The Microsoft 365 Commercial Support Team resolves customer support cases and provides support to help you be successful and realize the full potential and value of your purchase. Our support services extend across the entire lifecycle and include pre-sales, onboarding and deployment, usage and management, accounts and billing, and break-fix support. We also spend a considerable amount of time working to improve the supportability of Microsoft 365 services to reduce the number of issues you experience as well as minimize the effort and time it takes to resolve your issues if they do occur.  

Today, we’re excited to share more about some of our supportability work with Microsoft Copilot for Microsoft 365.  

In November 2023, we posted our first article on Copilot for Microsoft 365 sharing some initial insights and guidance from the customer support team’s perspective.  Even though it seems like yesterday much has happened since then and we’ve continued to learn from all customers as they rapidly adopt and use Microsoft Copilot to increase their productivity, collaboration, and creativity every day.    

Our published guidance and resources have continued to grow and expand too, and today we’ll share answers to some of the top questions we hear from customers along with some of our favorite resources to help you better prepare and learn more.  

Plan & Deploy  

Q: What are the prerequisites for my organization to use Copilot for Microsoft 365?  

A: Copilot has specific licensing prerequisites and deployment requirements and  most of these parallel the requirements to use Microsoft 365 Apps. Note that Copilot for Microsoft 365 is a user-based license and requires that Microsoft 365 Apps on a Windows desktop are deployed using a user-based license. More information on prerequisites here > Get started with Microsoft Copilot for Microsoft 365 | Microsoft Learn  

Q: What model does Copilot for Microsoft 365 use and are there any limits on number and length of conversations or file uploads?  

A: Some important updates that apply to this were recently shared on April 2.  All licensed commercial customers will have priority access to GPT-4 Turbo in Copilot for Microsoft 365.  Limits on the number of chats per day and the number of turns per conversation have been removed.  File uploads are unlimited up to 2 GB uploads per day.  More information on the recent announcements here > Bringing the latest capabilities to Copilot for Microsoft 365 customers | Microsoft 365 Blog  

Q: Are there any special considerations when using Microsoft Purview information protection capabilities with Copilot for Microsoft 365?  

A: Yes.  Before deploying Copilot, take time to get familiar with information protection considerations and ways to strengthen your data protection solutions when using Microsoft Purview. More information on deploying with Microsoft Purview here > Considerations for deploying Microsoft Purview data security and compliance protections for Microsoft Copilot | Microsoft Learn  

Q: How does semantic index work with Copilot and what are the supported content types?  Is there anything I can do as an administrator to prepare and manage the sematic index?  

A: The semantic index enhances Microsoft Copilot and search results in the Microsoft 365 applications, SharePoint Online, and Microsoft Teams by creating two indexes, one for user-level content and data, and one for tenant-level content and data.  Each supports different file types with the latest illustrated here:  

There are optional steps that administrators can take if they wish.  More information on semantic index here > https://learn.microsoft.com/en-us/microsoftsearch/semantic-index-for-copilot  

Q: Is there anything special I need to think about with network planning and endpoints?  

A: Copilot services connect to endpoints within the Microsoft 365 Endpoint taxonomy.  As with other Microsoft 365 services, it’s recommended to align networks with the Microsoft 365 Network Connectivity Principles for optimal performance and security.  

Q: Where can I learn more about Copilot for Microsoft 365 privacy and security and how our data is handled when my users interact with Copilot?  

A: In early March, additional data residency capabilities were announced here. Full details on data, privacy, and security for Microsoft Copilot for Microsoft 365 can be found here > Data, Privacy, and Security for Microsoft Copilot for Microsoft 365 | Microsoft Learn  

Q: Does Microsoft have a playbook or set of resources to help me plan and implement Copilot for Microsoft 365 for my organization and drive successful adoption?  

A: Comprehensive resources are available to assist in discovering, planning, and implementing Copilot for organizations of all sizes. This includes guidance for working across leadership, IT, and end users.  A full set of adoption resources can be found here > Copilot for Microsoft 365 – Microsoft Adoption  

Manage & Use  

Q: What level of controls are available to IT Admins for Copilot for Microsoft 365?  

A: By using the Copilot page in the Microsoft 365 admin center, IT Admins can manage how users in their organization interact with Copilot for Microsoft 365.  More information on Copilot management capabilities here > Manage Microsoft Copilot for Microsoft 365 with the Copilot page | Microsoft Learn  

Q: What languages does Copilot for Microsoft 365 support?  

