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Manufacturing AI OS

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Manufacturing AI OS

A new operating system that runs the factory

Just as a computer has Windows, a factory needs an operating system that looks after the whole. AI watches over it 24 hours a day, analyses, and identifies problems. And it tells you what should be done now, with the evidence behind it.

Fragmented data, unified
Equipment, quality, documents and field know-how on one map
AI watches 24 hours a day
Agents with separate roles monitor, analyse and predict as a team
It supports the decision
It points out what should be done and why, with the evidence

Plenty of data, but still no answers

What happens on the shop floor today

Factories already run plenty of systems. Yet when something goes wrong, working out why is still a person’s job.

What happens on the shop floor today

Factories already run plenty of systems. Yet when something goes wrong, working out why is still a person’s job.

Information is scattered

Production records sit in one system, inspection results in another, and work know-how on paper. There is no way to see it all at once.

Root cause takes a day

When a problem appears, several people dig through their own data. By the time the cause is found the next batch is already running.

Veteran instinct disappears

Twenty years of know-how lives only inside one person’s head. When that person is away, the whole line wobbles.

AI stops at the demo

It demonstrates well but never scales, because the data the AI needs to look at was never organised in the first place.

Information is scattered

Production records sit in one system, inspection results in another, and work know-how on paper. There is no way to see it all at once.

Root cause takes a day

When a problem appears, several people dig through their own data. By the time the cause is found the next batch is already running.

Veteran instinct disappears

Twenty years of know-how lives only inside one person’s head. When that person is away, the whole line wobbles.

AI stops at the demo

It demonstrates well but never scales, because the data the AI needs to look at was never organised in the first place.

Unified Operations Console

Key Functional Features

Unified Operations Console

The whole factory on a single screen

Whether everything is running well, where the risk is, what to deal with first. One screen, with no switching between systems.

Status at a glance

Historical parameter tracking for trend analysis and quality improvement

Only what matters rises to the top

Out of the thousands of alerts raised each day, only the ones a person must see are lifted to the front.

It points out what to do next

The AI posts its proposal with the evidence and the expected outcome attached, so a long deliberation ends in a single confirmation.

Multi-agent orchestration

AI assistants that talk to each other while they work

Not one all-purpose AI, but several AIs with different jobs working as a team. When the quality AI notices something off, it asks the equipment AI, then brings the combined answer to a person.

The roles are divided

One AI always watching, one that comes when called, one that computes overnight. Each does only what it does best.

Every exchange stays on the record

Who asked what and how it was answered is kept, so any decision can be traced back later.

They know when to stop

Past a set time or step count they are cleaned up automatically. No agent runs on forever burning resources.

Structuring fragmented data and manufacturing knowledge

Connecting scattered records to answer “why”

Numbers inside tables and sentences inside documents are placed on the same map. So when you ask “why did defects rise last week?”, it also shows what it looked at to reach that answer.

Ask in everyday language

There is no query syntax to learn. Ask the way you would ask the colleague at the next desk.

The evidence always comes with the answer

Which records and which documents it used are always shown. It does not offer answers that cannot be verified.

A person confirms the connections

The AI only proposes “these two look related”. Whether it becomes a real relation is decided by the owner.

Static and dynamic manufacturing knowledge backend

The more knowledge it holds, the more it can do

Knowledge that rarely changes — equipment specifications, working methods — and information that changes moment to moment are held together inside the operating system. And because there are open paths to draw on that knowledge, a new function is layered on top rather than built from scratch.

Factory knowledge in one place

Equipment specifications, work standards and maintenance history sit alongside the values coming off the line right now, organised in the same place.

Connect directly through static and dynamic APIs

Frequent look-ups run through fixed static APIs, look-ups whose conditions change every time run through dynamic APIs. A new system connects the same way instead of being built source by source.

Natural-language query changes how fast you can extend

Even without knowing the API, a plain question reaches the same knowledge. Quality prediction, energy saving, a new report — each is layered on this path, with no need to rebuild the data connection.

Unified Operations Console

The whole factory on a single screen

Whether everything is running well, where the risk is, what to deal with first. One screen, with no switching between systems.

