Copilot Extensions for Custom Entities

The rapid rise of AI-powered assistants—branded “Copilots” across Microsoft’s ecosystem—has redefined how users interact with business applications. Whether embedded in Office apps, Power Platform, or Dynamics 365, Copilot capabilities are increasingly infused into user workflows, allowing users to generate content, extract insights, and automate tasks with natural language.

But business applications are rarely one-size-fits-all. Most enterprises use custom entities to represent domain-specific concepts like inspections, audits, claims, or students—objects that don’t exist out-of-the-box in CRM or ERP systems. The real power of AI is unlocked when these copilots can reason over not only standard data models but also these customized data structures.

This is where Copilot Extensions for Custom Entities come into play.

What Are Copilot Extensions for Custom Entities?

Copilot Extensions for Custom Entities allow developers and makers to extend the capabilities of Microsoft Copilot (within Dynamics 365 or Power Apps) to include custom business logic, domain-specific entities, and tailored user experiences.

These extensions make it possible for Copilot to:

Understand the schema of your custom tables/entities
Respond to queries and generate responses using your custom data
Trigger workflows or actions involving custom business logic
Interact through natural language with custom models, not just built-in ones

Think of it as bringing your custom business vocabulary into the language Copilot understands and enabling it to act meaningfully on your unique data model.

The Building Blocks of Copilot Extensions

Let’s explore the architectural components and tooling that power Copilot extensions for custom entities:

1. Custom Tables (Entities) in Dataverse

In Power Apps or Dynamics 365, Dataverse is the underlying data platform where business data is stored. When you create a custom table (e.g., “Inspection Reports” or “Property Listings”), you define its schema, relationships, and business rules.

2. Copilot Studio

Microsoft’s Copilot Studio (formerly Power Virtual Agents) provides the low-code interface for building and managing Copilot experiences. With Copilot Studio, you can:

Add natural language capabilities
Integrate plugins or Power Automate flows
Configure data access and user permissions

3. AI Builder + Plugins

To enable reasoning over complex custom data, you can integrate AI Builder models (e.g., classification, prediction) or custom plugins that encapsulate logic (e.g., fetch related records, summarize history).

4. Prompt Engineering & Topics

Copilot Studio supports structured prompts, conversation topics, and input/output formatting that help guide Copilot in understanding user intent and generating relevant responses.

Example Scenario: Copilot for Property Management

Let’s imagine a company that manages commercial real estate and uses a custom Dataverse entity called “Lease Applications.”

They want their employees to be able to interact with Copilot to:

Ask questions like: “Show me pending lease applications for San Francisco”
Generate lease summaries based on multiple data points
Kick off approval workflows
Provide recommendations for follow-ups

To achieve this, they would:

Model the “Lease Applications” entity with all required fields like applicant info, property ID, status, and lease terms.
Use Copilot Studio to create topics like “Find Lease Applications” or “Summarize Application.”
Integrate Power Automate flows to handle actions like approvals or notifications.
Add knowledge sources (PDF templates, SOPs) so Copilot can answer related questions.

Now, the property manager can say:
“Copilot, summarize the top 3 pending lease applications and recommend next steps based on last contact date.”

And Copilot will generate a response using the custom data schema, business logic, and conversational tone—all without a human writing a report.

How Copilot Understands Custom Entities

Copilot’s intelligence is powered by Microsoft’s orchestration of:

Natural Language Understanding (NLU): Converts user input into structured intent.
Semantic Schema Inference: Copilot learns about custom entities, field types, and relationships.
Orchestration Layer: Determines whether to call plugins, flows, or respond via LLMs (Large Language Models).
LLM-Powered Prompts: Based on context, Copilot uses dynamic prompts to generate coherent, domain-specific responses.