A: Support for an additional 16 languages was recently announced.  Details on Copilot supported languages here > Supported languages for Microsoft Copilot – Microsoft Support  

Q: I assigned a Copilot license to a user but it’s not showing up for them. What should I do?  

A: After assigning a Copilot license to a user, it can take up to 72 hours for the background refresh of the license to take effect and for Copilot to show up in applications. Administrators can force a refresh by applying the File > Account > Update license button (located directly under the Microsoft 365 Apps for Enterprise logo).    

More information here > How to find and enable missing Copilot button in Microsoft 365 apps – Microsoft Support  

Q: Ok, so Copilot still doesn’t show up in Excel, OneNote, PowerPoint, and Word… what now?  

A: This will happen if Connected Experiences have been turned off in your organization (which enables analyzing of content for Microsoft 365 apps) including when users are using Windows Information Protection or Microsoft Defender Application Guard. To enable Copilot in this scenario, policy settings for connected experiences will need to be turned on. The following are indications that Connected Experiences are disabled:   

Office Cloud policy for “Allow the use of connected experiences in Office that analyze content” is disabled.  
Office ULS log shows “Data.IsPrivacyEnabled”:false” in the  Office.AugLoop.Client.Liblet.StartRuntimeAttempt event.  
Either of these registry keys are enabled:  

HKEY_CURRENT_USERSoftwarePoliciesMicrosoftoffice16.0commonPrivacyusercontentdisabled  
HKEY_CURRENT_USERSoftwarePoliciesMicrosoftoffice16.0commonPrivacydisconnectedstate  

More information on connected experience settings here > Data, Privacy, and Security for Microsoft Copilot for Microsoft 365 | Microsoft Learn  

Q: My users are getting mixed results when working with large documents and Copilot in Word. Any tips?  

A: When referencing documents with Copilot in Word, try to limit your document size to 20 pages and 15,000 words or less. This is the sweet spot for Copilot to work effectively. More information on working with Copilot in Word here > Keeping it short and sweet: a guide on the length of documents that you provide to Copilot – Microsoft Support  

Q: My users are really excited about the email summarization, drafting, and coaching capabilities with Copilot in Outlook but are running into occasional issues. Any limitations to be aware of?  

A: Copilot scenarios in Outlook are only available on a user’s primary mailbox and not with shared, archived, group, or delegated mailboxes. With Coaching by Copilot, a user’s email draft needs to be at least 100 characters to use the Coaching capability. There’s no limit on prompt length when using Draft with Copilot.  More information on working with Outlook here > Frequently asked questions about Copilot in Outlook – Microsoft Support  

Q: Users are bumping into some latency issues when working with Copilot in Excel. Any suggestions?  

A: Copilot in Excel works for Excel tables of up to 2 million cells. Skills like formula column suggestions, highlight, sort, and filter do not have data limits. Keep in mind that it can take some time to get a response when working with large tables. Note that Copilot in Excel only works with files that are hosted in OneDrive or Microsoft 365 SharePoint locations and have Autosave turned on.  More information on working with Excel here > Frequently asked questions about Copilot in Excel – Microsoft Support  

Q: What are some things my users can do to get better performance when using Copilot in Microsoft Teams?   

A: Limit questions to topics covered in the chat or meeting.  When conducting meetings and calls, speak or chat in supported languages. With chat scenarios, keep in mind that Copilot can only process as far back as 30 days from the last message sent. This may be further limited by retention policies that are set within your organization.  More information on working with Microsoft Teams here > Frequently asked questions about Copilot in Microsoft Teams – Microsoft Support  

Q: Some of my users are seeing issues or missing results when retrieving files to reference in Copilot with Word Online, PowerPoint Online, and Excel Online. What’s causing this?  

A: For Copilot to work with Office web applications like Word Online, PowerPoint Online, and Excel Online, third-party cookies must be enabled. Blocking third-party cookies will result in a failure when retrieving files to reference. More information on requirements here > https://learn.microsoft.com/en-us/microsoft-365-copilot/microsoft-365-copilot-requirements  

Q: I have users that aren’t seeing relevant documents or information, including personalized content (fx. suggested meeting hours) when using Copilot. Is there a way to fix this?  

