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.

For many small and medium-sized organizations, the ERP that once powered early growth is now slowing progress. Custom-built systems, often implemented long before the cloud era, were developed for a different time: smaller product catalogs, simpler compliance requirements, and fewer integration demands. Today’s businesses need more: more visibility, more agility, and more operational resilience. That is where Microsoft Dynamics 365 Business Central stands out. Its cloud-native architecture, rich financial and operational capabilities, and strong talent availability make it an ideal next step for organizations evolving from aging, home-grown systems. When “It Still Works” Is Not Enough Leaders often tell us their legacy ERP is still functioning. But “functioning” is not the same as “fit for the future.” Common challenges we hear include: 1) Systems Built for a Smaller Business Custom ERPs often cannot scale with new product lines, acquisitions, or international expansion. What once felt tailored now feels restrictive. 2) Rising Skill Gaps The original developers and architects are long gone. Each new change requires specialized workarounds, creating dependency on limited IT support and extending delivery timelines. 3) Infrastructure and Security Risks On-premises systems demand constant upkeep: servers, backups, security patches, disaster recovery, and more. Maintaining all this diverts attention from core business priorities and increases risk exposure. 4) Limited Audit and Compliance Capabilities Regulatory expectations have evolved. Many legacy ERPs lack traceability, standardized reporting, and audit-ready controls, making compliance costly and inefficient. These challenges create operational drag. Instead of enabling efficiency, the ERP becomes a barrier to progress. That is why many organizations are accelerating their move to the cloud, and Business Central has become the preferred direction. Why Business Central Is the Right Upgrade Path Modern Skills and Easier Adoption Business Central aligns with competencies already familiar to finance and IT teams. Talent is more widely available compared to niche ERP platforms, lowering hiring and training efforts. The Right Size for SMB Growth It offers robust ERP capabilities without the cost and complexity associated with larger enterprise systems. Cloud as a Differentiator With Microsoft handling security, performance, and updates, organizations free up resources for innovation instead of infrastructure maintenance. Designed for Integration CloudFronts has helped many organizations successfully transition from custom ERPs to Business Central Online. To further simplify operations, we have developed the PO BC Integration Module 2.0. This connects Dynamics 365 Project Operations and Business Central, delivering process continuity that is missing in standard connectors. A Foundation for the Future Migrating to Business Central is not just a technology upgrade. It is a strategic shift. It builds the foundation for advanced reporting, AI-driven insights, automation, and scalable growth. Businesses that make this move gain a system that: ✔ Supports today’s operations✔ Adapts to future changes✔ Reduces risk and complexity✔ Strengthens competitiveness Ready to Modernize Your ERP? CloudFronts helps organizations move from custom, outdated systems to Business Central with a structured, low-risk transformation approach. If you are considering your next ERP move, we are here to support you at every step. Connect with our experts: 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

The Integration Shift No Enterprise Can Ignore For more than a decade, legacy 3rd-party integration platforms served as the backbone of enterprise operations. But in a world being redefined by AI, cloud-native systems, and real-time data, these platforms are no longer keeping pace. Across industries, CIOs and digital leaders are facing the same reality: What was once a dependable foundation has now become a barrier to modern transformation. This is why enterprises around the world are accelerating the shift to Azure Integration Services (AIS) a cloud-native, modular, and future-ready integration backbone. From our field experience including the recent large-scale migration from TIBCO for Tinius Olsen one message is clear: Modernizing integration is not an IT upgrade. It is a business modernization initiative. 1. Why Integration Modernization Is Now a Business Imperative Digital systems are more distributed than ever. AI and automation are accelerating. Data volumes have exploded. Customers expect real-time experiences. Yet legacy middleware platforms were built for a world before: The challenges CIOs consistently report include: • Escalating licensing & maintenance costs: Annual renewals, hardware provisioning, and forced upgrades drain budgets. • Limited elasticity: Legacy platforms require you to over-provision capacity “just in case,” increasing cost and reducing efficiency. • Rigid, code-heavy orchestration: Every enhancement takes longer, requiring specialized skills. • Poor monitoring and operational visibility: Teams struggle to troubleshoot issues quickly due to decentralized logs. • Slow deployment cycles: Innovation slows down because integration becomes the bottleneck. This is why the modernization conversation has moved from “Should we?” to “How soon can we?”. 