Introduction: Merging Spatial Computing with AI Assistants
If you’ve ever wonderedhow to create an AI assistant that knows where you are, what you’re looking at, and how you want to interact, you’re in the right place.Spatial computing
What Spatial Computing Looks Like Today
Spatial computing goes beyond traditional 2D screens, leveraging holographic displays, AR/VR headsets, and the new spatial OS from Meta, Apple, and NVIDIA. Platforms likeMeta Quest Pro,Apple Vision Pro, and WebXR-based headsets now offer:
- Precise localization viaspatial anchors(ARCore, ARKit, OpenXR)
- Real-time hand, eye, and voice command detection
- High-fidelity 3D rendering with ray tracing and AI-based occlusion
Recent vision-language models likeTencent WeVisOne-VL (2B/4B)demonstrate how OCR, object detection, and segmentation can be unified into a single LLM, opening new possibilities for AI assistants that understand complex scenes.
Why Integrate an AI Assistant?
A traditional AI assistant is limited to isolated textual or vocal inputs. By adding spatial awareness, you can:
- Provide location-based contextual information (e.g., "show me the nearest restrooms")
- Manipulate 3D objects with natural commands (point and ask)
- Overlay real-time instructions on real-world tasks (maintenance, assembly)
- Leverage multimodal input: gaze, gestures, voice, and vision.
Technology Stack for a Spatial AI Assistant
The current foundations are built on three pillars:
1. Language and Vision Model
Models likeGPT-4o,Ling-3.0-flash(with NVIDIA’s MoE optimizations) and new Apache 2.0 vision-language models likeWeVisOne-VLprovide both language understanding and visual analysis. Use theOpenAI Completions APIorLangChainto concatenate spatial context with prompts.
2. Spatial Connection
SDKs likeUnity XR,ARCore,ARKit, andWebXRexposepositional tracking,hand tracking, andeye gaze. Savespatial anchordata in the cloud to make it persistent across sessions.
3. Rendering Engine and Output
Unity, Unreal Engine, or WebXR browsers can overlay 2D UI, 3D holograms, or light-based instructions. NVIDIA DGX Spark GPUs with MoE optimizations accelerate inference, keeping latency low for real-time responses.
Practical Example: A Spatial AI Assistant in Unity
Below is a complete, minimal example that demonstrates how to capture the user’s gaze and position, call an LLM, and display a response as a 3D UI element overlay.
// Install the XR Plug-in Management package via Package Manager
// Enable "OpenXR" or "XR Interaction Toolkit"using UnityEngine;
using UnityEngine.XR;
using UnityEngine.UI;
using System.Collections;
public class SpatialInputHandler : MonoBehaviour
{
public Transform xrOrigin; // User's main position
public Camera mainCamera; // Main camera
public float interactionDistance = 2.0f;
public TextMeshProUGUI debugText; // Optional: for debugging
void Update()
{
// Get the direction of the gaze
Ray ray = mainCamera.ScreenPointToRay(Input.mousePosition);
if (Physics.Raycast(ray, out RaycastHit hit, interactionDistance))
{
debugText.text = $"Looking at: {hit.transform.name}";
}
}
}In the UI callback method, send a prompt that includes spatial coordinates.
// In another script, e.g. AiAssistant.cs
using LangChain.Providers;
using LangChain.Chain;
public class AiAssistant : MonoBehaviour
{
public OpenAiProvider openai;
public SpatialInputHandler inputHandler;
public async void AskAi(string question)
{
// Get the user's position
Vector3 pos = inputHandler.xrOrigin.position;
string context = $"The user is located in 3D at pos = ({pos.x:F2}, {pos.y:F2}, {pos.z:F2}). " +
$"They are looking towards {inputHandler.mainCamera.transform.forward}. " +
$"
Question: {question}";
var chain = ChatSequentialChain.Create(new ChatSequentialChainInput
{
Messages = new[] { new HumanMessage(context) }
});
var response = await chain.RunAsync();
DisplayResponse(response);
}
}Position aCanvasat the user's eye height and display the text with a semi-transparent background.
Prompt Engineering for Spatial Contexts
The quality of the response depends on how you format the prompt. Guidelines for 2026:
- Include coordinates and object references.Example:
"The user is at (3, 0, 5) and is pointing towards 'shelf_door'." - Add the visual channel.If you have access to a vision-language model like WeVisOne-VL, send the image as base64 and ask it to extract relevant details.
- Use language consistent with the 3D interface.Replace 2D references like "click" with "look and speak" or "point".
- Separate context and question.Separation helps the LLM understand the scene without confusing tokens.
Example prompt
System: You are a spatial AI assistant that helps the user in 3D environments.
User: [Position] x:2.1 y:1.5 z:4.3, looking towards optics_shelf.
User: Show me the product specs for the object the user is looking at.
Assistant:Real-World Use Cases in 2026
- Virtual Commerce Navigation:An AR headset displays reviews and prices when the user looks at a product.
- Assisted Maintenance:A technician looks at an engine, and the AI assistant indicates components, steps, and risks.
- Collaborative Design:Multiple users in a shared 3D environment receive real-time design suggestions.
- Training and Simulation:New hires receive contextual instructions while inspecting virtual machinery.
Conclusion: Start Building Your Spatial AI Assistant Now
Spatial computing and AI assistants are converging faster than ever. By leveraging open-source vision-language models, cutting-edge AR/VR SDKs, and LLM services, you can create assistants that understand location, gaze, and intent. The code above is a starting point; expand it with your own 3D datasets, custom business logic, and multimodal pipelines.
Key Takeaways to Remember
- Usespatial anchorsto maintain persistence across sessions.
- Always include coordinates and object references in LLM prompts.
- Leverage vision-language models (e.g., WeVisOne-VL) for deeper scene understanding.
- Optimize inference pipelines with NVIDIA’s MoE optimizations for low latency.
- Test the experience with eye and gesture tracking, not just voice.
Next Steps
- Set up a Unity XR project with OpenXR.
- Connect an OpenAI/LangChain instance.
- Implement a simple 3D UI that displays responses.
- Iterate on prompt engineering based on user feedback.
With these foundations, you’re ready to create the spatial AI assistant that the next frontier of computing awaits.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (November 2026): Here are some recent trends and news to inspire you: - webAI Releases TwIL-8B, an 8.8B Reasoning Model Scoring 98.2 on MATH-500: webAI released TwIL-8B, an 8.8B reasoning model post-trained from IBM Granite 4.2 8B. It posts 98.2 on MATH-500 and 81.6 on LiveCodeBench, but trails ... [2026-11-04] - Ricursive AI Releases Ripple-65B Preview: A 65B Dense Open-Weight Model Scoring 80.5 on Terminal-Bench 2.1: Ricursive Intelligence released Ripple-65B Preview, a 65B dense open-weight LLM under Apache 2.0. It scores 80.5 on Terminal-Bench 2.1, ahead of US op... [2026-11-04] - NVIDIA Shows 3 Ways to Speed Up MoE Inference on DGX Spark, Lifting Ling-3.0-flash Throughput 42.5%: NVIDIA shows how 3 stacked optimizations speed up MoE inference on a single DGX Spark. On Ant Group inclusionAI's 124B Ling-3.0-flash, GSM8K decode th... [2026-11-03] Use this current information as inspiration to create an original and relevant prompt for 2026.