model exposes that Gold layer to Power BI without a refresh cycle, and how the built-in Fabric Data Agent gives business users conversational access to the same governed data. Technical Deep-Dive Bronze → Silver — Cleansing and Column Mapping The bronze_to_silver notebook is a parameterised PySpark notebook that promotes raw Bronze data into cleansed Silver tables. For each entity it: 1Reads the Bronze table for the configured entity 2Strips technical column prefixes added by D365 3Consults the b2s_columnconfig table to determine which columns are active and how they should be aliased 4Applies type casting and standardisation 5Writes the result to the Silver schema The same notebook promotes any Bronze table to Silver — changing the entity parameter is all it takes to onboard a new one. bronze_to_silver — strips prefixes, reads column config, applies aliasing. One notebook handles every Bronze-to-Silver promotion Silver → Gold — Business Logic and Joins The silver_to_gold notebook builds business-ready Gold tables by joining multiple Silver entities and applying business logic. For the Resource Time Tracking Gold table it: 1Joins six Silver entities — bookableresources, msdyn_timeentries, msdyn_projecttasks, msdyn_projects, accounts, and msdyn_transactioncategories 2Derives fields including Category, EntryDate, Weekday, TimeSheetStatus, and TimeSpent 3Translates D365 status codes into readable values 4Writes the result as a Delta table to the Gold schema The Gold table is the single, trusted, business-ready version of the data — the only layer exposed to reporting and AI consumers. silver_to_gold — joins Silver entities, applies business logic, and writes a clean Gold table ready for reporting and AI querying Direct Lake Semantic Model — Creation Once the Gold layer is ready, a Direct Lake semantic model is created directly from the Lakehouse home screen. The setup follows three simple steps: 1Click New semantic model from the Lakehouse toolbar 2Select only the Gold table — Bronze and Silver remain hidden from report authors 3Confirm — the model is created in Direct Lake mode, reading Delta Parquet files directly from OneLake No import step. No scheduled refresh. No data duplication. Only Gold is selected for the semantic model — reporting consumers never see raw or intermediate data Direct Lake Semantic Model — Power BI The finished Resource Time Tracking semantic model exposes Gold-layer fields ready for report authoring in Power BI: Category, Customer, EntryDate, ProjectName, ProjectTask ProjectType, ResourceName, TimeSheetStatus, TimeSpent Weekday, Week Number, Year Because it runs in Direct Lake mode, reports always reflect the latest Gold data without anyone needing to trigger a refresh. The finished semantic model in Power BI — Gold fields available for report authoring immediately, with no refresh cycle required Fabric Data Agent Beyond traditional BI, the built-in Fabric Data Agent extends the framework into conversational AI. Key characteristics of the agent: Grounded exclusively on the Gold layer — only curated, business-ready data is exposed Supports natural-language questions — trend analysis, outlier detection, resource summaries, and more Runs within the same Fabric workspace — no separate AI platform, no additional licensing, no integration work Business users and data teams access the same governed data whether they use Power BI or the agent The Fabric Data Agent grounded on the Gold layer — business users ask questions in plain English on the same data that powers Power BI Business Impact 1Consistent transformation governance — two notebooks enforce the same cleansing and business logic across every entity, from Bronze through Gold, with no per-entity exceptions or inconsistencies 2Near real-time reporting — Direct Lake semantic models read Gold Delta tables directly from OneLake, eliminating the import and refresh cycle that traditional Power BI datasets require 3Single trusted layer for all consumers — both Power BI reports and the Data Agent draw from the same governed Gold layer, ensuring consistent numbers across structured reporting and conversational queries 4Built-in AI access at no extra infrastructure cost — the Fabric Data Agent gives business users conversational access to governed data without a separate AI platform, additional licensing, or integration work 5End-to-end traceability — every … Continue reading Microsoft Fabric Part 2: Where Raw Data Becomes Business Intelligence and Conversational AI →

