Intelligence at infrastructure scale

Orchestrating every model, every agent and every approval from a single layer.

Your operation, everywhere it runs
One layer over every tool your business already uses
AI-OS sits above the systems you run on — email, chat, tickets, CRM — and watches the work arriving across all of them.
Today, none of it talks
Every tool holds a piece. Nothing holds the whole.
The complaint lands in email, the fix lives in the tracker, the context is in somebody's head. Work waits on whoever notices it first.
Model-agnostic routing
The right model for each task, not one vendor
Claude, GPT, Gemini or a local model — each task is routed to whichever performs it best. Models are replaceable; the operating system is the product.
It learns your business first
SOPs, policies and decisions, before it touches anything
Obsidian, Git, Notion, Drive. The operating system reads what your company already wrote down, and every agent works from that same memory.
Eight layers, one operating system
Knowledge, data, memory, agents, workflow, routing, automation, governance
Every task falls through the same stack — retrieve context, pick the agent, pick the model, execute, validate, log. The layers are what make it repeatable instead of a one-off prompt.
An AI workforce, with you on the gate
Specialised agents, handing work to each other
Support takes the complaint, Developer proposes the fix, QA verifies it. Nothing ships until a human approves — every business runs a different roster.
Every action leaves a record
Audit log, versioning, role-based access
Nothing happens off the books. Payments, contracts and anything leaving the building wait on a human, and the trail shows who approved what, when.
It gets better at your business
Finds the repetitive work, then removes it
The operating system watches where the time goes, proposes the automations worth building, and reports what it saved you.
AI-OS
Intelligence at infrastructure scale
Humans set direction. The operating system remembers the context, coordinates the agents, and measures what it saved you.

The eight layers

Knowledge — SOPs, policies, brand, decision logs
Operational Data — Customers, tasks, CRM, inventory, KPIs
Memory — Long-term, project, session and task
Agent Runtime — Specialised agents with roles and permissions
Workflow Engine — Trigger, retrieve, execute, validate
Model Router — Best model per task, any vendor
Automation — Email, CRM, invoices, reports, scheduling
Governance — Access, audit logs, approval gates

Where the knowledge comes from

Obsidian — SOPs, notes
Git — Code, decisions
Notion — Policies, specs
Drive — Contracts, brand

Any model, no lock-in

Claude — UX, writing, architecture
GPT — Coding, APIs, reasoning
Gemini — Very large documents
Local — Confidential workloads

How work moves

Agents hand work along a chain, and it stops at a human before anything ships.

Support — Complaint received
Developer — Fix proposed
You — Approve (human approval required)
QA — Verified
Deploy — Shipped

Every action leaves a record

An example trail. Payments, contracts and anything leaving the building wait on a human, and the log shows who approved what.

14:02:11 SUPPORT-AI Ticket #4417 classified — billing
14:02:13 SYSTEM Context retrieved — 3 SOPs, 1 policy
14:02:40 DEVELOPER-AI Fix proposed — patch 2 files
14:06:02 M. ANAS APPROVED — human gate cleared
14:06:03 QA-AI Suite green — 214 passed
14:07:55 OPS-AI Deployed — build 1042, reversible

What it reports back

31h Returned this week — Across 6 agents
284 Items handled — 94% without a human
12 Held for approval — Payments and contracts

Roles you can staff

Every business runs a different roster. These are the agents available to compose one from.

Support
Sales
Finance
Developer
QA
Operations
Marketing
Data Analyst

Run your operation on it.

We're opening early access to teams who want their production work handled by an agent workforce, with approval gates they control.

Request early access

Music: LÖVI — virtual love 2001 [NCS Release]