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Building an AI-Powered Mixed Reality Assistant with Flutter

Building an AI-Powered Mixed Reality Assistant with Flutter

A mixed reality assistant combines spatial understanding, computer vision, voice interaction, retrieval, and generative AI.

Imagine looking at a machine and asking:

What is this component and how do I replace it?

The system can combine the camera view, detected objects, spatial position, user speech, and technical documentation.

Architecture 

                  Flutter
                     |
          +----------+----------+
          |                     |
       Spatial                Voice
       Layer                  Layer
          |                     |
          v                     v
     Camera / AR            Speech Input
          |                     |
          +----------+----------+
                     ↓
              Context Builder
                     ↓
                AI Backend
                /                       ↓          ↓
             RAG          LLM
               \          /
                \        /
                 ↓      ↓
                  Response
                     |
          +----------+----------+
          |                     |
      Voice Output         AR Overlay
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Spatial Context

The AR layer can provide:

  • detected object
  • object position
  • camera pose
  • nearby planes
  • depth
  • anchor information

Example:

{
  "object": "industrial_valve",
  "confidence": 0.93,
  "position": {
    "x": 0.42,
    "y": 1.18,
    "z": -1.30
  }
}
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Voice Input

A user might say:

What is this component?
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Speech-to-text converts this to text for the AI pipeline.

Build Rich AI Context

Instead of sending only the question:

What is this?
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include the detected context:

User question:
What is this?

Detected object:
industrial valve

Confidence:
0.93

Spatial context:
Object is approximately 1.3 meters from the user.
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Add RAG

For technical applications, retrieve relevant documentation:

Detected Object
      ↓
Knowledge Base Search
      ↓
Technical Manual
      ↓
Relevant Sections
      ↓
LLM
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This is useful for:

  • maintenance
  • field service
  • training
  • education
  • technical support

Generate Spatial Responses

Instead of displaying only a chat message, attach information to the detected object:

       [ Valve ]
           |
     ┌───────────────┐
     │ Pressure      │
     │ regulator     │
     │ Max: 10 bar   │
     └───────────────┘
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The label can be attached to an AR anchor.

Voice Output 

AI Response
    ↓
Text-to-Speech
    ↓
Headset / Speaker
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Hands-free interaction is particularly useful in field-service scenarios.

Hybrid AI

Not every task needs a cloud model.

Use local processing for:

  • object detection
  • simple commands
  • wake-word detection
  • basic classification

Use cloud services for:

  • complex reasoning
  • document retrieval
  • large language generation

This can balance latency, privacy, and capability.

Flutter Architecture 

Presentation
    ↓
Assistant BLoC
    ↓
Assistant Use Case
    ↓
Context Repository
    ↓
+-----------------------+
|                       |
AR Service           AI Service
|                       |
Native AR              Backend
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State Model 

sealed class AssistantState {}

class AssistantIdle extends AssistantState {}

class AssistantListening extends AssistantState {}

class AssistantThinking extends AssistantState {}

class AssistantResponding extends AssistantState {
  final String text;

  AssistantResponding(this.text);
}

class AssistantError extends AssistantState {
  final String message;

  AssistantError(this.message);
}
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Reliability and Safety

AI output should not automatically be treated as fact.

For technical applications:

  • provide source documents
  • expose confidence where appropriate
  • distinguish detected facts from generated suggestions
  • let users verify critical information
  • avoid using model output as an unvalidated control signal

Performance

Reduce latency using:

  • on-device object detection
  • smaller models for classification
  • streaming responses
  • cached documents
  • local retrieval
  • asynchronous processing

Conclusion

An AI mixed reality assistant is a combination of spatial computing, computer vision, voice, retrieval, and generative AI.

Flutter can provide the application and orchestration layer, while native AR/XR components handle performance-sensitive spatial processing and rendering.

Useful Links

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

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