In Part 1, we built a simple MCP server in TypeScript that exposed a “getWeather” tool. Now, let’s take the next step: connecting our MCP server to Microsoft Copilot Studio so that Copilot agents can call it directly. This section will cover: Step 1 — Publish Your MCP Server to Azure To make your MCP server accessible to Copilot Studio, you’ll need to host it online. There are multiple ways to deploy it — Azure App Service, Azure Container Apps, or even Azure Functions if you prefer serverless. For example, using Azure App Service: Test using curl to ensure it responds with MCP-compatible JSON: Step 2 — Create a New Copilot in Copilot Studio Step 3 — Add Knowledge Sources Optionally, you can enrich your Copilot by adding: This gives your Copilot a baseline knowledge to answer broader questions, while the MCP server will handle specific tasks (like fetching live weather data). Step 4 — Create a Custom Connector in Dataverse To let Copilot Studio talk to our MCP server, we need a custom connector inside Dataverse/CRM. Step 5 — Add the Custom Connector to Copilot Studio you’ll see the MCP server in your Tools section of copilot. To test the setup, let’s ask Copilot: “What’s the current weather in Mumbai?” On the first attempt, Copilot will prompt you to establish a connection. Simply open the Connection Manager, click Connect, and authorize the link to your MCP server. Once connected, Copilot will fetch the live weather details for Mumbai directly from your MCP server. and click retry on the Test window of your copilot. And just like that, your MCP server is live and fully integrated. It can now provide real-time weather updates for any city mentioned in your conversation with Copilot. You can try out different variations of questions or phrasings — Copilot will intelligently interpret your request, extract the city name, and seamlessly call the MCP server to deliver accurate weather details. Beyond Weather: Business Integrations The same process works for enterprise systems. For example, instead of getWeather, you could expose: By publishing these tools via MCP, your Copilot becomes a true enterprise assistant, capable of pulling structured business data and triggering workflows on demand. 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 AI Race Has Begun but Most Businesses Are Crawling Artificial Intelligence (AI) is no longer experimental it’s operational. Across industries, companies are trying to harness it to improve decision-making, automate intelligently, and gain competitive edge. But here’s the problem: only 48% of AI projects ever make it to production (Gartner, 2024). It’s not because AI doesn’t work.It’s because most tech stacks aren’t built to support it. The Real Bottleneck Isn’t AI. It’s Your Foundation You may have data. You may even have AI tools. But if your infrastructure isn’t AI-ready, you’ll stay stuck in POCs that never scale. Common signs you’re blocked: AI success starts beneath the surface, in your data pipelines, infrastructure, and architecture. Most machine learning systems fail not because of poor models, but because of broken data and infrastructure pipelines. What Does an AI-Ready Tech Stack Look Like? Being AI-Ready means preparing your infrastructure, data, and processes to fully support AI capabilities. This is not a checklist or quick fix. It is a structured alignment of technology and business goals. A truly AI-ready stack can: Area Traditional Stack AI-Ready Stack Why It Matters Infrastructure On-premises servers, outdated VMs Azure Kubernetes Service (AKS), Azure Functions, Azure App Services; then: AWS EKS, Lambda; GCP GKE, Cloud Run AI workloads need scalable, flexible compute with container orchestration and event-driven execution Data Handling Siloed databases, batch ETL jobs Azure Data Factory, Power Platform connectors, Azure Event Grid, Synapse Link; then: AWS Glue, Kinesis; GCP Dataflow, Pub/Sub Enables real-time, consistent, and automated data flow for training and inference Storage & Retrieval Relational DBs, Excel, file shares Azure Data Lake Gen2, Azure Cosmos DB, Microsoft Fabric OneLake, Azure AI Search (with vector search); then: AWS S3, DynamoDB, OpenSearch; GCP BigQuery, Firestore Modern AI needs scalable object storage and vector DBs for unstructured and semantic data AI Enablement Isolated scripts, manual ML Azure OpenAI Service, Azure Machine Learning, Copilot Studio, Power Platform AI Builder; then: AWS SageMaker, Bedrock; GCP Vertex AI, AutoML; OpenAI, Hugging Face Simplifies AI adoption with ready-to-use models, tools, and MLOps pipelines Security & Governance Basic firewall rules, no audit logs Microsoft Entra (Azure AD), Microsoft Purview, Microsoft Defender for Cloud, Compliance Manager, Dataverse RBAC; then: AWS IAM, Macie; GCP Cloud IAM, DLP API Ensures responsible AI use, regulatory compliance, and data protection Monitoring & Ops Manual monitoring, limited observability Azure Monitor, Application Insights, Power Platform Admin Center, Purview Audit Logs; then: AWS CloudWatch, X-Ray; GCP Ops Suite; Datadog, Prometheus AI success depends on observability across infrastructure, pipelines, and models In Summary: AI-readiness is not a buzzword. Not a checklist. It’s an architectural reality. Why This Matters Now AI is moving fast and so are your competitors. But success doesn’t depend on building your own LLM or becoming a data science lab. It depends on whether your systems are ready to support intelligence at scale. If your tech stack can’t deliver real-time data, run scalable AI, and ensure trust your AI ambitions will stay just that: ambitions. How We Help We work with organizations across industries to: Whether you’re just starting or scaling AI across teams, we help build the architecture that enables action. Because AI success isn’t about plugging in a tool. It’s about building a foundation where intelligence thrives. 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.

