spatıa

Spatial agentic AI

AI has read the entire internet. It has never been inside your building.

Spatia builds a living model of physical space, and gives agents the ability to act inside it.

The exchange, live

Three Spatial AI agents. One conversation.

These agents understand the space and where you are inside it. That is what makes them different. The consumer, the venue and a brand each get one, they settle it between themselves over one shared live position, and hand back a single clean answer.

spatia LIVE POSITION Consumer AGENT Venue AGENT Brand AGENT
Consumer asksFind me a quiet coffee close by.
Venue answers, from live floor stateCorner Cafe. Low crowd, two minutes away.
Brand offersTwenty percent off, if it is on the way.
One answerGuided there, offer applied. No map opened.

Every exchange makes the next one sharper, and it only happens inside.

The layer

Watch a building become data.

Structure mappedfloor by floor
Why it compounds

The more Spatia runs, the smarter it gets. Every venue adds to what it learns, and that learning stays inside your walls. It compounds day after day, and no one outside can copy it.

Three Spatial Agentic AI Models

It runs on the venue's own systems.

Spatia is not a rip and replace. It is three spatial agentic AI models that sit on top of the IT and data a venue already has, so it fits the way the business already runs and stays inside its own compliance.

Sits on what is already there

Your infrastructure, made spatial

The models plug into the venue's existing systems, floor data and networks. Nothing new to stand up, nothing sensitive leaving the building. It reads the live state the venue already produces and turns it into answers and actions.

Compliant by construction

Inside the venue's own rules

Because it runs on the venue's own footprint, it stays inside the venue's data governance and privacy posture. The consumer, venue and brand models each work within that boundary, so it is safe to switch on without handing control to anyone outside.

One layer, three models, running on infrastructure the venue already owns.

The engine

Three models, one phone. No round trip.

Most assistants send every question to a data centre and pay for it by the token, so cost climbs with every question asked. We built our three models to run on the phone itself. Answers are computed on the device, which keeps the cost of running the product flat no matter how much it is used.

RUNS ON THE PHONE Voice ONNX RUNTIME Retrieval TENSORFLOW LITE Reasoning LITERT-LM 0 ROUND TRIPS

The whole loop stays inside the phone

Cost of operating

More questions should not mean a bigger bill.

cost questions →
Cloud assistants. Every question is a fresh round trip billed by the token, so the operating cost keeps climbing as usage grows.
Spatia on device. The models are already on the phone, so answering the millionth question costs about the same as the first.
The opportunity

Everyone is racing to build agents. None of them can act indoors.

Aerial night view of a city at dusk where every large indoor venue glows from within, with data threads rising from inside them.
$0BIndoor location by 2031
$0BLocation analytics by 2031
$0BAI training data by 2033

MarketsandMarkets · Grand View Research

Where it runs

Large, busy, and completely unmeasured.

Airport terminal
AirportsA wrong turn costs a flight. Dwell is the whole model.
Shopping mall atrium
MallsFootfall counted at the door and lost right after it.
Stadium bowl
StadiumsSixty thousand people needing the same four answers.
Museum gallery
MuseumsA thousand objects, three hours, no guidance.
Hospital lobby
HospitalsLate arrivals that begin as navigation failures.
Event hall
EventsCrowds that appear for a day, move fast, and never mapped.

Practically any large indoor space where GPS fails, Spatia makes it work. From warehouses to factories to public interest spaces.

Who's building it

Spatial computing, product, growth.

Eddie Avil
Eddie AvilCo-Founder, Technology

An early AR/VR pioneer, and host of 1CW: ONE’S Changing the World, a celebrated deep-tech podcast.

Rahul Mishra
Rahul MishraCo-Founder, Growth

Two decades building consumer businesses and new categories.

Dinesh Kumar PillaAI Engineer

Ships the app and the on device engine that makes it all run.

Advisors
Sourav Garg Sourav GargIIIT Hyderabad · Robotics Research Centre Assistant Professor at IIIT-Hyderabad and co-founder of Robodh AI. Researches place recognition, localization and navigation — how machines know where they are.
Karthik Sankaranarayanan Karthik SankaranarayananGuindy Angels · ex-Intel, Kinara (NXP) Ex-Intel; co-authored HotSpot, the thermal model that reshaped chip design. Led IIT Madras' RISC-V centre, drove edge AI at Kinara (now NXP), and co-leads Guindy Angels.
Arnav Neel Ghosh Arnav Neel GhoshBlippar · ex-Managing Director Ex-Managing Director at Blippar, one of the first augmented reality platforms to reach scale, where he led product and regional operations.

AI is learning to act.
We're giving it somewhere to stand.