2. Why Azure Is Becoming the Digital Backbone for Modern Enterprises Azure Integration Services brings together a powerful suite of cloud-native capabilities: This is not a one-to-one replacement for middleware. It is an entirely new integration architecture built for the future. 3. What We Learned from the TIBCO → Azure Migration Journey Across the Tinius Olsen modernization project and similar enterprise engagements, six clear lessons emerged. 1. Cost Optimization Is Real and Immediate Moving to Azure shifts integration from a heavy fixed-cost model to a lightweight consumption model. Clients consistently see: Integration becomes a value driver not a cost burden. 2. Elastic Scalability Gives Confidence During Peak Loads Legacy platforms require expensive over-provisioning. Azure scales automatically depending on demand. The result: Scalability stops being a constraint and becomes an advantage. 3. Observability Becomes a Competitive Advantage Azure’s built-in monitoring ecosystem dramatically changes operational visibility: Tasks that once required hours of log investigations now take minutes.Root-cause analysis speeds up, uptime improves, and teams can proactively govern critical workflows. 4. Developer Experience Improves Significantly Modern integration requires both: Azure enables both through Logic Apps + Functions, enabling teams to build integrations: Developers can finally innovate instead of wrestling with legacy tooling. 5. The Platform Becomes AI- and Data-Ready Migration to Azure doesn’t just replace middleware.It unlocks new modernization pathways: The integration layer becomes a strategic enabler for enterprise-wide transformation. 6. The Strategic Message for CIOs and Digital Leaders Modernizing integration is not simply about technology replacement. It is about: In short: It is about building a future-ready enterprise. Modernizing Integration Is No Longer Optional The next decade will be defined by AI-driven systems, composable applications, and hyper automation.Legacy integration platforms were not built for this future, Azure is. Enterprises that modernize their integration layer today will be the ones that innovate faster, scale smarter, and operate more efficiently tomorrow. Read the Microsoft-published case study:CloudFronts Modernizes Tinius Olsen with Microsoft Dynamics 365 Talk to a Cloud ArchitectDiscuss your integration modernization roadmap in a 1:1 strategy session. 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.

Enterprises today are collecting more data than ever before, yet most leaders admit they don’t fully trust the insights derived from it. Inconsistent formats, missing values, and unreliable sources create what’s often called a data swamp an environment where data exists but can’t be used confidently for decision-making.  Clean, trusted data isn’t just a technical concern; it’s a business imperative. Without it, analytics, AI, and forecasting lose credibility and transformation initiatives stall before they start.  That’s where the Medallion Architecture comes in. It provides a structured, layered framework for transforming raw, unreliable data into consistent, analytics-ready insights that executives can trust.  At CloudFront’s, a Microsoft and Databricks partner, we’ve implemented this architecture to help enterprises modernize their data estates and unlock the full potential of their analytics investments.  Why Data Trust Matters More Than Ever  CIOs and data leaders today face a paradox: while data volumes are skyrocketing, confidence in that data is shrinking.  Poor data quality leads to:  In short, when data can’t be trusted, every downstream process from reporting to machine learning is compromised. The Medallion Architecture directly addresses this challenge by enforcing data quality, lineage, and governance at every stage.  What Is the Medallion Architecture?  The Medallion Architecture is a modern, layered data design framework introduced by Databricks. It organizes data into three progressive layers Bronze, Silver, and Gold each refining data quality and usability.  This approach ensures that every layer of data builds upon the last, improving accuracy, consistency, and performance at scale.  Inside Each Layer   Bronze Layer —> Raw and Untouched   The Bronze Layer serves as the raw landing zone for all incoming data. It captures data exactly as it arrives from multiple sources, preserving lineage and ensuring that no information is lost. This layer acts as a foundational source for subsequent transformations.   Silver Layer —> Cleansing and Transformation   At the Silver Layer, the raw data undergoes cleansing and standardization. Duplicates are removed, inconsistent formats are corrected, and business rules are applied. The result is a curated dataset that is consistent, reliable, and analytics ready.   Gold Layer —> Insights and Business Intelligence   The Gold Layer aggregates and enriches data around key business metrics. It powers dashboards, reporting, and advanced analytics, providing decision-makers with accurate and actionable insights.   Example: Data Transformation Across Layers   Layer   Data Example   Processing Applied   Outcome   Bronze   Customer ID: 123, Name: Null, Date: 12-03-24 / 2024-03-12   Raw data captured as-is   Unclean, inconsistent   Silver   Customer ID: 123, Name: Alex, Date: 2024-03-12   Standardization & de-duplication   Clean & consistent   Gold   Customer ID: 123, Name: Alex, Year: 2024   Aggregation for KPIs   Business-ready dataset   This layered approach ensures data becomes progressively more accurate, complete, and valuable.   