A: This may be related to semantic index. Check and make sure Enterprise Search is enabled in the Microsoft 365 Admin Center. And then make sure Item insights are enabled. Having Item or People insights turned off will reduce the Microsoft Search and semantic index experience. More information here > https://learn.microsoft.com/en-us/microsoftsearch/semantic-index-for-copilot   

Copilot, Your Support Assistant  

Finally, keep in mind that you can always use Copilot as your support and troubleshooting assistant and even ask it to provide suggested prompts to use for ways it can help with different scenarios. Below is a quick example using Copilot in the browser.  There’s  a great mobile app (powered by GPT-4!) too.  

As with any scenario, make sure to use good prompting techniques to help Copilot provide the responses and results that you want. This includes:  
 

Providing complete details and sufficient context about the scenario or issue.  
Instructing on any specific information sources that Copilot should use when responding.  
Guiding how Copilot responds by telling it to act like a certain role or expert (e.g. “act like a technical expert on Exchange Online” or “act like a solution architect specializing in Teams meeting rooms and calling”).  
Consider the sequence and order of the context, example, and instruction details in your prompts. Experimenting with the order and when specific sources are referenced can have an impact on the quality of responses.  
Focusing on positive instructions and telling Copilot what to do (versus what not to do) often generates the best results.    

 
There’s a ton of great learning and resource material out there on prompt engineering. If you need a starting point, take a look at these resources > Learn about Copilot prompts – Microsoft Support and Copilot Lab (cloud.microsoft).  

Hope you found this helpful, and we’ll continue to share updates from our Copilot journey here in Microsoft 365 commercial support!  

 Brian Stoner is a Director in the CSS Modern Work Supportability Team where he leads a team of technical and business program managers.  

The CSS Modern Work Supportability Team delivers innovative self-help solutions and diagnostics, in-service enhancements, and support programs to help customers get maximum value from their Microsoft 365 commercial subscriptions and create an easy-to-use, connected support experience.  

​Microsoft Tech Community – Latest Blogs –Read More 

Knowing how technology works eases your mind, minimizes confusion, and deepens your understanding as you incorporate it into your daily work routine.

Copilot for Microsoft 365 leverages large language models (LLMs) that interact with your data using the Microsoft Graph and optionally the Internet to generate personalized experiences with related context as you work. Copilot for Microsoft 365 provides a powerful orchestration engine and combines the power of Large Language Models, your universe of data via Microsoft Graph, the Internet, and your Microsoft 365 apps. Importantly, Copilot for Microsoft 365 only uses information that you explicitly have access to. It only remembers your conversation while it’s in that conversation and we don’t use your information to train the Large Language model. The chat history is wiped clean with each new conversation.

OK, let’s go behind the scenes to further understand the architecture and dataflows behind Copilot for Microsoft 365, including retrieval augmented generation techniques, app commanding, and options to securely connect to external data. You’ll see the potential within chat and how it can save you time with examples of specific use cases.

Watch | “How Copilot for Microsoft 365 Works” presented by A.J. Brush and Mary David Pasch at the recent Microsoft 365 Community Conference #M365Con:

PowerPoint slide screenshot “Copilot for Microsoft 365 architecture” as presented at the Microsoft 365 Community Conference | April 30 – May 2024 | Orlando, Florida.

More on demand | Recent long-form Copilot and AI videos from Microsoft

“General Session: Reshaping productivity with Copilot for Microsoft 365” with Bobby Kishore and Dan Parish
“Delivering Business Value & User Satisfaction in the Era of AI” with Karuana Gatimu
“Harnessing Copilot and AI for Teams meeting effectiveness” with Shiraz Cupala
“Prepare your content for Microsoft Copilot with SharePoint content governance” with Dave Minasyan, Sanjoyan Mustafi, and Chris Bortlik
“Real World Lessons Managing Copilot for Microsoft 365” with Chris Bortlik and Manfred Koch
“Driving User Satisfaction w/ AI for Microsoft employees” with Claire Sisson
“Create an AI-Ready Organization – Technical & User Enablement Best Practices for Copilot for Microsoft 365” with Karuana Gatimu, Olga Gordon, and Cynthia Johnson
“Understanding Microsoft 365 Copilot Extensibility via Microsoft Graph connectors” with Gladys Alvarez Jimenez, and Brian T. Jackett
Review all Microsoft 365 Community Conference session videos (links to our main playlist of all]

