How to Build Spatial AI Assistants with the Latest Tools

Introduction: Why the combination of spatial computing and AI assistants is a game-changer

In 2026, artificial intelligence is merging with physical space through spatial computing. From 3D interfaces to augmented and virtual reality experiences, AI assistants can now understand and respond to spatial contexts in real time. This article explains how to combine the latest open-source libraries, such as NVIDIA’s Switchyard and Qwen’s zg, with modern prompt engineering techniques to create spatial AI assistants ready for deployment.

1. The essential building blocks of a spatial AI assistant

1.1 Spatial interfaces and multimodal inputs

• Depth cameras and LiDAR sensors for 3D mapping.
• Microphones and voice analyzers for voice control.
• Tangible objects and hand gesture tracking via high-speed webcams.

This data is then passed to an LLM through a spatial prompt that includes coordinates, objects, and suggested actions.

1.2 Local search engines for real-time responses

Large-scale cloud-based search introduces latency, which is unacceptable for spatial applications. Teams are now adopting Qwen Developers’ zg (zvec-grep), a local-first search layer that combines ripgrep, BM25, and vector search. This enables fast querying of vector stores, code files, and documentation without leaving the assistant’s container.

2. Using Switchyard for LLM-neutral routing

Code example: Initializing Switchyard

use switchyard::{Client, ModelProvider};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut client = Client::new()
        .add_provider(ModelProvider::OpenAI("gpt-4o".to_string()))
        .add_provider(ModelProvider::Anthropic("claude-3-opus".to_string()))
        .add_provider(ModelProvider::Local("llama-3-70b".to_string()))
        .build()
        .await?;

    // Example spatial prompt
    let prompt = r#"
    Given the camera view (x=10, y=2, z=5) and a red cube at (3,0,0),
    suggest the next movement to reach the object.
    "#"";

    let response = client.complete(prompt).await?;
    println!("Assistant: {}", response);
    Ok(())
}

This example demonstrates how to route a spatial request between multiple LLMs without modifying the assistant’s code.

3. Designing effective prompts for spatial contexts

A well-crafted prompt is the core of any spatial AI assistant. Best practices in 2026 include:

• Explicitly include coordinates and geometry.Example: «User location: (2,1,0). Environment grid: ...»
• Specify the desired output channel.«Return a natural language description and a VR controller command.»
• Set the context.«You are an AI assistant for a mobile robot in a factory. Priority: safety, efficiency.»

A common prompt template that teams are adopting is:

[ROLE] + [SPATIAL DATA] + [TASK] + [RULES] + [OUTPUT]

For example:

[ROLE] You are an AI assistant for an autonomous driving system.
[SPATIAL DATA] Front camera: 30 m, LiDAR: 5 obstacles, vehicle coordinates: (0,0).
[TASK] Suggest a safe turning maneuver.
[RULES] Priority: avoid collisions; maximize smoothness.
[OUTPUT] Short natural language command + trajectory.

4. Explainability: Why it matters in 2026

Partnerships between Motional and MIT have introduced real-time explanation systems for autonomous vehicles. Spatial AI assistant developers are now following this approach by adding an optional explanation to the LLM that shows step-by-step reasoning.

Integration involves:

• Generating an explanation in parallel with the main response.
• Storing the reasoning in a log for future audits.
• Displaying a visual map of decisions to the user (e.g., why a point was marked as dangerous).

This increases trust, especially in industrial and healthcare settings.

5. Deploying a spatial AI assistant prototype

Let’s combine these elements into a lightweight prototype that can run locally on an edge device, such as a HoloLens 3. The steps are:

1. Acquire spatial datausing a depth sensor and an eye-tracking module.
2. Extract embeddingswith a 3D vision model (e.g., DINOv2).
3. Query locally with zgto retrieve relevant documentation.
4. Compose promptsusing the spatial prompt template.
5. Forward via Switchyardfor language generation.

Each step can be containerized with Docker and deployed via Kubernetes on edge devices.

Key takeaways

• Spatial AI assistants combine 3D data, multimodal sensors, and LLMs through well-structured prompts.
• NVIDIA’s Switchyard simplifies routing between multiple LLM providers.
• Qwen’s zg provides a local-first, low-latency search engine essential for real-time interactions.
• Explainability (as demonstrated by Motional-MIT systems) is now a default feature for critical AI assistants.
• A prototype can be developed in a few days using containers, open-source libraries, and modern prompt engineering techniques.

Conclusion: Build your spatial AI assistant today

Next steps

• Clone the Switchyard repository and set up a local proxy.
• Test zg with a small vector store (e.g., project documentation).
• Experiment with a spatial prompt template in an augmented reality environment.
• Add an explanation component to meet regulatory requirements.

The future of AI assistants is three-dimensional. Your next spatial step is just around the corner.

References and further reading