One model, shared across every customer
Cheaper to run, but what one account teaches the model can surface in another account's results.
TEZZ · ARCHITECTURE
A boundary that is structural, not a setting
Architecture, for an AI system, means where the boundaries actually sit: which data a model can see, whether that data ever mixes with another client's, and where the system physically runs. Two AI products can share similar underlying technology and still differ completely on these boundaries.
That difference is what determines the actual risk to a client's data, not the marketing copy around the model.
THE RANGE OF PRACTICE
What a shared model looks like, and what a structurally isolated one looks like
Described by behaviour, not by a score.
Logically separated, same underlying weights
Data kept apart in the application layer, while the underlying model itself was trained on a mixed pool.
A model that belongs to one client alone
Nothing learned from one account's data can appear in another's, because the boundary is structural, not a setting.
WHERE WE SIT
One client, one model, never mixed
The same least-privilege thinking applied to access control generally.
A model built for a client belongs to that client alone and is never trained on another client's data. TEZZ runs inside Gigagrams platforms only, never deployed onto a client's own infrastructure, and is reached exclusively through the platform's API. Its advanced capabilities are in training. AI projects for clients are taken, built to Gigagrams protocols: zero trust, human in the loop, multi-agent.
STRAIGHT ANSWERS
Questions about TEZZ architecture
The ones that come up before anyone signs anything.
Where the boundaries sit: which data a model can see, whether that data ever mixes with another client's, and where the system physically runs. Two AI products can use similar underlying technology and still differ completely on these boundaries, which is what actually determines the risk to a client's data.
No. A model built for a client belongs to that client alone and is never trained on another client's data. Nothing learned from one account crosses into another, by design rather than by policy alone.
Inside Gigagrams platforms only. It is never deployed onto a client's own infrastructure, and customers reach it exclusively through the platform's API rather than running it themselves.
Yes. It follows the same zero trust and least-privilege thinking applied to access control generally: nothing gets broader reach than the specific task requires, whether that is a person's login or a model's training data.
Building on a Gigagrams platform and want TEZZ involved?
Tell us what you are building and we will scope what it can assist with, with the same data isolation applied to every client.