Status at a glance

Historical parameter tracking for trend analysis and quality improvement

Only what matters rises to the top

Out of the thousands of alerts raised each day, only the ones a person must see are lifted to the front.

It points out what to do next

The AI posts its proposal with the evidence and the expected outcome attached, so a long deliberation ends in a single confirmation.

Multi-agent orchestration

AI assistants that talk to each other while they work

Not one all-purpose AI, but several AIs with different jobs working as a team. When the quality AI notices something off, it asks the equipment AI, then brings the combined answer to a person.

The roles are divided

One AI always watching, one that comes when called, one that computes overnight. Each does only what it does best.

Every exchange stays on the record

Who asked what and how it was answered is kept, so any decision can be traced back later.

They know when to stop

Past a set time or step count they are cleaned up automatically. No agent runs on forever burning resources.

Structuring fragmented data and manufacturing knowledge

Connecting scattered records to answer “why”

Numbers inside tables and sentences inside documents are placed on the same map. So when you ask “why did defects rise last week?”, it also shows what it looked at to reach that answer.

Ask in everyday language

There is no query syntax to learn. Ask the way you would ask the colleague at the next desk.

The evidence always comes with the answer

Which records and which documents it used are always shown. It does not offer answers that cannot be verified.

A person confirms the connections

The AI only proposes “these two look related”. Whether it becomes a real relation is decided by the owner.

Static and dynamic manufacturing knowledge backend

The more knowledge it holds, the more it can do

Knowledge that rarely changes — equipment specifications, working methods — and information that changes moment to moment are held together inside the operating system. And because there are open paths to draw on that knowledge, a new function is layered on top rather than built from scratch.

Factory knowledge in one place

Equipment specifications, work standards and maintenance history sit alongside the values coming off the line right now, organised in the same place.

Connect directly through static and dynamic APIs

Frequent look-ups run through fixed static APIs, look-ups whose conditions change every time run through dynamic APIs. A new system connects the same way instead of being built source by source.

Natural-language query changes how fast you can extend

Even without knowing the API, a plain question reaches the same knowledge. Quality prediction, energy saving, a new report — each is layered on this path, with no need to rebuild the data connection.

Following the order is the fastest way

Lead: “You do not start a house with the roof. The Manufacturing AI OS is at its strongest and fastest when it is built in the order below.”

Connect to the shop floor

Collect every value coming off equipment, sensors and inspection machines, with no gaps and no interruptions. If this is blocked, nothing after it means anything.

Organise the assets and the knowledge

Register what each machine can do and how much, then weave scattered records, drawings, work standards and maintenance history into a single knowledge map.

Analyse the data and define the prediction and judgement models

Find the patterns in the accumulated data and set the criteria for what counts as normal and when to call it an anomaly. Quality prediction, equipment anomaly forecasting and specification judgement models are built at this stage.

Deploy the AI agents

Only now can the AI make judgements with evidence behind them. It watches around the clock, analyses, and points out problems before they arrive.

Validate on one line, then widen

Confirm the effect on a single line, then extend to other lines and processes. The results that accumulate become evidence for later judgements, so it grows more accurate the more it is used.

Connect to the shop floor

Collect every value coming off equipment, sensors and inspection machines, with no gaps and no interruptions. If this is blocked, nothing after it means anything.

Organise the assets and the knowledge

Register what each machine can do and how much, then weave scattered records, drawings, work standards and maintenance history into a single knowledge map.

Analyse the data and define the prediction and judgement models

Find the patterns in the accumulated data and set the criteria for what counts as normal and when to call it an anomaly. Quality prediction, equipment anomaly forecasting and specification judgement models are built at this stage.

Deploy the AI agents

Only now can the AI make judgements with evidence behind them. It watches around the clock, analyses, and points out problems before they arrive.

Validate on one line, then widen

Confirm the effect on a single line, then extend to other lines and processes. The results that accumulate become evidence for later judgements, so it grows more accurate the more it is used.

Thirty minutes is enough to tell whether it fits your factory

We look at the systems and the data you have today, and set out where it makes sense to begin.

Getting started

Fragmented data, unified

AI watches 24 hours a day

It supports the decision