Microsoft Fabric Part 1 — Config-Driven D365 Ingestion into the Lakehouse Summary Building a new pipeline for every new data entity is one of the most common and quietly expensive habits in enterprise data engineering — this blog shows how to eliminate it entirely. A config-driven ingestion framework on Microsoft Fabric pulls data from any D365 Finance & Operations entity into the Bronze layer of a Fabric Lakehouse using just two generic pipelines and one master CSV config file. Adding a new entity requires no new pipeline, no new code, and no deployment — just a single row added to a configuration file. The framework handles OAuth authentication, OData pagination at 5,000 records per page, incremental watermark filtering, and key-based upsert loading — all driven by configuration. This is Part 1 of a two-part series. Part 2 covers transformation through Bronze → Silver → Gold, Direct Lake reporting in Power BI, and conversational AI via the Fabric Data Agent. Table of Contents 01 Let’s Start Here 02 The Challenge 03 The Solution 04 Technical Deep-Dive 05 Business Impact 06 Conclusion Let’s Start Here Every data engineering team eventually hits the same wall. You build a pipeline for one entity — opportunities, invoices, time entries. It works well. Then another entity gets added, and another. Before long, you have a collection of pipelines that each do roughly the same thing but are written differently, maintained separately, and break in different ways. The question is not whether this happens — it always does. The question is whether your framework is designed to prevent it from the start. This is Part 1 of a two-part series on building an end-to-end data engineering framework on Microsoft Fabric connected to Dynamics 365. Before we get into the detail, here is how the full architecture fits together: The Full Architecture — D365 to AI D365 / F&O  →  Bronze Layer (raw ingestion — this blog)  →  Silver Layer (cleansed)  →  Gold Layer (business-ready)  →  Power BI Reports + Fabric Data Agent Part 1 covers the ingestion step — pulling data from D365 into the Bronze layer of the Fabric Lakehouse using a config-driven pipeline framework. Part 2 covers everything after Bronze — transformation through Silver and Gold, Direct Lake reporting in Power BI, and conversational AI through the Fabric Data Agent. The Challenge Traditional data engineering approaches treat each entity as a unique problem. A pipeline is built for accounts, another for contacts, another for time entries. Each has its own authentication logic, its own pagination handling, its own incremental-load approach. This creates a set of problems that compound over time: 1Onboarding a new entity requires a new pipeline build, test cycle, and deployment — work that can take days 2Incremental load logic is duplicated across pipelines, often inconsistently, leading to missed records or duplicates 3When upstream systems change — authentication, API structure, column names — the blast radius is wide 4There is no single place to look to understand what is being ingested and how The answer is not better pipelines. It is a framework where the pipeline is generic and the entity-specific details live in configuration. The Solution — Config-Driven Ingestion on Microsoft Fabric The ingestion layer of this framework runs entirely from a single Fabric workspace containing one Lakehouse, two pipelines, and one master configuration file. Every entity, its key column, its watermark column, and its source details live in a CSV — not in pipeline code. The Lakehouse is structured using a Medallion architecture — three table layers: Bronze (raw data exactly as it arrives from D365), Silver (cleansed and standardised), and Gold (business-ready, joined, and logic-applied). The ingestion framework is responsible for the first step — getting data from D365 into the Bronze layer reliably, incrementally, and without entity-specific code. The Fabric workspace — one Lakehouse, two pipelines, and a config file that together handle ingestion for any number of D365 entities Adding a new entity to the framework means adding one row to a CSV. No new pipeline. No new deployment. No code change. Technical Deep-Dive The Lakehouse and Config Files Inside CRM_Lakehouse, the Tables area is organised by Medallion layer. The Files area holds the configuration CSVs that drive every pipeline and notebook. Three files do all the work: ingestion.csv — controls which entities are ingested, how they are filtered, and where they land b2s_columnconfig.csv — controls Bronze-to-Silver column mapping, aliasing, and type casting gold_fixed.csv — controls Gold-layer business logic and join definitions The Lakehouse holds both the data layers and the config files that drive every pipeline and notebook — everything