WERK · Agent

Industrial AI

Your data scientist is already on the shop floor.

Your engineers run the analyses themselves – on their own facility data. Without years of standardization projects. Without a data-science bottleneck.

How it works

6-week pilot · On-premises, private cloud or hosted · TLS 1.3 · You decide where your data lives

WERK AgentOnline

Question

Line 3 fails about 20 minutes after every shift change – why?

Analysis

Finding

At the shift change the hydraulic oil cools by ~9 °C, viscosity rises – pressure build-up is delayed until the sensor triggers a shutdown.

Recommendation: Activate standby circulation – failures reduced by 96%.

Example analysis on anonymized facility data – not your live data.

The problem

AI pilots die between prototype and shop floor.

The data is there. But every analysis needs specialists again – centralized, with waiting time, and throughput is structurally capped.

Data to the expert

POC graveyard – pilots never scale beyond the first facility
Data chaos – every facility speaks its own language, legacy sensors included
Standardization trap – unify for months first, then value maybe someday
Data-science bottleneck – throughput ends with a handful of specialists

Experts to the data

Your engineers analyze themselves – the edge is in their process knowledge
The agent reads existing control systems, sensors and legacy data with no prep
Value from week one – no standardization as a prerequisite
Scales with the workforce – one agent per engineer

Not data to the expert. The expert to the data.

How it works

From data source to production-ready analysis.

Connect data sources

The agent connects to existing control systems, sensors and legacy data – without prior standardization.

Ask questions, analyze

Your engineers ask in natural language. The agent explores, correlates and visualizes – transparently, step by step.

Train & operate models

Your engineers – not external teams – build predictive models with their facility knowledge and put them into operation.

A real case

Line 3 – Maintenance

Machines fail about 20 minutes after every shift change. The agent finds the cause – in minutes instead of weeks.

Finding

At the shift change the hydraulic oil cools by ~9 °C, viscosity rises, and pressure build-up is delayed until the sensor shuts down.

Recommendation

Activate standby circulation so the oil stays at temperature.

Result

Failures reduced by 96% – no new sensors, no standardization project.

Comparison of unplanned failures on Line 3 over the first weeks after activating standby circulation versus prior operation. Illustrative, anonymized facility data.

The value

A win for both sides.

For engineers & technologists

No ticket queue. Analyses in minutes instead of months – and process knowledge delivers better results than external teams.

Analysis in minutes

For management

Decentralized scaling without a central bottleneck. Value from week one, with no standardization as a precondition.

Scales with the team

For data quality

Transparent, reproducible analysis steps reveal where data quality is missing – investment follows the real cost impact.

Transparent & reproducible

Minutes

analysis turnaround instead of weeks in the ticket system

€0

standardization budget as a prerequisite

1 : 1

one agent per engineer – throughput grows with the workforce

Operations & security

You decide where your data lives.

Encryption by default, access controlled – your data stays under your control unless you choose to share it.

On-premises

The agent runs in your own infrastructure.

Private cloud

Isolated instance in your cloud environment.

Hosted

Operated by us, with strict data isolation.

TLS 1.3 in transit · Encryption at rest · Controlled access

Become a pilot facility.

6 weeks, one real use case, 1–3 engineers, about 2 hours of feedback per week. Entry point: a 30-minute demo with your own dataset – a CSV export is enough.

Usually free during the pilot phase · a CSV export is enough