The Intent Layer for
Physical & Agentic AI

We translate raw perception into human intent — the missing layer between seeing and doing. So your agent doesn't just act. It understands why.

Get Started → How It Works

01 — The Problem

AI can act.
It doesn't know why.

Every layer of the AI stack is funded and shipping — except one. Robots know how to move but not why. Coding agents can write anything but need you to specify everything. The more autonomous the agent, the more it needs to understand intent. Nobody provides this as infrastructure.

Blind Manipulation

Robots can manipulate objects but don't know the user's goal. They move precisely — toward the wrong outcome.

Misinterpreted Intent

Agents execute tasks but often guess wrong about what is actually needed. Confidence without comprehension.

Guesswork at Scale

Every system today is guessing at user intent — and failing. The more autonomous the agent, the worse the guesses get.

The Intent Gap

Human Intent Accuracy 93%
With EmbodiedOS 77%
Best AI Model 40%
Goal Prediction Relevance 55%
Action Planning (no intent) 38%

Source: Meta AI Research, Fung et al., "Embodied AI Agents," 2025

02 — The Product

Three layers.
One company.

EmbodiedOS is a vertical stack: we collect our own data, build the intent model, and license it.

Data Fleet

We own the collection

Proprietary multimodal intent data from the real world. Paid participants with instrumented wearables and sensor-equipped environments.

Video, audio, gaze, hand tracking + ground-truth goal labels

Intent Model

The core IP

Translates raw multimodal input into structured human intent. Goal prediction, belief modeling, emotional state inference.

Outputs structured intent embeddings — not text — fast, cheap, composable

Intent API

The revenue engine

Licensed to any company building agents. Feed in sensor data, get structured intent output at real-time speed.

Personalization, confidence scores, ambiguity flags for safety-critical use

Your Sensors

You already have this

EmbodiedOS Intent API

We fill the missing layer

Your Agent / Robot

Plans toward predicted goals

03 — How It Works

Observation to intent
in milliseconds.

Not a monolithic LLM. A purpose-built architecture optimized for real-time intent prediction at low compute cost.

Model Architecture

JEPA-style joint-embedding

Each modality has its own encoder branch. A cross-modal attention layer fuses them into a single context embedding of dimension 1024.

  • Inputs: Video frames, audio, gaze vector, hand keypoints, IoT state
  • JEPA predicts in embedding space — 100x cheaper than generative
  • Cross-modal attention fuses all modalities into one vector

Transformer decoder + causal attention

Takes the "what is happening now" embedding and predicts "what the human wants" as an intent embedding.

  • Goal head — top-level and sub-goals
  • Belief head — what the user thinks is true
  • Emotion head — frustrated, confused, in flow
  • Trajectory head — how goals evolve over 30-300s

Structured embedding (not text)

Any action model can consume this — it's a standard embedding, not free text. Composes directly via vector similarity.

  • Goal vector (1024-dim) — predicted goal in learned space
  • Confidence score (0-1) — prediction certainty
  • Belief state — user perception vs reality
  • Time horizon — 5-second or 5-minute goal?

API Example

1 # POST /v1/intent
2 const response = await
3 embodied.predict(
4 video=camera_feed,
5 audio=mic_stream,
6 gaze=eye_tracker,
7 context_window="30s"
8 )
9
10 # Response
11 {
12 "goal": "make_coffee_for_two",
13 "confidence": 0.92,
14 "sub_goal": "boil_water",
15 "emotion": "focused",
16 "time_horizon": "5min"
17 }
<10ms ON-DEVICE 50-200ms CLOUD 10-100Hz EDGE LANGUAGE-AGNOSTIC

04 — See The Difference

Same robot.
Different intelligence.

A home robotics company. Their robot can pick, place, pour, stir. But without intent understanding, it needs the user to say exactly what to do.

Without EmbodiedOS

User pulls out pasta ingredients

Robot stands idle. Has no idea what's happening.

User says "help me cook"

Robot asks "What would you like to cook?" — doesn't see the pasta box already on the counter.

Water is boiling, user needs salt

Robot does nothing. Doesn't know what step the user is on. Waits for a command.

With EmbodiedOS

User pulls out ingredients

Goal: cook pasta (92%)

Fills pot, places on stove. No prompt needed.

Water boiling

Sub-goal: add salt

Moves salt within reach. "Shall I add salt?"

User opens drawers

Frustration + searching

"The colander is above the sink."

Phone rings

Intent paused. Timer needed.

Monitors pasta, alerts when done.

05 — Use Cases

Intent powers
every agent.

Same pattern, different verticals. The more autonomous the agent, the more it needs to understand what humans actually want.

Home Robotics

Your robot anticipates needs instead of waiting for commands. Reduces user frustration — the #1 churn driver in consumer robotics.

3x fewer voice commands needed Figure, Hello Robot, Samsung Bot, Moley Robotics

Coding Agents

Infers the full engineering goal from partial instructions. Cuts back-and-forth by 3x so developers stay in flow.

3x less back-and-forth Cognition (Devin), Cursor, Windsurf, Adept

Task Agents

Predicts user preferences and constraints from vague requests like "book me a good flight" — without 10 follow-up questions.

Fewer follow-up questions Rabbit, OpenAI Operator, multi-agent frameworks

Enterprise Agents

Reads a support ticket and infers the real underlying problem, not just the surface complaint. Measurable cost reduction.

Deeper issue understanding Salesforce Agentforce, ServiceNow, Intercom

The AI stack has a gap.
We fill it.

EmbodiedOS is the intent layer every agent needs. Let's talk about how it fits your stack.

Let's Talk → Read The Research