Building Reliable, Performant Data Pipelines  By leveraging Delta Lake on Databricks, the Medallion Architecture enables enterprises to unify streaming and batch data, automate validations, and ensure schema consistency creating an end-to-end, auditable data pipeline.  This layered approach turns chaotic data flows into a structured, governed, and performant data ecosystem that scales as business needs evolve.  Client Example: Retail Transformation in Action  A leading hardware retailer in the Maldives faced challenges managing inventory and forecasting demand across multiple locations. They needed a unified data model that could deliver real-time visibility and predictive insights.  CloudFront’s implemented the Medallion Architecture using Databricks:  Results:  Key Benefits for Enterprise Leaders  Final Thoughts  Clean, trusted data isn’t a luxury, it’s the foundation of every successful analytics and AI strategy.  The Medallion Architecture gives enterprises a proven, scalable framework to transform disorganized, unreliable data into valuable, business-ready insights.  At CloudFront’s, we help organizations modernize their data foundations with Databricks and Azure delivering the clarity, consistency, and confidence needed for data-driven growth.  Ready to move from data chaos to clarity? Explore our Databricks Services or Talk to a Cloud Architect to start building your trusted analytics foundation today.  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

The landscape of digital transformation has never been more complex. Rapid market shifts, rising customer demands, and tightening budgets have made technology decisions more consequential than ever. The challenge isn’t adopting new tools it’s leading transformation by ensuring that every investment is grounded in clarity, alignment, and predictability. At CloudFront’s, we understand this. That’s why our Business Assessment Engagement model has become a proven first step toward successful, low-risk technology implementations. What Is a Business Assessment? A Business Assessment is a structured, short-term engagement conducted before signing a full implementation Statement of Work (SoW). It is designed to create complete visibility into your current business processes, desired future state, and the potential risks that could impact your project. Typically spanning 3 – 4 weeks, this engagement brings together functional and technical stakeholders from both your organization and CloudFront’s. Whenever feasible, we conduct this assessment onsite, ensuring close collaboration and a deep understanding of your business landscape. During this engagement, our experts: The result is a detailed Business Requirements Study (BRS) a comprehensive document that translates assessment insights into an actionable implementation roadmap. This BRS becomes the foundation for a precise and mutually agreed Statement of Work, ensuring every phase of your digital transformation is built on validated insights and shared understanding. Why a Business Assessment Matters For enterprise technology leaders, the Business Assessment approach delivers tangible benefits: Ultimately, this process transforms uncertainty into informed decision-making, enabling IT leaders to confidently advance from planning to execution. Proven Success with CloudFront’s At CloudFront’s, we’ve seen firsthand how Business Assessment engagements set the stage for successful digital transformations. Clients who adopt this model enter implementation phases with greater predictability, stronger governance, and renewed confidence in both the technology and the partnership driving it. Recently, we partnered with one of the world’s largest U.S. based commercial vehicle manufacturers to conduct an onsite Business Requirements Study (BRS). Our team worked closely with their stakeholders to map existing systems and design a strategic roadmap for migration to Microsoft Dynamics 365 Supply Chain Management (SCM). Following the successful completion of the BRS, we are now leading Phase 1, enabling their inventory, advanced warehouse, and procurement operations to establish a strong operational foundation. In Phase 2, we will enable master planning, production, and quality management to deliver end-to-end operational efficiency, ensuring a seamless and future-ready digital ecosystem. Our clients consistently tell us that this approach not only de-risks their investment but also enhances alignment between business and IT, a crucial factor in any transformation journey. To conclude, in today’s unpredictable business landscape, a well-executed Business Assessment isn’t just a preliminary step, it’s a strategic imperative. By partnering with CloudFront’s for a Business Assessment, you’re not committing to uncertainty; you’re investing in clarity, alignment, and long-term success. If your organization is planning a digital transformation initiative, start with a Business Assessment Engagement and move forward with the confidence of knowing your path is mapped, risks are managed, and success is measurable. Ready to move from uncertainty to clarity?Connect with CloudFront’s at transform@cloudfronts.com to schedule a Business Assessment Engagement and gain a clear, actionable roadmap for your next digital transformation. Contact Us to start your assessment today.

