How to Create Spatial AI Assistants with LLMs and Spatial Computing?

Introduction: why spatial AI assistants are the future

AI assistants are evolving from simple text chatbots to immersive companions that interact with the surrounding 3D world.Spatial computing

1. Foundations: combining LLMs and spatial computing

A spatial AI assistant must process two data streams:

• Spatial inputs:coordinates, meshes, point-clouds, camera images, sensor data (IMU, LiDAR).
• Language inputs:user queries, commands, conversational context.

The LLM generates intentions and action plans; spatial modules (SLAM, odometry, voxel occupancy forecasts) translate those intentions into concrete actions.

1.1 Technology stack in 2026

• Edge LLMs:Google Gemini 3.6 Flash and 3.5 Flash-Lite dramatically cut token costs for agents, ideal for low-power devices.
• Serving frameworks:NVIDIA srt-slurm enables declarative, reproducible configurations with load-balancing optimized for spatial models.
• Spatial pipelines:Unity XR, WebXR, and ROS 2 provide sensor fusion and real-time rendering.

2. Designing prompts for a spatial assistant

Prompts must act as a bridge between language and the 3D world. An effective pattern is thespatial chain-of-thought prompt:

You are a spatial AI assistant operating within a 3D environment. Your current position is {pos}, orientation is {rot}. The user asks: "{query}".

1. Interpret intent:2. Check obstacles:3. Generate an action plan:4. Execute the action:Return only the plan and action if you are 100% confident of success. Otherwise, ask the user for confirmation.

This schema can be embedded in a broader LLM prompt that includes the 3D model specifications (e.g., mesh, collision layers). Structured tags help the model respect spatial constraints.

2.1 Example prompt with Gemini 3.6 Flash

In a retail-store scenario, the assistant might receive this prompt:

Role: spatial retail assistant.
Context: shelf at {shelf_id}, products in {category}. User: "Show me the blue variant of this product."

Obligations:
- Identify the product matching the category and color.
- Verify availability within a 2-meter radius.
- Return exact coordinates for retrieval.

Expected output:
{"action": "reach", "target": {"x": ..., "y": ..., "z": ...}, "confidence": 0.98}

3. Configuring LLM serving with NVIDIA srt-slurm

To run LLMs on latency-constrained edge devices, srt-slurm provides a declarative YAML file. Here is a minimal snippet for a spatial agent on an NVIDIA Jetson device:

apiVersion: srt/v1
kind: Service
metadata:
  name: spatial-agent
spec:
  image: nvcr.io/nvidia/golem:llm-7b
  resources:
    gpu: 1
    memory: "8Gi"
  env:
    - name: MAX_TOKENS_PER_MINUTE
      value: "20000"
    - name: MODEL_CACHE
      value: "/model/cache"
  scaling:
    minReplicas: 1
    maxReplicas: 3
    targetLatency: "50ms"
  healthCheck:
    endpoint: /v1/health
    interval: 30s

This file can be applied withsrtctl apply -f spatial-agent.yaml

4. Use cases and practical applications

• Guiding the warehouse:An LLM analyzes pick requests, plans a route, and drives a mobile robot to the product.
• Indoor navigation assistance:In an airport, the assistant translates voice commands into 3D directions, overlaying navigation arrows on the camera feed.
• Factory safety inspection:Detects real-time anomalies (e.g., an un-shielded worker) and alerts the operator with a spatial HUD.

5. Best practices and ethical considerations

• Reliability:Always perform an obstacle check before any motor action.
• Transparency:Log prompt histories and agent actions for future audits.
• Privacy:De-identify raw spatial data into aggregated heatmaps before storage.

6. Concrete steps to get started

1. Define aminimal spatial setup(e.g., Unity XR) that publishes camera transforms.
2. Design aprompt schemathat includes position, goal, and obstacle verification.
3. Deploy anLLM serviceusing the NVIDIA srt-slurm YAML for your edge hardware.
4. Prototype asimple agentthat answers user queries and moves toward the indicated location.
5. Iterate by collecting error logs and tuning prompt parameters.

Conclusion: your spatial assistant, now

The convergence of LLMs, spatial computing, and modern serving pipelines makes spatial AI assistants accessible to any developer. By leveraging Gemini 3.6 Flash for cost-effective reasoning, NVIDIA srt-slurm for efficient model management, and structured prompt engineering, you can build assistants that understand the 3D world and act usefully. The future is already here: start building the assistant that guides your users through space today.