The platform for AI built on your own systems.
SynOS connects the systems you already run, gives your agents governed access to them, and keeps every run and correction in the shape a training set is built from. It installs in your own cloud.
runs in your own cloud nothing is copied out any model, any agent framework
Where you are
Three situations, and what changes first in each.
01
Nothing in production yet
The board is asking, and nothing has reached a customer. Start with one workflow on the systems as they stand, with no data programme in front of it.
02
Shipped, and adoption is low
Six to twelve months of building, and the impact your customers were promised has not arrived. What the agent can read is usually the reason, and so is the absence of a route for corrections to come back.
03
Working, at frontier prices
Every call runs on a frontier model, and prompting has stopped lifting accuracy. Both have become budget questions, and the answer is a smaller model trained on your own work.
Outcomes
Three things a team is buying.
Accuracy prompting could not reach
The failures that survive every prompt rewrite are the ones where the agent never had the information. Your systems hold half of it and your experts hold the rest. Both become a training set, and your own trainer turns it into a model.
A lower cost per run
Smaller open weight models, fine tuned on your own work and running on your own infrastructure, in place of a frontier model on every call. Every run is priced per model and per provider, so the difference is measured rather than assumed.
AI sovereignty
The corrections accumulate on your own infrastructure and the models built from them are yours. Nothing crosses your boundary, and no other company trains on your work.
The platform
The environment, in four pieces.
An agent needs somewhere it can act and be scored. These four are what that place is made of, and each is useful on the day it is switched on.
01 · Context
What your systems hold, and how the work is done
Connects warehouses, databases, applications and documents, profiles what is in them, and resolves the same customer across all of them. The procedures your experts follow are captured alongside it, so an agent reads your business rather than a prompt describing it.
02 · Tool inventory
Every read and write an agent can take
One catalogue of the actions available across your systems, each scoped to who is asking and recorded when it is used. It is the same inventory whichever agent framework your team builds on.
03 · Runs and scoring
Where an agent rehearses
An agent acts against your systems with the write it intended intercepted and scored rather than committed, and against test cases your team writes. A release that scores worse than the last one does not ship.
04 · Training data
Runs and corrections, curated
Traces, tool calls and the corrections your experts make in the tools they already use become a curated dataset, exported in the format your trainer reads. We build the environment and the dataset. Your trainer does the training.
Where it sits
Under whatever you already run.
SynOS installs between the agents your team builds and the systems those agents need to read and act on. Nothing is migrated, there is no second copy of your data, and the agent framework and the models stay your choice.
Deployment
It runs where your data is.
Your infrastructure
Self-hosted by default, in your own cloud account or on your own hardware. The air-gapped configuration has end to end tests.
Your data
No data leaves your boundary, and the install does not depend on us being reachable.
Your models
Open weight models on your own infrastructure, or a hosted provider, chosen per workload rather than for the whole platform.
What you keep
Connections start read only. The environment, the traces and the agents stay with you whether or not you carry on afterwards.
Bring one workflow.
Whichever one you would fix first. We will read it with you and say what this reaches, and what it does not.