For years, Microsoft Dynamics GP has been a reliable ERP system, helping businesses streamline financial operations, maintain compliance, and drive efficiency. It became a backbone for thousands of organizations, particularly mid-sized businesses that valued its stability and robustness. But the business landscape has changed dramatically. Markets move faster. Customer expectations are higher. And technology is no longer just a support function, it is the engine of growth, agility, and innovation. This is why the transition from Dynamics GP to Microsoft Dynamics 365 Business Central is not just another software upgrade. It is a strategic leap forward that determines how ready your business is for the next decade. The Real Question: Maintain or Evolve? Every business leader faces this decision at some point: continue maintaining what’s familiar or evolve into what’s next. GP offers stability, but that stability now comes with limitations, manual upgrades, server costs, and restricted scalability. For many companies, these challenges are becoming a bottleneck to innovation. On the other hand, Business Central offers agility. It’s a modern, cloud-first ERP that grows with your business, continuously innovates, and seamlessly integrates with the entire Microsoft ecosystem. In today’s world, standing still is the same as moving backward. The choice is simple: maintain what works or evolve toward what drives growth. What Businesses Gain with Business Central Always Up to Date No more manual upgrades or disruptive transitions. Business Central runs on the cloud with continuous updates and innovations at no additional cost. This means your team is always using the latest technology, features, and security enhancements without the burden of maintenance. Faster Decisions, Smarter Moves In an age where data drives competitive advantage, Business Central integrates seamlessly with Power BI and embedded analytics to deliver real-time insights. Leaders can act on facts, not assumptions, and empower their teams to make faster, data-driven decisions that move the business forward. Scalability Without Limits Growth brings complexity, new markets, entities, currencies, and compliance requirements. Business Central scales effortlessly to handle it all. Whether you are expanding into new geographies or diversifying your business model, the system grows with you, not against you. An Integrated Digital Workplace Business Central works hand in hand with Microsoft 365, Teams, Power Automate, and AI. The result is a truly connected workplace where data flows freely, collaboration improves, and manual processes give way to automation. This integration not only boosts productivity but also builds a culture of transparency and shared accountability. Cost Efficiency and Risk Reduction By eliminating on-premise IT infrastructure, you reduce overheads, lower downtime, and free up valuable resources to focus on innovation. With built-in security, compliance, and automated backups, your business becomes more resilient and future-proof. A Transformation Story At CloudFronts, we recently began working with a mid-sized client who had been running Dynamics GP for nearly three decades. GP had been the financial backbone of their operations and had served them well. However, the leadership team recognized an emerging reality: GP will soon reach its end of life, and continuing to rely on it would increase both operational risk and cost. They made a strategic decision, to migrate to Business Central and secure a platform built for the next decade of growth. Their goals were clear: This migration is now underway, and the client views it not as an IT project, but as a business transformation initiative. For them, Business Central represents the foundation of a connected, intelligent enterprise, one where decisions are faster, processes are leaner, and growth is continuous. Why Now Is the Right Time Many businesses delay ERP migrations because “things are working fine.” But the reality is that postponing the move comes with hidden risks, rising IT maintenance costs, outdated security models, dependency on legacy infrastructure, and the gradual loss of talent familiar with older systems. At the same time, competitors who embrace modern ERP platforms are moving faster, integrating AI, automating workflows, and leveraging real-time insights. The cost of waiting is not just financial, it is strategic. Business Central is more than an ERP. It is a platform for growth, intelligence, and resilience. It enables organizations to future-proof their operations while staying agile in an unpredictable world. The Takeaway Migrating from GP to Business Central is not a technical move-it is a business transformation decision. It means: With Dynamics GP approaching its end of life, the question is not if you should move, but when and how strategically you make that move. The time to act is now. If you are evaluating your options or planning your next steps, let’s talk. At CloudFronts, we’ve helped businesses across industries transition from legacy ERP systems to modern, scalable platforms like Business Central with minimal disruption and maximum value. Reach out at transform@cloudfronts.com. Let’s explore how you can evolve confidently into the future of business.