Copilot doesn’t “hard-code” behavior. Instead, it works like a smart dispatcher, reasoning over:

The data model
The user’s prompt
Available actions and workflows
Any guardrails or filters defined by admins

Key Capabilities Enabled by Copilot Extensions

1. Querying Custom Data with Natural Language

Users can use simple language like:

“List all overdue inspections for the Chicago region.”
“What’s the average application approval time for the last 30 days?”

Copilot interprets these as structured queries against Dataverse, even if the data lives in custom tables.

2. Generating Summaries

Copilot can summarize the content of long-form custom data fields, like:

Case histories
Notes on client interactions
Audit trail entries

3. Automating Custom Workflows

By linking custom Copilot responses to Power Automate, users can say:

“Send a reminder to the applicant whose lease is pending since last week.”
“Trigger a background check for new applications.”

4. Answering FAQs Using Custom Knowledge

Upload PDFs, websites, or Excel sheets to act as knowledge sources, then configure Copilot to answer queries using both structured data and unstructured text.

Example: “What are the eligibility requirements for a government-sponsored lease?”

5. Using Business Logic Rules

If your custom entity has business rules (e.g., field validation, calculation logic), Copilot respects them when executing actions or suggesting outcomes.

Security and Access Control

Copilot follows the same role-based security model as Power Platform:

Users only see data they’re allowed to see.
Admins can set data loss prevention (DLP) policies and content filters.
Sensitive fields (like financial data or PII) can be excluded from Copilot access via schema configuration.

This ensures that Copilot can be powerful without compromising privacy or compliance.

Design Best Practices

If you’re extending Copilot for your custom entities, consider the following best practices:

✅ Design a Clean Data Schema

Use meaningful names and relationships
Normalize your data where possible
Document business logic for reference

✅ Use Clear Naming for Prompts and Topics

Keep topics short and aligned with user intent (e.g., “Find applications”)
Provide trigger phrases that match real-world phrasing

✅ Modularize Your Logic

Use Power Automate or Dataverse plugins for complex operations
Keep conversational flow lean; offload processing to background flows

✅ Secure Your Data

Use row-level security and field-level security in Dataverse
Test Copilot prompts under different user roles

✅ Iterate with User Feedback

Use telemetry and analytics in Copilot Studio
Conduct internal testing to find gaps in topic coverage

Real-World Use Cases

Healthcare – Patient Care Coordination

Custom Entity: “Care Plans”
Copilot assists nurses in summarizing patient conditions, identifying overdue follow-ups, and triggering appointment bookings.

Insurance – Claims Management

Custom Entity: “Claims Cases”
Copilot summarizes claims, flags high-risk cases, and initiates fraud review workflows using predictive AI models.

Higher Education – Admissions Processing

Custom Entity: “Student Applications”
Copilot answers FAQs, summarizes applicant profiles, and notifies departments about interview scheduling.

Construction – Site Inspections

Custom Entity: “Inspection Logs”
Copilot helps inspectors log safety violations, find overdue reports, and summarize inspection history by site.

Limitations and Considerations

Despite its growing power, Copilot extensions for custom entities come with limitations to be aware of:

Latency: Actions involving plugins or data retrieval may cause brief response delays.
Data Quality: Copilot’s usefulness depends on well-maintained and structured data.
LLM Context Limits: There’s a token limit to how much data the model can “see” in a single prompt.
Localization: Multilingual support may require additional tuning for prompts and responses.

That said, Microsoft is actively enhancing these capabilities with roadmap items like:

Deeper plugin orchestration
Support for multi-entity reasoning
Enhanced analytics and diagnostics for Copilot usage

The Future of Copilot Customization

The next phase of Copilot extensibility includes:

Custom GPTs: Building organization-specific GPT models aligned to industry vocabulary
Embedded Copilot experiences in custom Power Apps and Model-Driven Apps
Low-code prompt design tools with prebuilt patterns for common intents
Advanced grounding techniques to ensure Copilot responses are always traceable to your data

Microsoft is also expanding Copilot governance tools so admins can monitor prompt usage, control knowledge access, and filter harmful responses in enterprise environments.