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Microsoft’s AI Builder is transforming how organizations incorporate AI into their applications—no data science degree required. Designed for the Power Platform, AI Builder empowers users to create intelligent apps and automated workflows that can process text, read documents, classify images, and much more.

For environments operating within GCC, GCC High and DoD tenants, understanding the current capabilities and limitations of AI Builder is essential, especially when balancing innovation with security and compliance.

🧠 What Is AI Builder?

AI Builder is a Microsoft Power Platform capability that brings AI directly into your apps and flows. It’s integrated with tools like Power Apps, Power Automate, and Power Virtual Agents (MS Copilot Studio), and supports a wide range of tasks using both prebuilt and custom models.

💳 Licensing: AI Builder Credits Explained

AI Builder operates on a credit-based system, and these credits are consumed when using AI models in your workflows or apps. If you’re using a Power Apps per user or Power Automate premium license, you already receive monthly AI Builder credits to use across your tenant.

You can view, assign, and manage these credits through the Power Platform Admin Center.

⚙️ Prebuilt vs. Custom AI Models

Prebuilt Models

These models are ready to use, no training required. Common ones include:

  • Business Card Reader

  • Sentiment Analysis

  • Key Phrase Extraction

  • Text Recognition

  • Language Detection

  • Receipt Processing

  • Category Classification

  • Text Translation

🧩 Custom Models

Need something tailored? You can build your own models, including:

  • Object Detection

  • Form Processing

  • Binary Prediction

  • Custom Text Classification

  • Custom Entity Extraction

These are especially helpful for niche document types or internal processes that require domain-specific logic.

🧾 Supported Data Types

AI Builder supports multiple modalities:

  • Text: Sentiment, keywords, categorization

  • Images: Object detection, document OCR

  • Documents: Form processing, business cards, invoices

🔐 Security: Structured, Unstructured & Grounded Data

A standout capability for enterprise environments is how AI Builder can work on both structured and unstructured data—while remaining grounded to internal data sources.

This means:

  • No external data leakage

  • Full control over what data AI sees

  • Alignment with zero-trust and compliance models

In Government environments, this grounding is vital. Most AI Builder models run within Microsoft's secure boundary, and when integrated with Dataverse, your sensitive data stays inside a compliant infrastructure.

✍️ Prompt Engineering & Generative AI

Recent updates include custom prompt capabilities, allowing you to build task-specific instructions for generative AI—ideal for summarizing documents or generating insights. This works through integration with Azure OpenAI Service, although this may be limited or unavailable in certain government tenants at the time of writing.

🔗 Where AI Builder Fits in the Power Platform

You can integrate AI Builder models directly into:

  • Power Apps – Smart input processing, form classification, object detection

  • Power Automate – Document automation, real-time text analysis

  • Power Virtual Agents – Conversational AI enriched with custom or prebuilt intelligence

📌 Final Thoughts

Whether you're building smarter intake forms, automating manual reviews, or just experimenting with text classification, AI Builder makes it possible with minimal overhead. In secure environments like GCC, GCC High and DoD, where data sensitivity is paramount, it's one of the few low-code tools that lets you embed AI while remaining grounded and compliant.


Dan Sanabria, Ph.D. (Candidate)

This article was written by Dan Sanabria, an AI Research Scientist.

Daniel Sanabria is an AI Research Scientist with a wealth of experience in artificial intelligence (AI), machine learning, and natural language processing, with a primary focus on applying these technologies to the frontier of space exploration. With a solid background in software engineering, data science, and advanced AI techniques, Daniel’s work is grounded in innovative approaches to solving some of the most complex challenges in space robotics.

His current research, as outlined in his dissertation "Traversing Mars: A Rover and AI Experience", explores the integration of AI-driven systems for autonomous operation of rovers and drones on Mars. His research seeks to leverage advanced AI techniques such as machine learning, neuromorphic computing, and quantum computing to overcome the harsh environmental constraints of Mars, such as communication delays, power limitations, and extreme terrain.

The dissertation explores interdisciplinary strategies that combine AI, physics, neuroscience, and engineering to enhance robotic autonomy, focusing on AI’s role in optimizing decision-making processes for Mars-based rovers and aerial drones. Daniel’s work is contributing to the future of autonomous exploration beyond Earth, making AI-driven systems capable of operating independently in extraterrestrial environments.

With over a decade of experience in technology and AI, Daniel is deeply committed to pushing the boundaries of AI and space exploration. He is driven by the belief that AI will be a key enabler in the next era of space missions, allowing us to explore other planets with greater autonomy, efficiency, and precision.

Education

  • PhD in Artificial Intelligence, Capitol Technology University, 2025

  • MS in Computer Science with Concentration in Artificial Intelligence, Lewis University, 2022

  • BS in Computer Science, Rasmussen University, 2020

  • AS in Application and Software Development, Rasmussen University, 2019

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