Why SmartPitch? – The Idea and Pain Point  The idea for SmartPitch came directly from observing the day-to-day struggles of sales and pre-sales teams. Every Marketing Qualified Lead (MQL) to Sales Qualified Lead (SQL) conversion required hours of manual work: searching through documents stored in SharePoint, combing through case studies, aligning them with solution areas, and finally packaging them into a client-ready pitch deck.  The reality was that documents across systems—SharePoint, Dynamics 365, PDFs, PPTs—remained underutilized because there was no intelligent way to bring them together.   Sales teams often relied on tribal knowledge or reused existing decks with limited personalization.  We asked: What if a sales assistant could automatically pull the right case studies, map them to solution areas, and draft an elevator pitch on demand, in minutes?  That became the SmartPitch vision: an AI-powered agent that:  As a result of this product, it has helped us reduce pitch creation time by 70%.   2. The First Prototype – Custom Copilot Studio  Our first step was to build SmartPitch using Custom Copilot Studio. It gave us a low-code way to experiment with conversational flows, integrate with Azure AI Search, and provide sales teams with a chat interface.  1. Knowledge Sources Integration  2. Data Flow  3. Conversational Flow Design  4. Integration and Security  5. Technical Stack  6. Business Process Enablement  7. Early Prototypes  With Custom Copilot, we were able to:  We successfully demoed these early prototypes in Zurich and New York. They showed that the idea worked but they also revealed serious limitations.  3. Challenges in Custom Copilot  Despite proving the concept, Custom Copilot Studio had critical shortcomings:  Lacked support for model fine-tuning or advanced RAG customization.  However, incorporating complex external APIs or custom workflows was difficult.  This limitation meant SmartPitch, in its Copilot form, couldn’t scale to meet enterprise standards.  4. Rebuilding in Azure AI Foundry – Smarter, Extensible, Connected  The next phase was Azure AI Foundry, Microsoft’s enterprise AI development platform. Unlike Copilot Studio, AI Foundry gave us:  Extending SmartPitch with Logic Apps  One of the biggest upgrades was the ability to integrate Azure Logic Apps as external tools for the agent. This allowed SmartPitch to:  This modular approach meant we could add new functionality simply by publishing a new Logic App. No redeployment of SmartPitch was required.  Automating Document Vectorization  We also solved one of the biggest bottlenecks—document ingestion and retrieval—by building a pipeline for automatic document vectorization from SharePoint:  This allowed SmartPitch to search across text, images, tables, and PDFs, providing relevant answers instead of keyword matches.  But There Were Limitations  Even with these improvements, we hit roadblocks:  At this point, we realized the true bottleneck wasn’t the agent itself, it was the quality of the data powering it.  5. Bad Data, Governance, and the Medallion Architecture  SmartPitch’s performance was only as good as the data it retrieved from. And much of the enterprise data was dirty: duplicate case studies, outdated documents, inconsistent file formats.  This led to irrelevant or misleading responses in pitches.  To address this, we turned to Databricks’ Unity Catalog and Medallion Architecture:  You can read our post on building a clean data foundation with Medallion Architecture [Link]   Now, every result SmartPitch surfaced could be trusted, audited, and tied to a governed source.  6. SmartPitch in Mosaic AI – The Final Evolution  The last stage was migrating SmartPitch into Databricks Mosaic AI, part of the Lakehouse AI platform. This was where SmartPitch matured into an enterprise-grade solution.  