An AI platform that doesn’t just monitor industrial production environments — it remembers them. Four specialized reasoners on a biologically inspired memory substrate.
A public demo instance is running — no registration, no request. The credentials below are deliberately shared so anyone can look around.
E-mail
chef@foreman.de
Password
ForemanDemo2026!
The login is the plant-manager profile: it sees the whole fleet and may trigger the reasoners, so every capability is reachable from one account.
The interface is German. FOREMAN speaks the language of the shop floor it was designed for.
The data is simulated. No real plant is connected. Every prediction carries a visible simulation caveat — a deliberate design rule, not a placeholder.
It is a shared instance. Anything you enter is visible to everyone else and stays in the database. Please do not enter real personal data.
Nothing here switches anything. FOREMAN explains, it does not actuate. There is no path from this interface to a machine.
The full project deck — problem, three-layer architecture, its reasoning layer, and how it is built. Interactive; use the arrows to navigate. Open in full screen ↗
Industry delivers the data, FOREMAN reasons, operators act. Each layer is autonomous — the platform is an open node in the industrial software stack, not a closed app.
Sensor streams, PLC logic (OPC UA, MQTT, Modbus), log files, and maintenance history — integrated through normalizing, anonymizing adapters.
Four specialized reasoners work against a shared memory substrate, routed through a local + cloud model gateway.
An operator dashboard with traceable explanations — plus an MCP interface that any third-party system can consume.
Each reasoner answers a question that a snapshot dashboard cannot.
Links incidents, sensor drift, and operator logs into coherent narratives over time.
Flags changes in a machine’s behavior against its own historical profile.
Matches the current state against consolidated precursor patterns from past incidents.
Causal deviation analysis between maintenance events and machine behavior to optimize intervals.
Load data, not load simulation. FOREMAN does not simulate load itself — that needs parameters outside its observation boundary. It exposes the observed load profiles and limits read-only over MCP, for an external simulation tool to build on.
Every change passes defined gates before it reaches main. The binding definition lives in the repository’s GROUND_TRUTH §10–§11.