What gets done stays.What stays gets used.
STATUS QUO
The data in this businesshas never lived in one place
FOUNDATION
Build it once,and every feature after that just uses it
This judgment used to live only in the heads of veteran staff. Now it is part of the system.
IN PRODUCTION
On the same foundation,three things the agents already run
3D studio
Put a fabric on a 3D pattern and see it on the spot, without waiting for a sample.
WORLD MODEL
It happens in the model first,then in the company.
A model of this business: five kinds of things and the relationships between them. Every time the company does something, it knows a little more.
L1World
Fabrics, styles, customers, orders and trends: each defined, their relationships written down in one place.
L2Perception
Every question, recommendation and document is written back onto this map.
L3Action
Fabric search, recommending, fetching, cleaning, translating, 3D try-on: each is an agent, and they all read the same map.
L4Rehearsal
Run it in the model before it happens: score the recommendation, size the restock, put the fabric on a 3D pattern.
Foundations: the six things holding all of that up
Every one of them can be opened up right now
Business relationship graph
Tool whitelist
Fixed scoring formula
Answers carry their sources
A fixed exam before release
On-premise deployment
STACK
Take it apartand it is three layers stacked
Definitions and relationships
Every thing has its own definition, and the relationships are written down instead of living in one person's experience.
Acting on the map
Data access goes through a whitelist, and what it cannot answer it declines. Swap one out and the others are unaffected.
Recommendation is the first one
A new scenario does not rebuild the foundation. It connects to relationships that are already there.
FAQ
Four things we get asked most
Our data is a mess. Can we still use this?
Yes. It is cleaned once on the way in, rather than left for the model to guess at.
What data can the agents read?
Only authorized tables and fields, with the sources carried in the answer. Anything outside the whitelist, it declines.
What if a recommendation is off?
Every recommendation can be expanded to show what it was based on: which hard constraints it matched, and which dimension pulled the score down.
What if nothing matches?
It relaxes one condition and searches again, then says which condition it relaxed and which close matches are worth a look.