in one place The Master Config — ingestion.csv Every entity is described in a single row of ingestion.csv. Each row contains: Entity name and OData logical name — what to call and where to find it in D365 Primary key column — used for upsert to prevent duplicates Incremental filter column — the watermark field used to fetch only changed records Partition key — supports multi-source ingestion Checkpoint — stores the last watermark value applied so each run picks up exactly where the last one left off The pipeline reads this file, builds its incremental filter dynamically, and upserts records using the configured key. No entity-specific code exists anywhere. ingestion.csv — every entity is a single self-describing row. Adding a new D365 entity is a config change, not a code change The Trigger Pipeline The Data Ingestion Trigger pipeline is the orchestrator. It works in three steps: 1Lookup — reads ingestion.csv and returns the full list of configured entities 2ForEach — loops through every entity row in the config 3Invoke Pipeline — calls the Entity pipeline once per row, passing the full config object as a parameter One lightweight orchestrator pipeline controls an unlimited number of entities — no changes needed when a new entity is added. The Trigger pipeline — Lookup config, loop through entities, invoke the Entity pipeline once per row The Entity Pipeline The Data Ingestion Entity pipeline is the reusable worker. It … Continue reading Microsoft Fabric Part 1: Building a Config-Driven Data Ingestion Framework for Dynamics 365 →

Summary Designed and deployed a mobile-first Power Apps Canvas App for a Texas-based industrial cybersecurity firm specializing in operational technology (OT) security for oil and gas infrastructure. Unified time tracking, expense management, material consumption logging, and approvals into a single experience integrated with Dynamics 365 Project Operations. Eliminated fragmented desktop-based workflows that delayed project reporting, approvals, and billing. Automated expense receipt processing through Power Automate, improving compliance and reducing manual effort. Implemented project-scoped approval routing to ensure submissions were reviewed only by authorized stakeholders. Enabled real-time project visibility through structured Dataverse-driven workflows and lifecycle tracking. Provided mobile approvals and submission monitoring, dramatically reducing turnaround times. Improved data accuracy, audit readiness, and billing efficiency across field operations. Table of Contents Introduction Requirement & Business Scenario Solution Implementation Implementation Gallery Outcome FAQs Conclusion 1. Introduction Field-driven organizations live and die by the accuracy and speed of their project data. For a company securing critical infrastructure like oil rigs, every hour an engineer spends fighting with a clunky time-entry screen is an hour not spent on the job site — and every delayed expense submission is a delay in client billing and financial reporting. This is the story of how a Texas-based cybersecurity firm moved away from a fragmented, desktop-oriented workflow inside Dynamics 365 Project Operations and adopted a unified, mobile-first Canvas App that brought time tracking, expense submission, and material logging into one place, with built-in compliance controls and project-specific approval routing. The Goal: Build a unified mobile-first experience that allows field engineers to submit time, expenses, and materials from anywhere while ensuring compliance, controlled approvals, and real-time project visibility. 2. Requirement & Business Scenario The firm manages multiple concurrent field engagements using Dynamics 365 Project Operations as its system of record. Consultants and field engineers were expected to log three categories of activity against active projects: Time entries for hours worked Expense entries covering travel, accommodation, airfare, and related costs Material usage logs for equipment, parts, and consumables The core issue was that the underlying system was built for desktop use, not for engineers working on-site at remote rig locations. This created several compounding problems: Field staff had no efficient way to submit entries from a mobile device, so submissions piled up until they were back at a desk. Time, expense, and material tracking lived in separate workflows, forcing users to context-switch between screens for what should have been a single daily task. Expense compliance was inconsistent — receipts were sometimes attached, sometimes forgotten, and the process for linking a receipt to an expense record involved several manual, error-prone steps behind the scenes. Approvals had no project-level boundaries, making it hard to guarantee that only the right project stakeholders could review and approve specific submissions. Project managers lacked real-time visibility into resource usage, which meant billing and client reporting cycles were consistently delayed. Left unaddressed, these gaps were directly affecting data accuracy, audit readiness, and the speed at which the business could invoice clients. 