In project-based businesses, reporting is the final word. It tells you what was planned, what happened, where you made money, and where you lost it. But ask any project manager or CEO what they really think about project reporting today, and you’ll hear this: “It’s late. It’s manual. It’s siloed. And by the time I see it, it’s too late to act.” This is exactly why AI is no longer optional; it’s essential. Whether you’re in construction, consulting, IT services, or professional engineering, AI can elevate your project reporting from a reactive chore to a strategic asset. Here’s how. The Problem with Traditional Reporting. Most reporting today involves: Enter AI: The Game-Changer for Project Reporting AI isn’t about replacing humans; it’s about augmenting your decision-making. When embedded in platforms like Dynamics 365 Project Operations and Power BI, AI becomes the project manager’s smartest analyst and the CEO’s most trusted advisor. Here’s what that looks like: Imagine your system telling you: “Project Alpha is likely to overrun budget by 12% based on current burn rate and resource allocation trends.” AI models analyse historical patterns, resource velocity, and task progress to predict issues weeks in advance. That’s no longer science fiction—it’s happening today with AI-enhanced Power BI and Copilot in Dynamics 365. Instead of navigating dashboards, just ask: “Show me projects likely to miss deadlines this month.” With Copilot in Dynamics 365, you get answers in seconds with charts and supporting data. No need to wait for your analyst or export 10 spreadsheets. AI can clean, match, and validate data coming from: No more mismatched formats or chasing someone to update a spreadsheet. AI ensures your reports are built on clean, real-time data, not assumptions. You don’t need to check 12 dashboards daily. With AI, set intelligent alerts: These alerts are not static rules but learned over time based on project patterns and exceptions. To conclude, for CEOs and PMs alike: We can show you how AI and Copilot in Dynamics 365 can simplify reporting, uncover risks, and help your team act with confidence. Start small, maybe with reporting or forecasting, but start now. 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.