What We Gained in Mosaic AI:  In Mosaic AI, SmartPitch wasn’t just a chatbot it became a data-native enterprise sales assistant:  From these, we came to know the following differences between agent development in AI Foundry & DataBricks Mosaic AI –    Attribute / Aspect  Azure AI Foundry  Mosaic AI  Focus  Developer and Data Scientist  Data Engineers, Analysts, and Data Scientists  Core Use Case  Create and manage your own AI agent  Build, experiment, and deploy data-driven AI models with analytics + AI workflows  Interface  Code-first (SDKs, REST APIs, Notebooks)  No-code/low-code UI + Notebooks + APIs  Data Access  Azure Blob, Data Lake, vector DBs  Native integration with Databricks Lakehouse, Delta Lake, Unity Catalog, vector DBs  MCP Server  Only custom MCP servers supported; built-in option complex  Native MCP support with Databricks ecosystem; simpler setup  Models  90 models available  Access to open-source + foundation models (MPT, Llama, Mixtral, etc.) + partner models  Model Customization  Full model fine-tuning, prompt engineering, RAG  Fine-tuning, instruction tuning, RAG, model orchestration  Publish to Channels  Complex (Azure Bot SDK + Bot Framework + App Service)  Direct integration with Databricks workflows, APIs, dashboards, and third-party apps  Agent Update  Real-time updates in Microsoft Teams  Updates deployed via Databricks workflows; versioning and rollback supported  Key Capabilities  Prompt flow orchestration, RAG, model choice, vector search, CICD pipelines, Azure ML & responsible AI integration  Data + AI unification (native to Lakehouse), RAG with Lakehouse data, multi-model orchestration, fine-tuning, end-to-end ML pipelines, secure governance via Unity Catalog, real-time deployment  Key Components  Workspace & agent orchestration, 90+ models, OpenAI pay-as-you-go or self-hosted, security via Azure identity  Mosaic AI Agent Framework, Model Serving, Fine-Tuning, Vector Search, RAG Studio, Evaluation & Monitoring, Unity Catalog Integration  Cost / License  Vector DB: external, Model Serving: token-based pricing (GPT-3.5, GPT-4), Fine-tuning: case-by-case, Total agent cost variable (~$5k–$7k+/month)  Vector Search: $605–$760/month for 5M vectors, Model Serving: $90–$120 per million tokens, Fine-Tuning Llama 3.3: $146–$7,150, Managed Compute built into DBU usage, End-to-end AI Agent ~$5k–$7k+/month  Use Cases / Capabilities  Agents intelligent, can interact/modify responses; single AI search per agent; infrastructure setup required; custom MCP server registration  Agents intelligent, interact/modify responses; AI search via APIs (Google/Bing); in-built MCP server; complex infrastructure; slower responses as results batch sent together  Development Approach  Low-code, faster agent creation, SDK-based, easier experimentation  Manual coding using MLflow library, more customization, API integration, higher chance of errors, slower build  Models Comparison  90 models, Azure OpenAI (GPT-3.5, GPT-4), multi-modal  ~10 base models, OSS & partner models (Llama, Claude, Gemma), many models don’t support tool usage  Knowledge Source  One knowledge source of each type (adding new replaces previous)  No limitation; supports data cleaning via Medallion Architecture; SQL-only access inside agent; Spark/PySQL not supported in agent  Memory / Context Window  8K–128K tokens (up to 1M for GPT-4.1)  Moderate, not specified  Modalities  Text, code, vision, audio (some models)  Likely text-only  Special Enhancements  Turbo efficiency, reasoning, tool calling, multimodal  Varies per model (Llama, Claude, Gemma architectures)  Availability  Deployed via Azure AI Foundry  Through Databricks platform  Limitations  Only one knowledge source of each type, infrastructure complexity for MCP server  No multi-modal Spark/PySQL access, slower batch responses, limited model count, high manual development  7. Lessons Learned:  … Continue reading Inside SmartPitch: How CloudFronts Built an Enterprise-Grade AI Sales Agent Using Microsoft and Databricks Technologies  →

Everyone wants AI. Few are ready for it.  The question isn’t “When do we start?” but “Are we prepared to get it right?”  Because switching on Copilots without fixing your foundations doesn’t accelerate you. it amplifies chaos.  This article will cover how to fix your foundations for AI so that the AI tools you deploy are accurate and reliable.  Challenges of deploying AI Directly  Some of the common challenges of directly deploying AI on top of your business applications are –   And these issues just render the AI implementation as a failure immediately dismissing trust in using AI at all.  But these challenges can be overcome once the foundations of AI are in place which we’ll discuss in the next section.  Foundation of AI  At CloudFronts, we call this the 3 Pillars of AI Readiness:  Here’s how I sum up the foundation of the systems for AI –  For example, when CloudFronts helped Tinius Olsen modernize their systems, the focus wasn’t just technical uplift. It was about ensuring every business process was cloud-ready so AI models could actually trust the data.   