3. Solution CloudFronts designed a unified mobile experience using Power Apps Canvas Apps layered on top of Dynamics 365 Project Operations and Dataverse, built around one guiding principle: One App. All Submissions. Controlled Approvals. Real-Time Visibility. For field users, the app became the single place to submit time entries on a daily or weekly basis, create expense entries with automatic receipt handling, log material consumption against the correct project, and track the live status of every submission. For project approvers, the same app surfaced only the entries tied to projects they were actually responsible for, let them approve or reject submissions directly from their phone, and preserved a clean, audit-ready trail for every decision. Day Mode and Week Mode Users could switch between a detailed single-day entry view, useful for precise logging and corrections, and a bulk weekly view that sped up repetitive data entry — letting each person work the way that suited their role. Calendar-Based Swipe Navigation A Dynamics-style calendar with swipe gestures let users move quickly across days and weeks, reviewing or correcting historical entries without friction. Stage-Aware Interface Every record followed the same lifecycle — Submitted, Pending, Approved, Rejected, Recall Requested, Recall Approved, Recall Rejected — and the UI adapted to whatever stage a record was in. Action buttons such as Submit, Approve, Reject, and Recall only appeared when they were actually valid, significantly reducing user confusion and accidental actions. Conditional Receipt Enforcement Rather than requiring a receipt for every expense category, the app applied compliance rules selectively. Receipts were mandatory for airfare and OT hardware purchases, while remaining optional for lower-risk categories such as meals and local transportation. 4. Implementation The technical implementation centered on a unified Dataverse data model and a set of automations that removed manual work from both the field user and the back office. Unified Data Model Time, expense, and material entries were all structured in Dataverse and linked back to the relevant project, resource, approval record, and — for expenses — supporting documentation. Every submission created a record with a clearly defined lifecycle stage, ensuring all three entry types behaved consistently even though their underlying business logic differed. Validation Before Submission The Canvas App enforced field-level validation before allowing a record to be saved, checking that essentials such as transaction date, project, category, quantity, and cost information were populated. If( Or( IsBlank(DatePicker.SelectedDate), IsBlank(ProjectCombobox.Selected), IsBlank(CategoryCombobox.Selected), IsBlank(QuantityInput.Value), IsBlank(PriceInput.Value) ), Notify(“Required fields are missing.”, NotificationType.Error) ) Patch-Based Record Creation Once validation passed, the application used Dataverse Patch operations to create records and calculate derived values such as expense subtotals dynamically based on quantity and unit price. Automated Receipt Handling For expense submissions, the previously manual chain of creating an Expense Receipt record, attaching a file as a Note, converting it to the correct document format, and updating the status to Submitted was fully automated. The Canvas App passed the expense ID, file name, and file content to a Power Automate flow, which created the Expense Receipt record, stored the file as a Note (Annotation) with the correct MIME type, … Continue reading How an Industrial Cybersecurity Company in Texas Improved Field Time and Expense Tracking with Microsoft Power Apps and Dynamics 365 Project Operations →

