Summary A global manufacturing client was facing rising middleware costs, poor visibility, and growing pressure to support analytics and AI initiatives. A forced three-year middleware commitment became the trigger to rethink their integration strategy. This article shares how the client moved away from legacy middleware, reduced integration costs by nearly 95%, improved operational visibility, and built a strong data foundation for the future. Table of Contents 1. The Middleware Cost Problem 2. Building a New Integration Setup 3. Making Integrations Visible 4. Preparing Data for AI 5. How We Did It Savings Metrics The Middleware Cost Problem The client was running critical integrations on a legacy middleware platform that had gradually become a financial and operational burden. Licensing costs increased sharply, with annual fees rising from $20,000 to $50,000 and a mandatory three-year commitment pushing the total to $160,000. Despite the cost, visibility remained limited. Integrations behaved like black boxes, failures were difficult to trace, and teams relied on manual intervention to diagnose and fix issues. At the same time, the business was pushing toward better reporting, analytics, and AI-driven insights. These initiatives required clean and reliable data flows that the existing middleware could not provide efficiently. Building a New Integration Setup Legacy middleware and Scribe-based integrations were replaced with Azure Logic Apps and Azure Functions. The new setup was designed to support global operations across multiple legal entities. Separate DataAreaIDs were maintained for regions including TOUS, TOUK, TOIN, and TOCN. Branching logic handled country-specific account number mappings such as cf_accountnumberus and cf_accountnumberuk. An agentless architecture was adopted using Azure Blob Storage with Logic Apps. This removed firewall and SQL connectivity challenges and eliminated reliance on unsupported personal-mode gateways. Making Integrations Visible The previous setup offered no centralized monitoring, making it difficult to detect failures early. A Power BI dashboard built on Azure Log Analytics provided a clear view of integration health and execution status. Automated alerts were configured to notify teams within one hour of failures, allowing issues to be addressed before impacting critical business processes. Preparing Data for AI With stable integrations in place, the focus shifted from cost savings to long-term readiness. Clean data flows became the foundation for platforms such as Databricks and governance layers like Unity Catalog. The architecture supports conversational AI use cases, enabling questions like “Is raw material available for this production order?” to be answered from a unified data foundation. As a first step, 32 reports were consolidated into a single catalog to validate data quality and integration reliability. How We Did It Retrieve config.json and checkpoint.txt from Azure Blob Storage for configuration and state control. Run incremental HTTP GET queries using ModifiedDateTime1 gt [CheckpointTimestamp]. Check for existing records using OData queries in target systems with keys such as ScribeCRMKey. Transform data using Azure Functions with region-specific Liquid templates. Write data securely using PATCH or POST operations with OAuth 2.0 authentication. Update checkpoint timestamps in Azure Blob Storage after successful execution. Log step-level success or failure using a centralized Logging Logic App (TO-UAT-Logs). Savings Metrics 95% reduction in annual integration costs, from $50,000 to approximately $2,555. Approximately $140,000 in annual savings. Integrations across D365 Field Service, D365 Sales, D365 Finance & Operations, Shopify, and SQL Server. Designed to support modernization of more than 600 fragmented reports. FAQs Q: How does this impact Shopify integrations? A: Azure Integration Services acts as the middle layer, enabling Shopify orders to synchronize into Finance & Operations and CRM systems in real time. Q: Is the system secure for global entities? A: Yes. The solution uses Azure AD OAuth 2.0 and centralized key management for all API calls. Q: Can it handle attachments? A: Dedicated Logic Apps were designed to synchronize CRM annotations and attachments to SQL servers located behind firewalls using an agentless architecture. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.

In a world where timing, accuracy, and coordination make or break profitability, modern project-based enterprises demand more than isolated systems. You may be leveraging Dynamics 365 Project Operations (ProjOps) to manage projects, timesheets, and resource planning and Dynamics 365 Finance & Operations (F&O) for financials, billing, and accounting. But without seamless integration, you’re stuck with manual transfers, data silos, and delayed insights.  That’s where PO F&O Connector app comes in built to synchronize Project Operations and F&O end-to-end, bringing together delivery and finance in perfect alignment. In this article, we’ll explore how it works, why it matters to CEOs, CFOs, and CTOs, and how adopting it gives you a competitive edge.  The Pain Point: Disconnected Project & Finance Workflows  When your project execution and financial systems aren’t talking:  The result? Missed revenue, resource inefficiencies, and poor visibility into project financial health.  The Solution: Cloudfronts Project-to-Finance Integration App  Cloudfronts new app is purpose-built to connect Project Operations → Finance & Operations seamlessly, automating the flow of project data into financial systems and enabling real-time, consistent delivery-to-finance synchronization. Key capabilities include:  Role  Core Benefits  Outcomes  CEO  Visibility into project margins and outcomes; faster time to value  Better strategic decisions, competitive agility  CFO  Automates billing, enforces accounting rules, ensures audit compliance  Revenue gets recognized faster, finance becomes a strategic enabler  CTO  Reduces custom integration burdens, ensures system integrity  Lower maintenance costs, scalable architecture  Beyond roles, your entire organization benefits through:  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