Additional, important Copilot and AI resources from Microsoft

Copilot hub on adoption.microsroft.com
Insight into how Microsoft operates its AI services. #ResponsibleAI
Download the Copilot for Microsoft 365 Success Kit
Read “The Art and Science of Working with AI” (on Worklab)
Microsoft Copilot for Microsoft 365 overview article on Microsoft Learn
Learn more about expanding Copilot with Microsoft Graph connectors

Thanks for your time discovering more about Copilot for Microsoft 365, Karuana Gatimu and Mark Kashman

​Microsoft Tech Community – Latest Blogs –Read More 

The generative AI landscape moves at a fast pace, and it can be challenging to keep up with the latest developments, even for seasoned developers. There are in particular two questions that often come up when starting a new AI project:

How can I quickly validate an AI app idea, without investing too much time and resources?
If I have a working prototype, how fast can I scale it to production?

You don’t want to be outpaced by competitors or newer technologies, and you want to be able to quickly iterate on your ideas or pivot to new ones. This is where LangChain.js comes in. It’s a framework that allows you to build AI applications very little adherence to the underlying AI technologies and tools. It abstracts some of the complexity of AI development, allowing you to focus on the business logic of your application.

In this article, we’ll take you through the development journey, starting from an idea and progressing towards production. We’ll explore how LangChain framework together with Azure AI building blocks allows you to quickly build complex AI applications at the various stages of development.

Note: If you prefer to watch a video version of this article, you can find it on YouTube here.

TL;DR key takeaways

AI is not only reserved to Python developers: JavaScript developers also have everything they need to build AI applications.
LangChain.js provides useful abstraction over AI models and APIs, allowing you to switch between them easily. This is particularly useful when you’re experimenting with different models or when you want to scale your application, moving from a local SLM model to a cloud-based LLM.
Ollama is allow you to experiment with AI models and embeddings locally, at no cost (if you have a powerful enough machine).
Azure provides many AI building blocks and services that you can use to scale your application to production.

Here’s the source code on GitHub of the project we use as an example in this article.

If you like the project, don’t forget to give it a star :star:️!

Working locally with Ollama

Ollama is a command-line tool that allows you to run AI models locally on your machine, making it great for prototyping. Running 7B/8B models on your machine requires at least 8GB of RAM, but works best with 16GB or more. You can install Ollama on Windows, macOS, and Linux from the official website: https://ollama.com/download.

Once you have Ollama installed, let’s first download some models. You can find a list of available models on the Ollama website. For this example, we’ll use the Phi-3 Mini model. Open a terminal and run the following command:

ollama pull phi3

Note: This will download a few gigabytes of data, so make sure you have enough space on your machine and a good internet connection.

Once the model is downloaded, you can start interacting with the Ollama server. For example, you can use the ollama run command to generate text based on a prompt:

ollama run phi3 “What is artificial intelligence? Explain it to a 5 years old child.“

You can also have a minimal ChatGPT-like experience right from you terminal by just running:

ollama run phi3

You can then chat with the model interactively. Once you’re done, you can stop the server by pressing Ctrl+D. Ollama also provides a REST API that you can use to interact with the model. The API provides many options, like streaming, JSON mode, and more. Here’s an example of how you can use the API:

curl http://localhost:11434/api/generate -d ‘{
“model”: “phi3”,
“prompt”: “What is artificial intelligence? Explain it to a 5 years old child.”,
“stream”: false
}‘

After running this command you should see a JSON response from the model Ollama even provides an OpenAI compatible API, so you can use it as drop-in replacement for OpenAI models in your applications. And as Ollama runs entirely on your machine, it means you don’t even need a network connection to use it.

While Ollama is great for experimentation and prototyping, keep in mind that smaller models are not as powerful as the larger models available in the cloud. While it might be enough to validate your idea, you’ll probably want to switch to cloud-based models for production to get better results.

Prototyping with LangChain.js

Now that we know how to run AI models locally, let’s see how we can use LangChain.js to quickly prototype an AI application. LangChain.js is a JavaScript framework that provides a high-level API to interact with AI models and APIs with many built-in tools to make complex AI applications easier to build.