Summary Large manufacturing companies depend on Material Requirements Planning (MRP) to manage demand, supply, inventory, procurement, and production. In many organizations, planning data is spread across ERP systems, legacy applications, spreadsheets, and manufacturing systems, making it difficult to answer a critical question: Will we have the right material at the right time? A centralized MRP reporting solution built using Dynamics 365 and Power BI provides a single view of demand, inventory, procurement, production, and supplier performance. The result is better visibility, faster planning decisions, reduced manual effort, and improved supply chain performance. Table of Contents Customer Spotlight The Challenge The Solution Executive Supply Chain Overview Detailed MRP Analysis MRP Trends Over Time Inventory Optimization & Supplier Performance Production Planning Insights Business Impact FAQs Conclusion Customer Spotlight A Large Manufacturing Company in North America The organization manages complex manufacturing operations involving procurement, inventory, warehousing, production planning, and supplier management. Their planning environment includes: Large volumes of customer demand Thousands of raw material items Multiple suppliers and sourcing channels Complex production schedules Inventory distributed across warehouses and plants The Challenge The challenge was not a lack of data but having too much disconnected data across multiple systems. Planning teams needed answers to questions such as: Which materials may cause production shortages? Is demand data accurate? Are materials being ordered at the correct time and quantity? Which items are overstocked or understocked? Which suppliers are causing delays? Can production begin without material shortages? Which items require urgent planner attention? The Solution The solution combines Dynamics 365 planning data with Power BI reporting capabilities. Demand from sales orders and forecasts Inventory and on-hand balances Planned purchase orders Planned production orders Planned transfer orders Purchase orders and supplier data Bills of Materials (BOM) Production routes Lead times Safety stock parameters Executive Supply Chain Overview The dashboard provides a high-level view of supply chain performance and enables filtering by Site, Warehouse, Item, Supplier, Planner, and Date. Demand Visibility Demand vs Supply Inventory Health Stock & Shortages Supplier Metrics OTIF & Delays Detailed MRP Analysis The dashboard helps planners understand why MRP generated a recommendation. Item details and inventory balances Demand and supply transactions Planned orders Net requirements Lead times Order quantities MRP exception messages MRP Trends Over Time The trend dashboard enables proactive planning by highlighting: Demand changes over time Inventory movement trends Material shortages Purchase order delays Forecast accuracy Inventory Optimization & Supplier Performance The objective is simple: Right Material, Right Place, Right Time. The dashboard identifies: Items below safety stock Excess inventory Slow-moving inventory Location-based shortages Inventory in transit Supplier performance is measured using: On-time delivery OTIF (On Time In Full) Lead-time performance Supplier delays Open purchase orders Production Planning Insights This dashboard connects production planning with material planning. Production order status Material availability Capacity constraints Work-In-Progress (WIP) Production delays Business Impact Before After Data spread across systems Centralized visibility Manual reporting Automated insights Late issue detection Early issue identification Reactive planning Proactive decision-making Disconnected processes Connected planning view Limited executive visibility Real-time dashboards FAQs 1. Does Dynamics 365 support MRP? Yes. Dynamics 365 supports Material Requirements Planning and automatically generates planned orders based on demand and supply. 2. Why use Power BI? Power BI transforms planning data into actionable dashboards and visual insights. 3. What data is typically included? Inventory, sales orders, forecasts, purchase orders, suppliers, planned orders, and production data. 4. Can legacy planning systems be integrated? Yes. External planning and demand data can be consolidated into the reporting solution. 5. Can shortages and supplier performance be tracked? Yes. Dashboards can track shortages, supplier reliability, OTIF, and delivery performance. Conclusion MRP is not simply about generating planned orders. It is about making better business decisions. When Dynamics 365 planning data is combined with Power BI analytics, organizations gain visibility into demand, inventory, procurement, supplier performance, and production readiness. Instead of asking “Why did production stop?”, organizations can focus on “What should we act on today to prevent tomorrow’s disruption?” The result is a more proactive and data-driven approach to manufacturing planning that improves service levels, reduces risk, and enhances operational performance.

