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WORLD FIRST Real-time 3D world model · Sept 2026

The Spatial Brainfor the Physical World

Transforming complex physical infrastructure into a living, self-predicting ecosystem for autonomous agents, fleets, and humans.

Explore live 3D capture
SIMULATED FEED · HKIA T2drag to orbit
Live today

Not a concept. Already running.

0+

people a day move through spaces our model already sees. Roughly the population of a mid-sized country.

LiveSince you opened this page0people have walked through them
0billion+

journeys a year through mapped venues

Airport + rail, 2025
0

venues mapped and live

Terminals, stations, campus, resorts
0km²+

of airport terminal alone, about 24 football pitches

One international hub
0

closures needed to capture any of it

Ordinary phones, live operations

Captured with ordinary phones during normal operations. No closures, no laser rigs.

Vision

A queryable reality for all.

Deep World Model is the first real-time 3D world model for complex physical infrastructure: a single, living spatial layer that robots, emergency teams and people can all query, trust and act on at the same time.

Robots & fleetsOne shared spatial backbone, zero map collisions
Emergency teamsA live 3D picture shared across agencies
PeopleNatural-language answers and step-free routes
HKIA · T2 · PHYSICS + SEMANTICS
The problem

Buildings don't know
what's inside them.

That silence has a cost, and robots, responders and travellers are the ones who pay it.

SECTION VIEW · CONCOURSE L2 SECTION VIEW · CONCOURSE L2 MAP · 2023: FLAT FLOOR REALITY:STAIRWELL !
FAILURE 01

Robots fall down stairs

Autonomous machines relying on outdated maps walk into stairwells, drop-offs and hoardings that didn't exist when the map was made.

FLOOR PLAN · REV. 2019SCREEN SHARE · 4s LAG FLOOR PLAN · REV. 2019 SCREEN SHARE · 4s LAG UNMAPPED PARTITION INCIDENT ?
FAILURE 02

Responders work blind

No emergency operations center on Earth runs on a live 3D model. Complex incidents are still coordinated from static floor plans and screen shares.

STEP-FREE ROUTE · 08:00APP: STEP-FREE ✓ STEP-FREE ROUTE · 08:00 APP: STEP-FREE ✓ ENTRANCE GATE 23 LIFT OUT OF SERVICE !
FAILURE 03

Journeys quietly break

A step-free route calculated this morning can be wrong by this afternoon, the moment a lift goes down or a corridor gets rerouted.

"

The single largest cause of indoor autonomous failure is a map that no longer matches the building, and until now nobody had solved it.

Timing

Why spatial AI, why now.

01 / EVOLUTION

Agents left browsers

AI evolved from conversational reasoning to real-world action. Real actions always happen somewhere physical.

02 / ECONOMICS

Hardware deflation

Robot and sensor costs fell exponentially, while indoor spatial ground truth stayed slow and prohibitively expensive.

03 / LIMITATION

Where GPS terminates

Most human and economic activity happens indoors. Google Maps, Apple Maps and GPS all stop at the doorway.

04 / FRAGMENTATION

Siloed private maps

Every robot vendor builds a brittle, isolated map. Operators are left carrying competing, rapidly ageing spatial silos.

Live capture

Real 3D capture.
Not CGI.

Fly through each site in full HD, then open its interactive model.

The engine

The world's first
living spatial AI engine.

The real-time 3D world model for complex physical infrastructure.

✦ BreakthroughWorld's first

Signal-first multimodal

Fuses ambient Wi-Fi, BLE and geomagnetic signals with vision, dramatically reducing hallucination.

✦ Minutes, not weeksWorld's fastest

Micro-scene self-healing

The generative engine updates altered spaces in minutes, with zero full-building rescans.

✦ Just a phoneMinimal hardware

Crowd-powered ingestion

Captured with mobile devices and crowd input. No downtime, no laser rigs.

The stack

Four layers. Two moats.

1

3D Geometry

Base commodity
2

Semantics

Machine-readable world
Moat
3

Currency

In sync with reality
Moat
4

Universal Agentic API

One brain for all
3D geometry is becoming a commodity. Knowing what every voxel is (Semantics) and keeping it in sync with reality (Currency) is the real enterprise moat.
Applications

One engine.
Six ways to put it to work.

The same living world model powers every use case. No separate systems, no separate maps.

Localized currency

The world's first & fastest
self-healing engine.

When terminal spaces change, DWM updates only the modified micro-zones, in minutes. No full-building rescans.

Before: covered hoarding in the HKIA departures hall After: the new pop-up shop, regenerated in the 3D model BEFORE · HOARDINGAFTER · REGENERATED
MICRO-ZONE RECAPTURE · SHOPFRONT
REGENERATED1 shopfrontof 170,000 m² untouched
TIME TO SYNCMinutesfrom a mobile device
World's first

Localized voxel patching

Partial-zone updates touch only altered micro-areas (retail fit-outs, hoardings, pop-ups) using ordinary mobile or CCTV feeds.

World's fastest

Hours, not weeks

A generative neural pipeline processes local geometry changes in minutes, with zero operational footprint or floor closures.

Zero-latency sync

Airport-wide broadcast

Pushes updated 3D ground truth to connected C2 consoles and multi-vendor autonomous fleets at the same time.

The strategic moat

Ground truth that stays 100% true, 100% of the time.

Traditional digital twins cost $100,000 and take 3 weeks to rescan a terminal section. DWM self-heals in minutes from a mobile device.

Cost per terminal-section update
Traditional digital twin $100,000
Deep World Model A mobile device
Time to updated ground truth
Traditional rescan 3 weeks
DWM micro-zone heal Minutes
Positioning

The vertical depth moat.

A shared foundational backbone carries universal physics and geometry. Custom semantic heads provide domain-specific mastery.

We are not building one world model. We are building a world model that already knows what kind of building it is looking at.

Architecture

Proprietary spatial AI stack.

End-to-end, from raw mobile-device data to a queryable, machine-readable 3D world.

Spatial resolution
Up to 3 cm

Geometry detail in priority zones (5 cm semantics). Set per zone: low-priority areas can be coarser, up to about 1 m.

Throughput
<10 hrs

For 170,000 m²: 3h passive capture plus 6h neural reconstruction.

Semantic taxonomy
~1,200

Base classes plus domain extensions.

Regeneration
<1 hr

Micro-zone regeneration. Only modified zones are recomputed and broadcast.

Spatial-semantic reasoning Cross-modal generalization Understands exits, fare gates, clearance limits Crowdsourced via Beetle app
Team experience

We've shipped spatial
at city scale.

0M+

LeaveHomeSafe downloads, Hong Kong's citywide contact-tracing system

0

Integrated resorts on the Cotai Strip with indoor navigation, from The Venetian® to The St. Regis Macao

0

MTR stations with inclusive in-station navigation

Management leadership

Enterprise-ready.
Proven go-to-market.

A team of 20+ specialists across signal processing, graph neural networks, machine learning, location intelligence and generative AI. Full-stack builders in spatial computing, semantic AI and enterprise infrastructure, with massive-scale deployments behind us.

Strategic partners & backers
Join the spatial revolution

Every space will need
a spatial brain.
We're building it.

Connecting physical reality to the next generation of artificial intelligence, automated fleets and autonomous agents.

info@collectiv.co Email us
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