Let’s start with a simple example project from scratch. Open a terminal and run the following commands:

# Creates a new folder and initializes a new Node.js project
mkdir langchain-demo
cd langchain-demo
npm init es6 -y
npm i langchain @langchain/core @langchain/community pdf-parse faiss-node
touch index.js

Now open the index.js file in your favorite code editor and add the following code:

import { HumanMessage, SystemMessage } from “@langchain/core/messages”;
import { ChatOllama } from “@langchain/community/chat_models/ollama”;

const model = new ChatOllama({ model: “phi3” });
const response = await model.invoke([
new SystemMessage(“You’re a helpful assistant”),
new HumanMessage(“Say hello”),
]);

console.log(response.content);

Run the code with node index.js. You should see the response from the model in the console. Congrats, you’ve just built the hello world of AI chatbots!

A more complex example

What if I want to use RAG (Retrieval-Augmented Generation) to ground the answers using documents? Let’s update our index.js file with the following code:

import { RecursiveCharacterTextSplitter } from “langchain/text_splitter”;
import { createStuffDocumentsChain } from “langchain/chains/combine_documents”;
import { createRetrievalChain } from “langchain/chains/retrieval”;
import { ChatPromptTemplate } from “@langchain/core/prompts”;
import { ChatOllama } from “@langchain/community/chat_models/ollama”;
import { OllamaEmbeddings } from “@langchain/community/embeddings/ollama”;
import { FaissStore } from “@langchain/community/vectorstores/faiss”;
import { PDFLoader } from “@langchain/community/document_loaders/fs/pdf”;

// 1. Initialize the models
const model = new ChatOllama({ model: “phi3” });
const embeddings = new OllamaEmbeddings({ model: “all-minilm:l6-v2” });

// 2. Load PDF document and split it into smaller chunks
const loader = new PDFLoader(“terms-of-service.pdf”, { splitPages: false });
const pdfDocument = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 100 });
const documents = await splitter.splitDocuments(pdfDocument);

// 3. Put the documents into a vector store and convert them to vectors
const store = await FaissStore.fromDocuments(documents, embeddings, {});

// 4. Create the RAG chain that retrieves and combines the prompt with the documents
const combineDocsChain = await createStuffDocumentsChain({
llm: model,
prompt: ChatPromptTemplate.fromMessages([
[“system”, “You’re a helpful assistant”],
[“human”, “Answer the question: {input}nusing the following documents:nn{context}”],
]),
});
const chain = await createRetrievalChain({
retriever: store.asRetriever(),
combineDocsChain,
});

// 5. Generate the result
const response = await chain.invoke({ input: “What’s our mission?” });
console.log(response);

This code will load a PDF document, split it into smaller chunks, convert them to vectors, and then use them in a multi-step workflow (chain) to perform a vector search and generate a response using the best results. Pheeew! This one is a more complex example, but it shows how LangChain.js can help you build more advanced AI scenarios in a few lines of code.

Before running this code, you first need to download this PDF document and put it in the langchain-demo folder.

We also need to download the embeddings model. You can do this by running the following command:

ollama pull all-minilm:l6-v2

This one is very small (~50MB), and helps converting text to vectors. Now you can run your code with:

node index.js

The resulting answer directly comes from the PDF document, you can open it and look at the original mission statement at the beginning of the document to see how the model used it in its response.

Using the same principles as this example, we’ve prototyped a chatbot for the Contoso Real Estate company: we’ve built an experience that allows customers to ask support questions about the usage of its products. You can find the full source code of the project on GitHub. The final results, with an added chat UI, look like this:

Now that we have a working prototype, let’s see how we can deploy it to production using Azure.

Migrating to Azure

Azure provides many AI services that you can use for your applications, in our case we’ll use Azure OpenAI for the models and Azure AI Search as our vector database. Thanks to LangChain.js abstraction, migrating your prototype to Azure for production is relatively straightforward, as you can swap the models and vector database without changing anything else in your code.

