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The Human-Agent Operating Layer

The environment your company builds and trains its own AI in.

It installs inside your own infrastructure and profiles your systems where they already sit. Engineering sets the rails once; the people who hold the knowledge build the agents.

self-hosted, or fully air-gapped any model, any harness no migration

Nothing moves Your systems are profiled where they sit and queried live. No warehouse project first.
Your experts build it The people who hold the knowledge author the rules, in plain English, without a ticket.
You keep what it learns Corrections, traces and evals accumulate on your infrastructure, and stay yours.

Where AI programmes stall

Three things stop the programme, and it is the same three every time.

“Modernise your data first.”

A data programme is quoted before a single agent does real work, and adoption stops there for months.

“We were told to build it. We cannot hire for it.”

Governance, identity, sandboxes and safe deployment inside their own environment. An infrastructure bench with no way to staff it.

“It just makes it unfeasible.”

Pilots that look fine at demo scale die on the economics of a frontier model on every call.

Or wait for the models, and what that costs →

The precondition we do not have

Everyone else needs the data moved first.

SynOS comes to the data instead. Your systems are profiled where they sit, and the rows are queried live at answer time.

Fig. 02 · the same table under both approaches
How profiling works →

What installs

One layer, wired in once, under the AI tools your teams already use.

Claude Code, Codex, Cursor, chat or your own agents all reach the same brain and the same governed tools, so a change of tool is never a change of platform.

Fig. 10 · your tools above, your systems below, the layer between
See how each piece works →

What accumulates

A correction is made once, and then it is permanent.

Reviewed once, it becomes a rule every agent inherits, context in the brain, a case in your evals, and material a model of your own can learn from later.

Fig. 01 · one correction, four destinations
What training your own AI actually involves →

Two ways in

Same platform, same rails, same install.

Inward · your operations

Your experts build the agents

Internal work is blocked on an engineering queue. The people who know the exceptions author it themselves, inside rails set once.

Outward · your product

The SaaS you ship becomes agent-native

Agents inside your product acting on each customer's own data, with an isolated brain per tenant, in your own tenancy.

What teams build on it →

See it on your own systems.

A walkthrough on your actual workflows, or early access if you would rather read first.