An AI agent is a smart program that can think, make decisions, and do tasks. Sometimes it works alone, and sometimes it works with people or other agents. The main difference between an agent and a regular assistant is that agents can do things on their own. They don’t just help—you can give them a goal, and they’ll try to reach it. Every AI agent has three main parts: Agents can take input like a message or a prompt and respond with answers or actions.  For example, they might look something up or start a process based on what you asked. Azure AI Foundry is a platform that brings all these things together; so you can build, train, and manage AI agents easily. References What is Azure AI Foundry Agent Service? – Azure AI Foundry | Microsoft Learn Understanding deployment types in Azure AI Foundry Models – Azure AI Foundry | Microsoft Learnhttps://learn.microsoft.com/en-us/azure/ai-foundry/how-to/index-add Usage Firstly, we create a project in Azure AI Foundry. Click on Next and give a name to your project. Wait till the setup finishes. Once the project creation finishes we are greeted with this screen. Click on Agents tab and click on Next to choose the model. I’m currently using GPT-4o Mini. It also includes descriptions for all the available models. Then we configure the deployment details. There are multiple deployment types available such as – Global Deployments Data Zone Standard Deployments Standard deployments [Standard] follow a pay-per-use model perfect for getting started quickly.They’re best for low to medium usage with occasional traffic spikes.  However, for high and steady loads, performance may vary.Provisioned deployments [ProvisionedManaged] let you pre-allocate the amount of processing power you need.This is measured using Provisioned Throughput Units (PTUs).  Each model and version requires a different number of PTUs and offers different performance levels. Provisioned deployments ensure predictable and stable performance for large or mission-critical workloads. This is how the deployment details look for in Global Standard. I’ll be choosing Standard deployment for our use case. Click on deploy and wait for a few seconds. Once the deployment is completed, you can give your agent a name and some instructions for their behavior. You should specify the tone, end goal, verbosity, etc as well. You can also specify the Temperature and Top P values which are both a control on the randomness or creativeness of the model. Temperature controls how bold or cautious the model is. Lower temperature = Safer, more predictable answers. (Factual Q&A, Code Summarization)Higher temperature = More creative or surprising answers. (Poetry/Creative writing) Top P (Nucleus Sampling) controls how wide the model’s word choices are. Lower Top P = Only picks from the most likely words. (Legal or financial writing) Higher Top P = Includes less likely, more diverse words. (Brainstorming names) Next, I’ll add a knowledge base to my bot. For this example, I’ll just upload a single file.However, you have the option to add an sharepoint folder or files, connect it to Bing Search, MS Fabric, Azure AI search, etc as required. A Vector store in Azure AI Foundry helps your AI agent retrieve relevant information based on meaning rather than just keywords.It works by breaking your content (like a PDF) into smaller parts, converting them into numerical representations (embeddings), and storing them.When a user asks a question, the AI finds the most semantically similar parts from the vector store and uses them to generate accurate, context-aware responses. Once you select the file, click on Upload and save. At this point, you can start to interact with your model. To “play around” with your model, click on the “Try in Playground” button. And here, we can see the output based on our provided knowledge base. One more example, just because it is kind of fun. Every input that you provide to the agent is called as a “message”. Everytime the agent is invoked for processing the provided input is called a “run”. Every interaction session with the agent is called a “thread”. We can see all the open threads in the threads section. To conclude, Azure AI Foundry makes it easy to build and use AI agents without writing any code.  You can choose models, set how they behave, and connect your data all through a simple interface. Whether you’re testing ideas, automating tasks, or building custom bots, Foundry gives you the tools to do it.If you’re curious about AI or want to try building your agent, Foundry is a great place to begin. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com

Since the turn of 2025, AI has been thrown around a lot in conversations – both individual and also at an organizational level. Major technology providers have started their own suite of tools to build AI agents.  While these tools are good enough for simpler AI use cases like fetching data from systems and presenting to us, but complex use cases like predicting patterns, collating data from multiple systems and driving insights from connected systems – that’s where AI implementations need to be looked at like projects which needs architecting and implementing with organization’s vision of AI.  Let’s look at how we can make sure that AI implementations give us over 95% accuracy and not just answers every time which we assume might be correct.  Is AI enough by itself?  Common perception that AI Agents are deployed on top of applications which can be used to interact with the underlying systems to do what users are supposed to get done from AI.  This perception stems from our use of AI tools like ChatGPT/Claude/Gemini as they interact with the Internet to get your queries answered. Since this is a tool available independently, there’s not technical setup and it is ready to go.  Speaking of being Copilot being enough on itself, it depends on where the data is sourced from – and what the intent of the Agent is. If your Custom Copilot / AI Agent is meant to only look at some SharePoint files, some websites and within 1 system in your M365 gated access, you should be able to patch to knowledge sources and be good enough to let AI Agent give you the information in the format you need.  