Upgrading from legacy systems And this is the foundation that needs to be had before AI can be implemented at your organization.  Data & AI Maturity Curve by Databricks  Given the above foundations in place for your AI Adoption strategy and choosing the right framework for your implementation, the Data & AI Maturity Curve shown below can be referenced to see where your organization is on the curve and where do you want to get to –   On a high level, the foundation will get you to look back at the data and see what has happened in the past and AI tools can help you get this information accurately.  Further, once trust is established, actions like making the AI predict the future state of operations, prescribe steps and even take decisions on our behalf can be achieved – provided you really want that to happen. It might be too soon just yet.  To conclude, AI success = Foundations × Trust.  Without modern systems, connected data, and governed access, AI is just noise.  But with these in place, every AI tool you deploy whether predictive analytics or Copilots becomes an accelerator for decision-making, not a distraction.  Before you deploy AI, fix your foundations. If you’re serious about making AI a trusted accelerator not a costly experiment start with modernization, connection, and governance. At CloudFronts, we help enterprises build these foundations with confidence. Let’s connect over our email: Transfrom@cloudfronts.com  

In today’s global manufacturing landscape, businesses need more than just strong products to stay competitive. They need digital operations that connect customers, distributors, and internal teams in different regions. One powerful way to achieve this is by integrating e-commerce platforms with enterprise resource planning (ERP) systems.  This is the story of a 140-year-old global leader in materials testing machine manufacturing that transformed its order-taking process through a Shopify–Dynamics 365 Finance & Operations integration.  The Challenge  With offices in five countries and sales across the UK, Europe, China, India and multiple U.S. territories, this manufacturer had a truly global footprint. Yet, order-taking remained manual and inefficient:  In short: their legacy setup couldn’t keep up with modern customer expectations or their own ambitions for global growth.  The Solution  Over the course of a decade long partnership, we helped the company modernize and digitize its business processes. The centre piece was a seamless integration between Shopify and Dynamics 365 Finance & Operations (F&O), built natively within F&O (no recurring middleware costs).  Key integrations included:  This solution ensured that high data volumes and complex processing demands could be handled efficiently within F&O.  The Results  The change has reshaped how the company works:  Lessons for Other Global Manufacturers  This journey highlights critical lessons for manufacturers, distributors, and global businesses alike:  The Road Ahead  After integrating Shopify with Dynamics 365 F&O, the company has launched a dedicated distributor website where approved distributors can place orders directly on behalf of customers. This portal creates a new revenue stream, strengthens the distribution network, and ensures orders flow into F&O with the same automation, inventory sync, and reporting as direct sales. By extending digital integration to distributors, the company is simplifying order-taking while expanding its business model for global growth.  Ending thoughts  The journey of this global manufacturer shows that true digital transformation isn’t about adding more tools, it’s about connecting the right ones. By integrating Shopify with Dynamics 365 F&O, they moved from fragmented, manual processes to a scalable, automated ecosystem that empowers customers, distributors, and internal teams alike.  For any organization operating across regions, the lesson is clear: e-commerce and ERP should not live in silos. When they work together, they create a foundation that not only accelerates order taking but also unlocks new revenue streams, sharper insights, and stronger global relationships.  In a world where speed, accuracy, and customer experience define competitiveness, the question isn’t whether you can afford to integrate, it’s whether you can afford not to.  What’s next:   Don’t let manual processes slow you down. Connect with us at transform@cloudfronts.com and let’s design an integration roadmap tailored for your business.