5 min read
Add AI to your business processes without building a machine learning pipeline.
Microsoft AI Builder brings prebuilt and custom AI models into Power Apps and Power Automate. It handles document processing, text classification, object detection, prediction, and entity extraction. Your team uses these capabilities inside the tools they already work in, without data science expertise or separate infrastructure.
AI Builder runs inside the Power Platform security boundary. Your data stays in your tenant. Models train and run on Microsoft infrastructure with the same compliance certifications as the rest of your Microsoft 365 and Azure environment.
Work out of the box with no training. Invoice processing, receipt scanning, business card reading, text recognition (OCR), sentiment analysis, language detection, key phrase extraction, and category classification. Call them from a Power Automate flow or a Power Apps screen and get structured output back.
Train on your data for organization-specific tasks. Document processing models extract fields from your specific form layouts (insurance claims, purchase orders, inspection reports). Classification models categorize text into your defined categories. Prediction models forecast outcomes based on historical Dataverse data. Object detection models identify items in images using your labeled training data.
AI Builder is strong at structured document processing, simple classification, and prediction on tabular data. It is not a replacement for Azure AI Services, custom machine learning models, or large-scale data processing. Choosing the wrong tool wastes credits and delivers poor results.
Custom models need clean, representative training data. Document processing models need 5-15 well-varied samples at minimum. Prediction models need hundreds or thousands of historical records with consistent data quality.
Teams build a standalone document processing flow but do not connect it to the downstream business process. Invoices get scanned, but the extracted data does not flow into the approval workflow, the ERP system, or the reconciliation process.
AI Builder consumes credits per prediction. Organizations do not track consumption, hit their credit limit mid-month, and production flows stop working. Credit allocation and monitoring must be part of the deployment plan.
We assess whether AI Builder is the right tool for your problem. If it is, we define the model type, the data requirements, and the expected accuracy threshold. If Azure AI Services or a different approach fits better, we say so upfront.
We help structure and clean the training data your custom models need. For document processing, we guide sample collection and labeling. For prediction models, we assess your Dataverse data quality and volume.
We train custom models, evaluate accuracy, iterate on training data, and validate performance against your acceptance criteria before integrating into production flows.
We build the Power Automate flows and Power Apps screens that put AI Builder models into your business processes. Document processing flows that extract, validate, route, and store data. Classification triggers that automate case routing. Prediction dashboards that surface forecasts to decision-makers.
We configure credit allocation, usage monitoring, and alert thresholds. We document which models run in production, what data they access, and who owns them. This integrates with your broader Power Platform Governance framework.
AI Builder models and their associated flows deploy through the same CI/CD pipelines as your other Power Platform solutions. See our Power Platform ALM approach.
Every engagement produces named, tangible deliverables. Configured infrastructure and working documentation.
Our Power Platform Assessment includes an AI readiness review: current data inventory, use case prioritization, credit allocation planning, and implementation roadmap.