Challenge occurs where you expecting the AI Agents to make sense of the data which is stored differently in different systems with different naming conventions – that’s when AI agents will fall through because it cannot understand when you are pointing to an “Account” in CRM, but the same is stored as a “Customer” in Business Central.  And this is where something like a Unity Catalog comes into picture. The term itself describes that the data comes together in a catalog for common access and AI agents to source from.  Let’s look at how we can imagine this unity catalog to be in the next section.  Unity Catalog  Unity Catalog can be thought of as an implementation strategy and collection of connected systems over which AI Agents can be based upon.  Here’s how I summarize this process –   Above diagram is a summary for how AI implementations will scale within organizations and have different variations of the same.  To encapsulate, while independent AI agents can be implemented for personal use within the organization, given the appropriate privileges, for AI to make sense of and enable trusted decision making, AI implementations need to have data readiness in place with clarity.  Hopefully, this topic summarizes the direction in which organizations can think of AI implementation, more than just building agents.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

In the fast-evolving business landscape, sales leaders and business owners—whether new startups or established enterprises—face an unprecedented challenge: how to scale efficiently while maintaining a competitive edge. The digital revolution has created a vast ecosystem of tools, but many businesses are still unsure of how to leverage them effectively. For existing businesses, the challenge lies in moving away from manual data entry, disjointed workflows, and delayed decision-making that hinder productivity. Many companies still rely on outdated methods like Excel sheets, paperwork, and disconnected systems, leading to inefficiencies and lost revenue. For new or growing businesses, the challenge is different—they need to build a scalable foundation from day one, ensuring that the right digital tools are in place to support growth, automation, and decision-making. This is where Microsoft’s cloud ecosystem, particularly Dynamics 365 CE, Power Platform, and Power BI, plays a critical role in setting up businesses for long-term success. Automation is no longer just an operational advantage; it is a strategic imperative. Leveraging these tools, organizations can create a seamless, data-driven ecosystem that empowers sales teams to work smarter, not harder. But automation must be approached thoughtfully. It’s not about replacing human intuition; it’s about enhancing it. The Business Challenge: Automation is for Everyone, Not Just Tech Giants A common misconception is that automation is reserved for large enterprises with vast IT budgets. However, small and mid-sized businesses, as well as new startups, can also harness automation to streamline operations and scale efficiently. The key lies in understanding where automation can add value and how leaders can architect a strategy that integrates human judgment with system intelligence. Consider a mid-sized manufacturing firm that still manages leads and customer follow-ups manually. The sales team spends hours logging interactions, tracking deals, and following up via emails, leading to lost opportunities. By implementing Power Automate with Dynamics 365 CE, the company can: For a new business venturing into the cloud ecosystem, automation is a game-changer from day one. Instead of relying on traditional methods, they can: The result? More deals closed in less time, with greater accuracy and a human-first approach to relationship-building. The “ACTION” Framework for Sales Automation (Automate, Connect, Track, Improve, Optimize, Nurture) Sales Process Automation: From Lead to Close with Structured Chaos The “SMART” Approach to Sales Automation (Simplify, Monitor, Automate, Refine, Transform) Example 1: Automating Lead Qualification Imagine a sales rep manually filtering through hundreds of incoming leads to identify high-potential prospects. This process is not only time-consuming but also prone to bias. With AI-powered lead scoring in Dynamics 365 CE, the system automatically: Example 2: Automated Follow-Ups to Prevent Lost Deals A major challenge in sales is following up consistently. Research suggests that 80% of sales require five follow-ups, yet many reps give up after one or two. With Power Automate, businesses can: These micro-automations ensure no lead falls through the cracks, keeping the pipeline healthy and sales reps focused on closing deals. Power Virtual Agents (Copilot Agents): Revolutionizing Customer Engagement With the rise of AI, Power Virtual Agents, now called Copilot Agents, have transformed how businesses handle customer engagement and service. These AI-driven chatbots can: CRM Integration: The Power of a Unified System Many organizations use third-party tools for sales, marketing, and customer service. However, seamless CRM integration with Dynamics 365 CE provides unmatched insights and operational efficiency. By integrating with external platforms: Stakeholders & Business Owners: Making Data-Driven Decisions For business owners and key decision-makers, automation isn’t just about efficiency—it’s about strategic growth and profitability. By leveraging AI and automation tools, they can: Challenges in Sales Automation and How to Overcome Them 1. User Resistance to Automation 2. Integration Difficulties 3. Lack of Proper Communication 4. Data Quality Issues Conclusion: The Future of Business is Automated, But Still Human Automation is not a replacement for human expertise—it’s a force multiplier. Businesses that embrace automation with a strategic, human-first approach will thrive in the modern market. By leveraging Dynamics 365 CE, Power Platform, and Power BI, businesses can build a scalable, insight-driven ecosystem that not only improves sales productivity but future-proofs the organization for long-term success. I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.