Our team has developed virtual assistants for healthcare organizations, also published in Azure Marketplace. This can be used for a personal assistant perspective. We have also developed an application for one of the fertilizer companies. Here, a farmer can go to their application, click a photo of any disease or progress in the plant, and it will identify what type of fungus or disease that plant has. Accordingly, it will recommend what kind of fertilizers and how to use them. These are a couple of use cases we have worked on.
Senior management assistance for Christian Roy at a tech services company with 1-10 employees
Real User
Top 20
2024-05-01T15:21:00Z
May 1, 2024
We used the model to produce the dashboards. They created them, but we weren't satisfied because they weren't interactive for decision-making and governance. We integrated Azure with SharePoint and AI to create an interactive model. That's what we did with Azure for our specific project. We use Azure, but what we put in place is not just Azure. We created interactive dashboards. These allow people to instantly understand the situation when they see a red code, for instance. This enables governance to make a strong diagnosis of the situation and resolve it. It also helps integrate all the digital elements that affect decision-making in project resolution. This allows for evaluation and restructuring of project scope with an agile approach, and to put in place solutions to integrate stabilizing elements. The project I completed for this specific issue last year was a big success and is now being used by the entire department. I'm an IT integrator. When I use Azure, if the model meets the need, I use what the system offers.
Country Director (Sri Lanka)/ Executive RPA Lead - Asia Region at a tech services company with 51-200 employees
Real User
Top 20
2024-04-23T14:57:00Z
Apr 23, 2024
We utilize the product for various applications that handle unstructured or semi-structured data, such as question-and-answer systems, chatbots, and text-generation tasks.
Analyst Developer at a government with 1,001-5,000 employees
Real User
Top 20
2024-03-08T23:10:43Z
Mar 8, 2024
We're implementing an assistant using Azure OpenAI. The challenge is grounding OpenAI responses to our specific data. We can only offer users basic querying, like for documents they're stuck on. It handles the request. It's primarily the question-answering feature.
For Azure OpenAI, currently, we have two main use cases. One is code generation, legacy modernization in code, unit test-based integrations, and Symantec Telstra integrations. So, everything related to ADM and SDMC is a top use case. The other use case is mostly related to documentation, test text summarization, and text creation. So, the top use cases are the engineering use cases regarding code generation, with test case automation and unit testing solutions.
Head of IT at a manufacturing company with 1,001-5,000 employees
Real User
Top 5
2023-12-11T03:52:39Z
Dec 11, 2023
There are a couple of use cases. The first one involves an educational institute that has a massive amount of documentation. They have around 30,000 PDFs, most of which are used by project managers. Each PDF averages around 30 pages and covers topics like new risk management, product management, and so on. My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context. The first demo I gave involved someone from the construction industry looking for ideas on mitigating risks related to team and material management. Azure OpenAI provided an immediate answer based on our own internal knowledge base, not a public one. The user then asked how they could become more proficient in this area. We suggested some certifications available through our system. Having a large number of documents can make it difficult for people to find the information they need. Even when they find the relevant document, it might be very long, making it time-consuming to locate the specific answer. It's especially challenging because the documents are PDFs, not web pages. It was difficult for users to get the precise answer they needed. Previously, we used Elasticsearch, which could find the relevant document but couldn't provide the answer directly. That's where Azure OpenAI comes in. We used Azure Cognitive Search and Azure OpenAI together to achieve this user experience. I primarily use it for documentation. That's the main function we're using it for. My second use case involves a contact center solution. Many big companies use contact center solutions like Google Dialogflow to replace human agents with bots. This is my next successful use case. I've deployed it for a company on a pilot basis, and they're running campaigns with it. Instead of human agents, the bot is able to answer customer inquiries over the phone.
We work with a lot of enterprise-level customers in healthcare, retail, and manufacturing. The solution can be used to predict diseases based on X-rays in healthcare and for predictive analytics in retail. In manufacturing, we need more than 100 images to train the models. DALL·E can generate more than 1000 images based on the specific descriptions we give.
We are assisting our customers in deploying a commercial universal AI solution aimed at aiding them in researching and managing their internal company policies and regulations. To do this, I've extracted all the relevant documents from the HR department and created conversational interfaces for our clients. These interfaces are integrated into various platforms like Microsoft Teams, allowing everyone within the company to interact with the AI.
I was freelancing for a company that wanted me to make tutorials on how the platform can be used. So, here are just a few model-building video tutorials I made from the platform. That's pretty much it.
