However large the space,
one continuous journey
Connect journeys broken across cameras and floors. Start crowding, layout, and safety decisions from one space.
Route comparison
Broken per-camera trails become one continuous route
Even across multiple floors, one visitor’s dwell time stays continuous.
Each camera on its own
One path connected by SAAI
Conceptual diagram. A dedicated world and plausibility suite for this space are not connected to the runtime yet.
Operating challenges of large spaces
Even a vast space, one flow
See the flow of a vast space at a glance
The larger and more multi-level a space, the harder it is to tell where it’s crowded. See crowd flow by zone on one screen, then prepare staffing and guidance in advance.
Bring visitor flow into space operations
We read the flow from the entrance to stores and amenities. See where people linger and where they pass through, then use it to refine layout and guidance routes.
Anomaly detection with fewer blind spots
The larger the space, the harder it is to check every area. When signals such as overcrowding, falls, or unusual situations appear, the right person is notified for a fast response.
Connect journeys broken across cameras
MTMC (Multi-Target Multi-Camera) Spatial AI joins scattered camera views into one coordinate system. Only continuous flow across zones and floors can turn large-space data into operating decisions.
Explore Spatial AI technology- Unify multiple camera views into one spatial coordinate system
- Track continuous flow across floors and zones
- Compare zone-by-zone crowding and flow by the same standard
- Turn tenant-by-tenant performance into evidence for rent discussions
Wondering if your camera layout can do this too?
Request a consultation"It was large and multi-level, so it was hard to tell where it was crowded, but seeing the whole space on one screen lets us prepare staffing and guidance in advance."
Large-space operations lead
Hypermarket · Mall
An exhibition space that quantified interest in themed and pop-up zones, and rearranged accordingly, saw visitors rise about 30%.
* Illustrative, based on customer cases. Results vary by store conditions.
Before · After
From watching each camera apart, to seeing the space as one
Knowing first
saai care · MTMCMTMC links many cameras into one coordinate. It senses crowding and unusual paths first.
- Many cameras → one spatial coordinate
- Zone crowding and dwell, live
- Unusual paths sensed before the threshold
Adoption process
Three steps, from pilot to every store
- 01
Pilot
2–4 weeks
Start in 1–3 stores, on the CCTV you already have.
No new hardware, no upfront burden
- 02
Validate
4–8 weeks
Set a KPI baseline and verify the improvement in numbers.
If it does not work, you can stop right here
- 03
Roll out
On your HQ schedule
Replicate the validated standard across every store, the same way.
Standardized without per-store variance
* Durations are illustrative. They vary with store count and site conditions.
Case studies
Real deployments in this industry
Same prescription, two showrooms, two answers
We swapped the cars and the experience zone, then measured 44 days twice. The results split.
Showroom B · visitors
About 49% up
One in three walked out. We added a till and measured again
One week after installation, then six months on. Checked twice.
Visits to fixtures near the new till (six months on)
More than 10x
3 Core SAAI Products
How three products help this space together
Detect, analyze, and act connected in one continuous operating loop.
saai care
Detects critical anomalies in real time.
• Detects falls and abnormal dwell by zone across large mixed-use spaces
saai insight
Analyzes why it happened and reveals trends.
• Analyzes movement across every zone with MTMC multi-camera tracking
- 103 patents
- SOC 2 · PIPA certified
- 1,700+ cameras connected
- 8+ partner brands
Enterprise rollout
Bring many spaces
into one operating system
Tell us the scale and environment of the spaces you run, and we will guide you to an enterprise-wide approach.
Request a consultation