Summary Automated end-to-end contract generation, digital signing, and document filing for an Australia-based commercial linen and garments company using Dynamics 365 Sales, Microsoft Power Automate, and Adobe Acrobat Sign. Eliminated manual contract preparation by generating personalized Word contracts directly from accepted Dynamics 365 Quotes using a reusable Word template. Leveraged Adobe Acrobat Sign text tags embedded within the Word template to automatically create signature, date, and fillable fields without manual field placement or custom development. Automated agreement creation, customer notifications, and real-time signing status tracking through Adobe Acrobat Sign, providing complete visibility throughout the contract lifecycle. Implemented a dedicated child Power Automate flow that automatically identified completed agreements from Adobe Sign emails and archived signed contracts into the correct SharePoint document library. Reduced contract turnaround from a manual, multi-step process to a one-click, fully automated workflow while ensuring audit-ready signed documents and eliminating manual document handling. Table of Contents Introduction Business Challenge Procedure End-to-End Flow Why This Approach Works Conclusion Introduction For any business that runs on contracts — service agreements, quotes-turned-orders, vendor sign-offs — the gap between "quote accepted" and "contract signed" is often where deals slow down. Manual document preparation, back-and-forth emails, chasing signatures, and manually filing signed copies all eat into time that should be spent serving the customer. For an Australia based Linen and Garments, a commercial textile services company, CloudFronts built an end-to-end automation that takes a sales quote all the way through to a fully signed, filed contract — with zero manual document handling in between. The solution combines a Word contract template, Microsoft Power Automate, and Adobe Acrobat Sign, orchestrated across two connected flows: a parent flow that creates and sends the contract, and a child flow that listens for the signed response and files it automatically. This post walks through how that solution works, including the one detail that makes the whole thing possible without any custom code: Adobe Sign text tags embedded directly inside the Word template. The Business Challenge Once a quote is accepted, the team needed the resulting contract to: Be generated automatically from the quote and its line items — no manual copy-pasting of customer details into a Word document. Be sent for signature immediately, with the right fields ready for the customer to fill in and sign — bank details, account information, and a signature block, all in the right place. Notify both the customer and the internal Adobe Sign account holder the moment it’s out for signing. Automatically file the final, fully signed PDF back into the correct SharePoint location tied to that quote — without anyone needing to remember to save it. Doing this by hand across multiple people and mailboxes was slow and error-prone. The goal was to make the entire journey — quote to signed, filed contract — happen in minutes, with no manual document work at any step. Procedure Step 1: Auto-Generating the Contract from the Quote The process starts with a single action: Create Contract. This triggers the parent Power Automate flow, which: Composes the Quote ID from the selected record. Retrieves the document location tied to that quote (the Word contract template). Pulls the Quote, the associated Customer, and the Contact record for the signer. Uses these to populate a Word template — the standard “Populate a Word Template” merge step — filling in customer name, contract terms, line items, and contact details automatically. This is the same idea used in most contract-automation flows: merge structured CRM/quote data into a pre-built Word template, so the resulting document is fully personalized without a single manual edit. Step 2: Making the Contract Signable — Text Tags in the Template This is the step that makes the entire signing experience work, and it's worth explaining properly, because it's easy to get wrong. A merged Word document, by itself, is just static text. For Adobe Acrobat Sign to know where a customer needs to sign, initial, or fill something in, the template needs special markers called text tags — plain text strings embedded directly into the Word template before it's ever merged. When the finished document is sent to Adobe Sign, Adobe automatically scans it, finds these tags, and converts them into live, interactive fields for the signer. For the contract, the template includes tags like: {{Customer_Sign_es_:signer1:signature}} {{Date_Of_Signature_es_:signer1:date}} {{Financial_Institution_es_:signer1}} {{BSB_Number_es_:signer1}} {{Account_Name_es_:signer1}} {{Account_Number_es_:signer1}} Each tag follows Adobe's syntax: a field name, the _es_ identifier, the signer role (signer1), and an optional field type (signature, date, or left blank for a plain fillable text box). Because these tags are just text, they can sit anywhere in the Word template exactly where the business wants the field to appear — no separate field-placement tool required. Getting this right matters more than it looks. A few lessons learned building this out: The entire tag must stay on a single line and in a common font — if it wraps across a line break during merge or PDF conversion, Adobe won’t recognize it, and the raw tag text stays visible instead of becoming a field. Field type directives are limited to what Adobe actually supports (signature, date, initials, etc.) — leaving the type off entirely creates a plain fillable text field, which is what was used for the banking detail fields here. Converting the merged Word document to PDF before sending it to Adobe Sign tends to produce more consistent tag detection than sending the raw .docx. Because the tags are static text baked into the template, no extra configuration is needed in Power Automate to "activate" detection — it happens automatically the moment the document is sent to Adobe Sign for signature. Step 3: Sending the Contract for Signature Once the Word template is fully populated, the flow hands it off to Adobe Acrobat Sign using the Create an agreement from a file content and send for signature action — passing the merged file straight through, along with the signer's name, email, and role. At this point, two things happen simultaneously: a) The customer's Contact person receives … Continue reading From Quote to Signed Contract in Minutes: Automating Adobe Acrobat Sign Integration for an Australia based Linen and Garments company →