How to Leverage Spatial Computing to Boost AI Assistants Today

What is spatial computing and why it revolutionizes AI assistants

Spatial computing combines 3D information, real-world sensors, and artificial intelligence to provide assistants with richer, situational context. Instead of text-only responses, assistants can now understand "where" and "how" something is located in space, making interactions more natural and useful.

Unlocking this capability means we can build assistants that truly act in the surrounding environment, whether it's guiding a user through a physical space, overlaying helpful information on machinery, or processing documents that have a spatial component.

Definition and key components

• Mapping and localization:SLAM, GPS, inertial sensors for determining position.
• Visual computing:computer vision, depth sensing and point cloud processing.
• Edge AI:models optimized to run on memory-constrained devices.
• API integrations:LLMs that accept prompts with coordinates, spatial tags, or 3D assets.

How to integrate spatial computing into your AI assistants

1. Add spatial context to prompts

The simplest part is to enrich the prompt with location information. Here’s a quick template you can copy into any chat interface that supports structured messages.

{
  "prompt": "Explain how to repair the control panel at location {coordinate_xyz}. Consider that the user is facing the device and the panel is mounted at a height of 1.2 m.",
  "context": {
    "position": {"x": 3.5, "y": 1.2, "z": 0},
    "orientation": "front-facing",
    "environment": "workshop"
  }
}

This pattern works with most commercial and open-source LLMs that accept JSON input. The key is to keep position fields in a consistent coordinate system (e.g., meters from a reference point).

2. Choose open models that support spatial reasoning

In the 2026 landscape, there are three notable models:

• Ricursive AI Ripple-65B:a 65-billion-parameter LLM under Apache 2.0, scoring 80.5 on Terminal-Bench 2.1. Its size gives it strong comprehension of complex, multilingual prompts, including those with spatial tags.
• webAI TwIL-8B:an 8.8-billion-parameter reasoning model optimized for math and coding, scoring 98.2 on MATH-500. It works well when you give it spatially explicit steps (e.g., "calculate the area of a rectangle with sides {width} and {height}").
• Anthropic FlashProve:a zkSNARK prototype that signs LLM responses, ensuring a response actually comes from a known set of weights without revealing the weights themselves. Ideal for scenarios where provenance is required (e.g., legal or medical assistance) while preserving privacy.

Integrating these models with a spatial layer means you can run inference locally (Edge AI) and still get provenance checks with FlashProve when needed.

Real-world use cases and practical workflows

Navigation assistance workflow

• Input:
• Processing:Converts pixels to 3D coordinates using intrinsic calibration.
• Prompt:Sends coordinates + user request to the assistant.
• Output:Overlays AR instructions or 2D text with contextual warnings.

Spatial documentation workflow

A field operator scans a piece of machinery with a portable lidar. The point cloud is processed into a 3D model, which is indexed in a vector DB. The assistant can then answer questions like "Which screws are housed in the left support?" by retrieving relevant 3D data and providing a summary with images.

Current trends: the latest models and verification technologies

The LLM landscape is evolving rapidly. Open-weight models like Ripple-65B democratize cutting-edge research, while verification systems like FlashProve add an unprecedented level of provenance assurance.

Combining a large LLM with a cryptographic proof layer means you can build assistants that are both powerful and transparent, meeting growing regulatory expectations for AI.

Example integration: logistics assistant

Let’s say we want to build an assistant that helps warehouse workers locate items. The system:

1. Reads RFID tags and the camera position on the cart.
2. Builds a prompt like the one shown above.
3. Calls the Ripple-65B (or TwIL-8B) API via a simple HTTP wrapper.
4. Uses FlashProve to verify that the response comes from the model and not an external source.

import requests
import json

API_URL = "https://api.rich.ai/v1/chat"
HEADERS = {"Authorization": "Bearer YOUR_KEY", "Content-Type": "application/json"}

def spatial_assistant(query, position):
    payload = {
        "model": "ripple-65b",
        "messages": [{
            "role": "user",
            "content": f"{query} at position {position}"
        }],
        "proof": "flashprove"
    }
    resp = requests.post(API_URL, headers=HEADERS, json=payload)
    return resp.json()["choices"][0]["message"]["content"]

print(spatial_assistant("Show the shortest route to reach shelf A", {"x": 5.2, "y": 0, "z": 2.1}))

Actionable takeaways

• Define a coordinate system:Choose a single coordinate system (e.g., meters from the main entrance) and use it for all your input data.
• Enrich prompts:Always add location data to user messages. Use JSON or simple markup to keep the prompt readable.
• Experiment with open models:Download Ripple-65B or TwIL-8B and test spatial workflows locally before moving to large-scale