Build Powerful Automations with No-Code, Drag-and-Drop AI Builder

You can train and build custom AI models to address unique business needs. For a website, this could include training models to recognize specific images or patterns, analyze user feedback sentiment, or extract data from unstructured text on the site.

• Model Training: With Custom AI Models, you can train machine learning models using your own data. This data can be specific to your business processes or website needs. For example, you can create models for image recognition, text classification, or regression analysis based on your unique data sets.
• No-Code/Low-Code Approach: AI Builder is designed to be user-friendly, even for those without extensive machine learning expertise. It offers a low-code/no-code approach to building and training models. This means you don't need to write complex code or algorithms to train your models.
• Model Evaluation: After training a model, AI Builder provides tools to evaluate its performance. You can assess metrics like accuracy, precision, recall, and F1-score to determine how well the model is likely to perform on new, unseen data.
• Text Classification: A template that can be used for categorizing and tagging text data, helpful for content classification or sentiment analysis on your website.

It can analyze text data, including sentiment analysis, key phrase extraction, language detection, and translation. This is valuable for monitoring customer sentiment from website reviews and feedback.

• Sentiment Analysis: Sentiment analysis models are used to determine the sentiment or emotional tone expressed in text, whether it's positive, negative, or neutral. These models are commonly used in customer feedback analysis, social media monitoring, and opinion mining.
• Key Phrase Extraction: Key phrase extraction models identify and extract important phrases or terms within a piece of text. This helps in summarizing content and identifying the main topics or themes discussed.
• Language Detection: Language detection models can automatically identify the language of a given text, which is particularly useful when dealing with multilingual content on websites or in global business contexts.
• Named Entity Recognition (NER): NER models identify and categorize named entities within text, such as names of people, organizations, locations, dates, and more. This is useful for extracting structured information from unstructured text.

• Text Summarization: Text summarization models automatically generate concise and coherent summaries of longer text documents. This is useful for providing quick overviews of articles or reports on a website.
• Intent Analysis: Intent analysis models aim to determine the intent or purpose behind a user's text, such as a query on a website. They are used in chatbots, virtual assistants, and search engines to provide relevant responses.
• Customization and Training: Many text analytics AI models and templates can be customized and trained to suit specific business needs. This allows organizations to fine-tune models for domain-specific terminology and requirements.

Using Models & Datasets, you can create automation flows that incorporate AI Builder models. For instance, you can automatically trigger actions on your website, such as sending personalized responses to user inquiries or routing customer requests based on their sentiment.

• Triggered Events: Automation flows can be triggered by various events, such as the submission of a form on a website, the receipt of an email, or a change in a data source. When an event occurs, the flow can be configured to perform specific actions, including the use of AI models.
• Seamless Integration: AI models built using AI Builder can be seamlessly integrated into these flows. For example, you can create a flow that detects sentiment in customer feedback submitted through a website contact form and automatically routes positive feedback to a different department than negative feedback.
• Conditional Logic: You can use conditional statements to make decisions within your flows based on AI model predictions. For instance, you might decide to escalate customer complaints if sentiment analysis indicates strong negative sentiment.
• Data Transformation: Flows can also perform data transformations, such as data validation and enrichment, before using AI models. This ensures that the data fed into the AI model is in the right format and quality.

• Multi-Step Workflows: Automation flows can consist of multiple steps, allowing you to create complex, multi-step workflows that involve both AI-driven and traditional actions. For example, a website registration process could include AI-based identity verification followed by user account creation.
• Real-Time or Scheduled Execution: Flows can be triggered in real-time or scheduled to run at specific times, ensuring flexibility in how you automate processes on your website.

AI Builder can be integrated with other external services, databases, and web APIs, allowing you to pull data from external sources or push processed data back to your website or other systems.

• Custom Model Training: AI Builder allows you to create and train custom AI models to meet your specific business needs. You can use your own data to train models for tasks like object detection, classification, and text extraction. Once your custom model is trained, it can be accessed via APIs.
• AI Builder APIs: Microsoft provides APIs that allow you to interact with your AI Builder models and templates programmatically. These APIs can be used to send data to the model, retrieve predictions, and integrate the AI capabilities into your website or applications.
• RESTful APIs: The AI Builder APIs are typically RESTful APIs, which use HTTP methods (GET, POST, PUT, DELETE) for communication. This means you can make HTTP requests to these APIs from your website or application.
• Real-time and Batch Processing: AI Builder APIs can be used for real-time processing, where you send data to the API and get immediate results, or for batch processing, where you send a batch of data for processing and receive results later.
• Documentation and Support: Microsoft provides documentation and support for AI Builder and its APIs. Utilize these resources to understand how to integrate and use AI Builder effectively.

One of the main advantages of AI Builder is that it doesn't require extensive coding knowledge. It's designed for business users, and you can create AI-driven automation with minimal coding.

• Pre-Built AI Models: No-code platforms typically provide pre-built AI models and templates that cover common AI use cases. These models are ready-made and can be easily integrated into your applications or processes. Examples of pre-built AI models might include sentiment analysis, object recognition, language detection, and data extraction from forms.
• Customization: While pre-built models are designed to address common scenarios, they are also customizable. Users can fine-tune these models to meet specific business requirements.
• Drag-and-Drop Models and Workflows