The core argument
Before AI can work in a space,
the space has to become data.
Documents and transactions are already data, so a language model can sit on top of them today. Stores and sites are different. A space has to become data first (DX) before AI can work on top of it (AX).
Computer vision based spatial AI is what creates that DX. Adopt the two separately and the work of connecting them remains.
Anonymization
Behavior signals remain after identity is removed. This is where data you are allowed to keep begins.
More on AnonymizerSpatial coordinates
Flow, dwell, and shelf contact land on one coordinate system. Here a space becomes data you can query.
More on Spatial AIOperating standards
Different stores are counted by the same definitions. An answer from one store holds in another.
Model composition
We do not hand everything to one model.
The vision models that read a space and the language models that put it into words are good at different things. We pick and connect the right model for each task.
Our own vision models
We build the part that reads flow and state without identifying anyone. Video is anonymized on site before anything else touches it.
Foundation models, combined
Summaries and dialogue run on published language models. We do not rebuild them. We use what they already do well.
Grounding in spatial context
The language model receives anonymized spatial data together with the operating standards. It answers about this space instead of in general.
Which combination works better is checked against the record of approvals and rejections. That record is also the reason to swap a model.
How a proposal is built
Behind one proposal there are four steps.
Detect the signal, analyze the evidence, propose the action, and let a person confirm. Pick a case and follow all four steps.
- 01
Detect
saai care
- 7 people waiting at checkout
- Average wait 4 min 12 sec
- 1 lane open
- 02
Analyze
saai insight
- The average wait for this weekday and hour is 2 min 10 sec.
- Entrance visits are close to usual. What grew is dwell at the checkout.
- Over the past 4 weeks the wait passed 4 minutes on 3 days, all of them rainy.
- 03
Act
saai agent
Open one more checkout lane.
Priority · High
Why this proposal
The 4-week record shows more people leaving without paying once the wait passes 4 minutes.
Expected · Wait around 2 min · example estimate
- 04
Confirm
A person
From here on, it belongs to a person.
Principles
We drew the lines it will not cross, first.
However capable the agent becomes, these three lines stay.
The AI stops at the proposal
Nothing runs without approval. Even after approval, it can be stopped and rolled back.
No claim without evidence
What the proposal is based on sits right next to the proposal.
A rejection stays on record
When a person says no, that judgement is kept and shapes the next proposal.
How these three lines held up in the field is recorded in a case study.
A hypothesis, a shelf change, and a second measurementFeature standard
Before a feature ships, it must pass five checks.
This is how principles become product criteria.
Check 03 · Does it reduce what people need to read?
An example placing a legacy alert stream beside a prioritized view. It narrows today’s work instead of adding metrics.
Explore adoption
Decide where to start together.
Tell us how you operate today. We will propose a level to review.