Our machines are twenty years old. Is this possible?

Usually yes. Older machines are read through PLC tags, retrofit sensors or the historian. The constraint is the control network design, not the age of the equipment.

Do we need a digital twin?

Not at the start. A twin is worth building once the line data is trustworthy and you have a scheduling or capacity question expensive enough to simulate.

Manufacturing & Industrial · Smart factory (Industry 4.0)

Industry 4.0 for the plants you already run

Connect the plant floor to the data layer, then make it decide faster.

Book the Data Foundation Assessment

Overall equipment effectivenessUnplanned downtimeScrap and rework rateEnergy per unit produced

The problem

What this fixes

Line data sits in historians, PLCs and spreadsheets that never meet. Production, quality and maintenance each keep their own version of yesterday, so the daily meeting starts by reconciling numbers instead of fixing the line.

OT/IT integration, unified namespace, an industrial data platform and the analytics on top: OEE, throughput bottlenecks, energy per unit, quality genealogy and a digital twin of the line. Built on standard industrial protocols so it works with the machines you have, including the ones nobody wants to touch.

Manufacturing & Industrial

OEE that everyone trusts

Availability, performance and quality computed from machine signals, not shift reports.

Manufacturing & Industrial

Bottleneck and changeover analysis

Find where the line actually loses time, including micro-stops nobody logs.

Manufacturing & Industrial

Energy per unit

Consumption tied to product and line so efficiency claims can be evidenced.

Manufacturing & Industrial

Faster rollout to the next plant

A unified namespace makes the next site a configuration exercise rather than a new project.

In this sector

Where it earns its place

Delivery

How we build it

Foundation check

Before any build

We inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.

Narrow first release

First release

One workflow, one team, real traffic, measured against the baseline we captured before we started.

Controls and evaluation

Runs in parallel

Guardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.

Scale and hand over

After sign-off

Additional workflows, sites or languages, then runbooks and training so your team can operate it without us.

Data foundation

What has to be right first

Machine connectivity via OPC UA, MQTT or historian export.
An asset hierarchy that matches the physical plant.
Production orders and quality results tied to the same time base.
A segmented OT network with an agreed data diode or DMZ pattern.

Controls

Guardrails and KPIs

Every action is logged and replayable

Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.

Data stays where your regulator requires

In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.

Accuracy is monitored, not assumed

A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.

A human owns every consequential decision

Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.