Introduction: Why spatial computing is changing how we interact with AI assistants
In 2026,spatial computingis no longer a future concept: it’s a daily reality that redefines how AI assistants perceive, reason, and act in physical space. From location-based interfaces to multimodal language models that understand depth and context, you can now build assistants thatunderstand where you are,what object you have in front of you, andwhat action you want to takein a natural way.
This article walks you throughhow to leverage the latest open-source tools(likeHermitfrom Infini-AI-Lab),voice cloningmodels fromEdge0, and ultra-fast decision models likejoey-1from Baseten to build a complete, market-ready spatial AI assistant.
How to build a spatial AI assistant: an overview
Creating a spatial assistant no longer requires a complex proprietary stack. The three fundamental phases are:
1. Define the spatial context
Start with areliable position sensor(GPS, Wi-Fi trilateration, inertial sensors, or a network of cameras). Raw coordinate data is then normalized into a logical map of the room or environment. This step feeds the next phase of prompt engineering.
2. Use a modern prompt engineering framework
In 2026,Hermitfrom Infini-AI-Lab has become thestandard frameworkfor training external agents that combine instructions, memory, and tools. It lets you define astructured promptthat includes explicit spatial references, making it easier for the model to act consistently.
Example prompt for a spatial agent (Hermit format):
{
"system": "You are a spatial AI assistant operating in an indoor environment. Use the provided location and detected objects to decide which action to execute.",
"skills": ["locate_object", "speak", "move_to"],
"memory": {
"current_zone": "${zone}",
"target_object": "${object}"
},
"tools": {
"locate_object": "function(args: {name}) returns bounding_box",
"move_to": "function(args: {target}) returns path",
"speak": "function(args: {text}) returns audio_id"
},
"prompt": "The user wants to find a {{object}} in the {{zone}}. First check if it’s present on the map, then move towards it and speak a confirmation message."
}This prompt directly incorporates spatial markers ({{object}}, {{zone}}) that the agent can interpolate with real-time sensor data.
3. Add vocal awareness with Audio8-TTS-Realistic
Voice is a key element for spatial interaction. Edge0’sAudio8-TTS-Realisticmodel (0.6B, zero-shot, Apache 2.0 license) provides realistic voice cloning in 23 languages with near-zero latency. You can integrate it to provide contextual vocal responses that match the spatial context.
Example API call to generate a vocal response:
import edge0
model = edge0.Audio8TTSRealistic()
text = f"I found {object} near {zone}."
audio_id = model.synthesize(text, voice_seed="user_01")
print(f"Audio ID: {audio_id}")4. Integrate a decision model for real-time actions
When an assistant must choose between multiple actions (e.g., which object to reach first), a fast decision model is essential.joey-1from Baseten offers calibrated probabilities in just 20 ms P50 at $0.01 per 1M tokens, making it ideal for edge inference.
Simplified code flow:
import baseten
client = baseten.Client()
probabilities = client.predict(
model_name="joey-1",
input_data={"options": ["light", "door", "shelf"]
})
selected_action = max(probabilities, key=probabilities.get)
print(f"Selected action: {selected_action}")Practical example: building a navigation assistant for a store
Imagine aphysical e-commercescenario where a customer needs to find a product on a shelf. Our spatial assistant will perform:
- Position detection:The inertial sensor detects that the user is in aisle A.
- Map lookup:The system maps the aisle and identifies the product’s approximate location.
- Agent prompt:The Hermit prompt is populated with zone and object markers.
- Decision:joey-1 evaluates whether to head directly or wait for a user confirmation signal.
- Vocal response:Audio8-TTS-Realistic provides a personalized vocal guide.
The end result is aresponsive assistantthat guides the user without the need for a screen, using only contextual voice commands.
Code flow (pseudo-code)
# 1. Position acquisition
position = inertial_sensor.get_position()
zone = map_service.resolve_zone(position)
# 2. Object lookup
object = user_request.target_object
location = discovery_service.locate(object, zone)
# 3. Agent prompt (Hermit)
prompt = agent_template(zone, object)
response = hermet_agent.execute(prompt)
# 4. Action selection (joey-1)
action = decision_model.choose(response.path_options)
# 5. Voice synthesis (Audio8-TTS)
voice_text = f"Take the {action.path} way"
audio = tts_model.synthesize(voice_text, voice_seed=user_voice)
# 6. Output
audio_player.play(audio)Common pitfalls and how to avoid them
- Ignoring edge latency:Always use a decision model like joey-1 for time-critical operations; large language models can be too slow.
- Overloading prompts with spatial markers:Keep the prompt readable; use placeholders like {{object}} and {{zone}} only when necessary.
- Neglecting vocal privacy:Even with open-source models, encrypt audio data in transit and retain only necessary voice seeds.
- Relying on a single position source:Combine multiple sensors (IMU + Wi-Fi + camera) for robust localization.
Conclusion: the future of spatial computing and AI assistants in 2026
2026 marks a turning point:spatial computingand modernAI assistantsconverge into seamless, contextual, and vocal experiences that work in real time at the edge. Leveraging open-source frameworks like Hermit, realistic voice cloning like Audio8-TTS-Realistic, and ultra-fast decision models like joey-1, you can create assistants that understand not just what you say, but alsowhere you are and what you’re trying to do.
Start with a solid spatial sensing foundation, build clear and modular prompts, and integrate fast, voice-first responses and speed-optimized decisions. The result is a spatial AI assistant that feelspresent,helpful, and trulyhuman
Key actions:
- Implement a unified position detection system today.
- Try Hermit with a basic spatial agent prompt.
- Record a voice sample for the user and test zero-shot synthesis with Audio8-TTS.
- Integrate joey-1 for all time-critical decisions.
With these steps, your spatial AI assistant will be at the forefront of 2026 innovation and beyond.