If you look at the chat API code, this is what we use to run the code locally with Ollama:

embeddings = new OllamaEmbeddings({ model: ollamaEmbeddingsModel });
model = new ChatOllama({
temperature: 0.7,
model: ollamaChatModel,
});
store = await FaissStore.load(faissStoreFolder, embeddings);

Switching to Azure OpenAI and Azure AI Search is as simple as changing the model and store initialization:

const credentials = getCredentials();

embeddings = new AzureOpenAIEmbeddings({ credentials });
model = new AzureChatOpenAI({
temperature: 0.7,
credentials,
});
store = new AzureAISearchVectorStore(embeddings, { credentials });

We use passworless authentication for increased security, so we don’t need to store any secrets in our code. The implementation of the getCredentials was omitted for simplicity, but you can find it here.

And that’s it for the migration, at least for the code part, as to make it work you still have to create the necessary resources in Azure. We’ll cover this in the next section.

Azure Developer CLI

As developers, we know infrastructure is no fun, but it’s a necessary part of deploying applications to the cloud. Azure provides a tool called Azure Developer CLI that makes it easier to create and manage resources in Azure. It allows you to use Infrastructure as Code to define your resources in a declarative way, and then deploy them with a single command.

We won’t cover in details how to build the infrastructure templates, if you’re curious though you can have a look at the infra folder and the azure.yaml file used to configure and deploy the resources.

The good news is that we have many samples that you can use as a starting point for your own project infrastructure. Once the configuration is done, you can create the resources and deploy the application with a few commands:

# Authenticate to Azure
azd auth login
# Provision and deploy the resources
azd up

You can try it with our Serverless AI Chat project we used as an example in this article.

Azure building blocks

We skipped a bit over the implementation details of our example project, but to build it quickly we used some of the already existing Azure AI building blocks. Here’s a list of some components we used, that you can reuse in your own projects:

OpenAI Node.js SDK: we announced at Build a new integration of Azure OpenAI with the official OpenAI Node.js SDK, meaning it’s now easier than ever to switch between OpenAI and Azure OpenAI models. The LangChain.js Azure OpenAI integration has also been updated to use this new SDK.

Azure integrations in LangChain.js: we’ve contributed support for many Azure services in LangChain.js, to make it easier to build your AI applications on top of Azure. This includes Azure OpenAI, Azure AI Search, Azure CosmosDB, and more. You can find more information in the LangChain.js documentation.

AI Chat protocol: we’ve defined an API schema for AI chat applications, to make the frontend and backend components communicate. This schema is implemented in many of our AI samples, making them interoperable and easy to extend. We also provide an NPM package that includes the TypeScript types for the data objects and a client library to interact with the API.

AI Chat UI components: if you want to focus on the backend part of your AI chat application, we provide a set of web components that implements the AI Chat protocol. You can use them to quickly build a chat UI for your application. And since most of our AI samples also implement the protocol, you can also reuse any of their frontend component if you prefer, like the one we used in our example project.

Conclusion

We’ve covered a lot of ground in this article, starting from running AI models locally with Ollama, to prototyping a chatbot with LangChain.js, and finally deploying it to production on Azure. In a fast-paced environment like AI development, using JavaScript with existing high-level frameworks like LangChain.js and building on top of off-the-shelf building blocks can help you iterate quickly on your ideas and eventually bring them to production. you can quickly iterate on your ideas and bring them to production with the right tools and services.

Reference links

Here are some useful links to get you started with the tools and services we’ve mentioned in this article:

LangChain.js
Ollama
Azure Developer CLI
Azure OpenAI Node.js SDK
AI Chat protocol
AI Chat UI components
Serverless AI Chat sample

​Microsoft Tech Community – Latest Blogs –Read More 

Hi @alanderfield 

I am facing a problem with report access and I am faced with this error while trying to log in. Please see image- even as I refresh, it stays on the same page. 

This is quite troubling as we are to do a client insights presentation this week but I am not able to log in with this error. I have reached out to the fast track MS team who were supposed to guide this client but they were not able to solve it. The client is also OOO the whole week and we are not able to get them to help raise a ticket on the MAC site. 

Please advise if anything could be done on the back end? This problem has been here since last Thursday.

​Hi @alanderfield I am facing a problem with report access and I am faced with this error while trying to log in. Please see image- even as I refresh, it stays on the same page.  This is quite troubling as we are to do a client insights presentation this week but I am not able to log in with this error. I have reached out to the fast track MS team who were supposed to guide this client but they were not able to solve it. The client is also OOO the whole week and we are not able to get them to help raise a ticket on the MAC site.  Please advise if anything could be done on the back end? This problem has been here since last Thursday.   Read More

​