When integrating Dynamics 365 with external systems, one of the first decisions you’ll face is real-time vs batch (scheduled) integration. It might sound simple, but choosing the wrong approach can lead to performance issues, unhappy users, or even data inconsistency. In this blog, I’ll Walk through the key differences, when to use each, and lessons we’ve learned from real projects across Dynamics 365 CRM and F&O. The Basics: What’s the Difference? Type Description Real-Time Data syncs immediately after an event (record created/updated, API call). Batch Data syncs periodically (every 5 mins, hourly, nightly, etc.) via schedule. Think of real-time like WhatsApp you send a message, it goes instantly. Batch is like checking your email every hour you get all updates at once. When to Use Real-Time Integration Use It When: Example: When a Sales Order is created in D365 CRM, we trigger a Logic App instantly to create the corresponding Project Contract in F&O. Key Considerations When to Use Batch Integration Use It When: Example: We batch sync Time Entries from CRM to F&O every night using Azure Logic Apps and Azure Blob checkpointing. Key Considerations Our Experience from the Field On one recent project: As a Result, the system was stable, scalable, and cost-effective. To conclude, you don’t have to pick just one. Many of our D365 projects use a hybrid model: Start by analysing your data volume, user expectations, and system limits — then pick what fits best. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

From AI Experiments to AI That Can Be Trusted  Generative AI has moved from experimentation to executive priority. Yet across industries, many organizations struggle to convert pilots into dependable business outcomes.  At CloudFronts, we’ve consistently seen why.  Whether working with Sonee Hardware in distribution and retail or BÜCHI Labortechnik AG in manufacturing and life sciences, AI success has never started with models. It has started with trust in data.  AI that operates on fragmented, inconsistent, or poorly governed data introduces risk not advantage. The organizations that succeed follow a different path: they build intelligence on top of trusted, enterprise-grade data platforms.  The Real Challenge: AI Without Context or Control  Most stalled AI initiatives share common traits:  This pattern leads to AI that looks impressive in demos but struggles in production.  CloudFronts has seen this firsthand when customers approach AI before fixing data fragmentation. In contrast, customers who first unified ERP, CRM, and operational data created a far smoother path to AI-driven decision-making.  What Data-Native AI Looks Like in Practice  Agent Bricks represents a shift from model-centric AI to data-centric intelligence, where AI agents operate directly inside the enterprise data ecosystem.  This aligns closely with how CloudFronts has helped customers mature their data platforms:  In both cases, AI readiness emerged naturally once data trust was established.  Why Modularity Matters at Enterprise Scale  Enterprise intelligence is not built with a single AI agent.  It requires:  Agent Bricks mirrors how modern enterprises already operate through modular, orchestrated components rather than monolithic solutions.  This same principle guided CloudFronts data architecture work with customers:  AI agents built on top of this architecture inherit the same scalability and control.  Governance Is the Difference Between Insight and Risk  One of the most underestimated risks in AI adoption is hallucination, AI confidently delivering incorrect or unverifiable answers.  CloudFronts customers in regulated and data-intensive industries are especially sensitive to this risk.  For example:  By embedding AI agents directly into governed data platforms (via Unity Catalog and Lakehouse architecture), Agent Bricks ensures AI outputs are traceable, explainable, and trusted.  From Reporting to “Ask-Me-Anything” Intelligence  Most CloudFronts customers already start with a familiar goal: better reporting.  The journey typically evolves as follows:  This is the same evolution seen with customers like Sonee Hardware, where reliable reporting laid the groundwork for more advanced analytics and eventually AI-driven insights.  Agent Bricks accelerates this final leap by enabling conversational, governed access to enterprise data without bypassing controls.  Choosing the Right AI Platform Is About Maturity, Not Hype  CloudFronts advises customers that AI platforms are not mutually exclusive:  The deciding factor is data maturity.  Organizations with fragmented data struggle with AI regardless of platform. Those with trusted, governed data like CloudFronts mature ERP and analytics customers are best positioned to unlock Agent Bricks’ full value.  What Business Leaders Can Learn from Real Customer Journeys  Across CloudFronts customer engagements, a consistent pattern emerges:  AI success follows data maturity not the other way around.  Customers who:  were able to adopt AI faster, safer, and with measurable outcomes.  Agent Bricks aligns perfectly with this reality because it doesn’t ask organizations to trust AI blindly. It builds AI where trust already exists.  The Bigger Picture  Agent Bricks is not just an AI framework it reflects the next phase of enterprise intelligence.  From isolated AI experiments to integrated, governed decision systems  From dashboards to conversational, explainable insight  From AI as an initiative to AI as a core business capability  At CloudFronts, this philosophy is already reflected in real customer success stories where data foundations came first, and AI followed naturally.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