The main use case for Azure OpenAI is invoice processing. The first step is to recognize the text from images through Azure Cognitive Services, and then utilize Azure OpenAI to extract relevant information from the text. It provides more accurate information extraction compared to Azure Recognizer. This automation helps streamline the accounting process.
The Azure OpenAI service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.
Our team has developed virtual assistants for healthcare organizations, also published in Azure Marketplace. This can be used for a personal assistant perspective. We have also developed an application for one of the fertilizer companies. Here, a farmer can go to their application, click a photo of any disease or progress in the plant, and it will identify what type of fungus or disease that plant has. Accordingly, it will recommend what kind of fertilizers and how to use them. These are a couple of use cases we have worked on.
We used the model to produce the dashboards. They created them, but we weren't satisfied because they weren't interactive for decision-making and governance. We integrated Azure with SharePoint and AI to create an interactive model. That's what we did with Azure for our specific project. We use Azure, but what we put in place is not just Azure. We created interactive dashboards. These allow people to instantly understand the situation when they see a red code, for instance. This enables governance to make a strong diagnosis of the situation and resolve it. It also helps integrate all the digital elements that affect decision-making in project resolution. This allows for evaluation and restructuring of project scope with an agile approach, and to put in place solutions to integrate stabilizing elements. The project I completed for this specific issue last year was a big success and is now being used by the entire department. I'm an IT integrator. When I use Azure, if the model meets the need, I use what the system offers.
We utilize the product for various applications that handle unstructured or semi-structured data, such as question-and-answer systems, chatbots, and text-generation tasks.
When I write documentation, I use the solution for translating anything and researching.
We're implementing an assistant using Azure OpenAI. The challenge is grounding OpenAI responses to our specific data. We can only offer users basic querying, like for documents they're stuck on. It handles the request. It's primarily the question-answering feature.
We use the solution for Document Intelligence, ChatGPT, and NLP.
For Azure OpenAI, currently, we have two main use cases. One is code generation, legacy modernization in code, unit test-based integrations, and Symantec Telstra integrations. So, everything related to ADM and SDMC is a top use case. The other use case is mostly related to documentation, test text summarization, and text creation. So, the top use cases are the engineering use cases regarding code generation, with test case automation and unit testing solutions.
There are a couple of use cases. The first one involves an educational institute that has a massive amount of documentation. They have around 30,000 PDFs, most of which are used by project managers. Each PDF averages around 30 pages and covers topics like new risk management, product management, and so on. My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context. The first demo I gave involved someone from the construction industry looking for ideas on mitigating risks related to team and material management. Azure OpenAI provided an immediate answer based on our own internal knowledge base, not a public one. The user then asked how they could become more proficient in this area. We suggested some certifications available through our system. Having a large number of documents can make it difficult for people to find the information they need. Even when they find the relevant document, it might be very long, making it time-consuming to locate the specific answer. It's especially challenging because the documents are PDFs, not web pages. It was difficult for users to get the precise answer they needed. Previously, we used Elasticsearch, which could find the relevant document but couldn't provide the answer directly. That's where Azure OpenAI comes in. We used Azure Cognitive Search and Azure OpenAI together to achieve this user experience. I primarily use it for documentation. That's the main function we're using it for. My second use case involves a contact center solution. Many big companies use contact center solutions like Google Dialogflow to replace human agents with bots. This is my next successful use case. I've deployed it for a company on a pilot basis, and they're running campaigns with it. Instead of human agents, the bot is able to answer customer inquiries over the phone.
We work with a lot of enterprise-level customers in healthcare, retail, and manufacturing. The solution can be used to predict diseases based on X-rays in healthcare and for predictive analytics in retail. In manufacturing, we need more than 100 images to train the models. DALL·E can generate more than 1000 images based on the specific descriptions we give.
We are assisting our customers in deploying a commercial universal AI solution aimed at aiding them in researching and managing their internal company policies and regulations. To do this, I've extracted all the relevant documents from the HR department and created conversational interfaces for our clients. These interfaces are integrated into various platforms like Microsoft Teams, allowing everyone within the company to interact with the AI.
I was freelancing for a company that wanted me to make tutorials on how the platform can be used. So, here are just a few model-building video tutorials I made from the platform. That's pretty much it.
I use Azure OpenAI to create message dashboards for my company.
The main use case for Azure OpenAI is invoice processing. The first step is to recognize the text from images through Azure Cognitive Services, and then utilize Azure OpenAI to extract relevant information from the text. It provides more accurate information extraction compared to Azure Recognizer. This automation helps streamline the accounting process.