In today’s data-driven business landscape, fast, reliable, and automated data integration isn’t just a luxury it’s a necessity. Organizations often deal with data scattered across various platforms like CRMs, ERPs, or third-party APIs. Manually managing this data is inefficient, error-prone, and unsustainable at scale. That’s where Azure Logic Apps comes into play. Why Azure Logic Apps? Azure Logic Apps is a powerful workflow automation platform that enables you to design scalable, no-code solutions to fetch, transform, and store data with minimal overhead. With over 200 connectors (including Dynamics 365, Salesforce, SAP, and custom APIs), Logic Apps simplifies your integration headaches. Use Case: Fetch Business Data and Dump to Azure Data Lake Imagine this:You want to fetch real-time or scheduled data from Dynamics 365 Finance & Operations or a similar ERP system.You want to store that data securely in Azure Data Lake for analytics or downstream processing in Power BI, Databricks, or Machine Learning models. What About Other Tools Like ADF or Synapse Link? Yes, there are other tools available in the Microsoft ecosystem such as: Why Logic Apps Is Better What You Get with Logic Apps Integration Business Value To conclude, automating your data integration using Logic Apps and Azure Data Lake means spending less time managing data and more time using it to drive business decisions. Whether you’re building a customer insights dashboard, forecasting sales, or optimizing supply chains—this setup gives you the foundation to scale confidently. 📧 Ready to modernize your data pipeline? Drop us a note at transform@cloudfronts.com — our experts are ready to help you implement the best-fit solution for your business needs. 👉 In our next blog, we’ll walk you through the actual implementation of this Logic Apps integration, step-by-step — from connecting to Dynamics 365 to storing structured outputs in Azure Data Lake. Stay tuned!

At CloudFronts Technologies, we understand that growing organizations often operate across multiple geographies and business units. Whether you’re working with Dynamics 365 CRM or Finance & Operations (F&O), syncing data between systems can quickly become complex—especially when different legal entities follow different formats, rules, or structures. To solve this, our team developed a powerful yet simple approach: Dynamic Templates for Multi-Entity Integration. The Business Challenge When a global business operates in multiple regions (like India, the US, or Europe), each location may have different formats for project codes, financial categories, customer naming, or compliance requirements. Traditional integrations hardcode these rules—making them expensive to maintain and difficult to scale as your business grows. Our Solution: Dynamic Liquid Templates We built a flexible, reusable template system that automatically adjusts to each legal entity’s specific rules—without the need to rebuild integrations for each one. Here’s how it works: Why This Matters for Your Business Real-World Success Story One of our client’s needs to integrate project data from CRM to F&O across three different regions. Instead of building three separate integrations, we implemented a single solution with dynamic templates. The result? What Makes CloudFronts Different At CloudFronts, we build future-ready integration frameworks. Our approach ensures you don’t just solve today’s problems—but prepare your business for tomorrow’s growth. We specialize in Microsoft Dynamics 365, Azure, and enterprise-grade automation solutions. “Smart integrations are the key to global growth. Let’s build yours.” We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.

In today’s world of modern DevOps and continuous delivery, having the ability to analyze application behavior quickly and efficiently is key to Quality Assurance (QA). Azure Application Insights offers powerful telemetry collection, but what makes it truly shine is the Kusto Query Language (KQL)—a rich, expressive query language that enables deep-dive analytics into your application’s performance, usage, and errors. Whether you’re testing a web app, monitoring API failures, or validating load test results, KQL can become your best QA companion. What is KQL? KQL stands for Kusto Query Language, and it’s used to query telemetry data collected by Azure Monitor, Application Insights, and Log Analytics. It’s designed to be read like English, with SQL-style expressions, yet much more powerful for telemetry analysis. Challenges Faced with Application Insights in QA 1. Telemetry data doesn’t always show up immediately after execution, causing delays in debugging and test validation. 2.When testing involves thousands of records, isolating failed requests or exceptions becomes tedious and time-consuming. 3.The default portal experience lacks intuitive filters for QA-specific needs like test case IDs, custom payloads, or user roles. 4.Repeated logs from expected failures (e.g., negative test cases) can clutter insights, making it hard to focus on actual issues. 5.Out-of-the-box telemetry doesn’t group actions by test scenario or user session unless explicitly configured, making traceability difficult during test case validation. To overcome these limitations, QA teams need more than just default dashboards—they need flexibility, precision, and speed in analyzing telemetry. This is where Kusto Query Language (KQL) becomes invaluable. With KQL, testers can write custom queries to filter, group, and visualize telemetry exactly the way they need, allowing them to focus on real issues, validate test scenarios, and make data-driven decisions faster and more efficiently. Let’s take some examples for better understanding: Some Common scenarios where a KQL proves to be very effective. Check if the latest deployment introduced new exceptions Example: Find all failed requests Example: Analyse performance of a specific page or operation Example: Correlate request with exceptions Example: Validate custom event tracking (like button clicks) Example: Track specific user sessions for end-to-end QA testing Example: Test API performance under load Example: All of this can be Visualized too – You can pin your KQL queries to Azure Dashboards or even Power BI for real-time tracking during QA sprints. To conclude, KQL is not just for developers or DevOps. QA engineers can significantly reduce manual log-hunting and accelerate issue detection by writing powerful queries in Application Insights. By incorporating KQL into your testing lifecycle, you add an analytical edge to your QA process—making quality not just a gate but a continuous insight loop.Start with a few basic queries, and soon you’ll be building powerful dashboards that QA, Dev, and Product can all share! Hope this helps ! I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.