MANUFACTURING

Software, data, cloud and AI for manufacturing operations

Q: What ROI can we expect from industrial AI?

It is measured on your ground: stops avoided, scrap receding, interventions planned instead of endured. We count on your production data, never on market averages.

Q: Are our sensor datasets sufficient?

This is checked during scoping: quality, frequency, history, access and connection to the decision being prepared. A first scope can reveal missing data before any expansion.

Q: How do you integrate with MES or ERP systems?

We start by mapping interfaces, responsibilities and OT/IT constraints. Integration can then move in stages, with the required security and operating validation.

Q: Do shop-floor teams need AI expertise?

No. Interfaces are designed for operators and technicians. They learn to read the system's alerts, not to train models.

Machine data, quality, documents and planning can become useful software products for shop-floor teams. Each use is scoped around operating, integration and site-security constraints.

Discuss your field

A specific field

Make machine condition easier to read

Vibration, temperature, pressure and history can be brought together in a view that helps qualify a piece of equipment’s condition.

Signals and context together

Assist quality control

Industrial vision can prepare an inspection, make a deviation visible and route cases to the quality team for review.

Control to confirm

Explore maintenance signals

History and drift signals can support a maintenance review, with their limits and uncertainty made visible.

Hypotheses to validate

Prepare planning trade-offs

Forecasts, inventory and constraints can be compared in a tool that leaves rules, exceptions and decisions with planners.

Comparable scenarios

Design the right software product

A project can combine supervision, operator interfaces, OT/IT integrations and cloud or edge services according to site constraints.

From sensor to operator screen

What the field requires

Industrial environments

Dust, vibration and extreme temperatures guide the choice between edge, local infrastructure or cloud, as well as test conditions.

OT/IT integration

MES, ERP, SCADA and PLCs require progressive integration, explicit boundaries and validation with OT and IT teams.

Service continuity

Go-live can be staged, with recovery modes and rollback criteria defined before a use is extended.

Industrial cybersecurity

Network segmentation, encryption, full traceability. Production data stays under your control.

A method that stays grounded

The work starts from a decision, available data and operating conditions. Software, data, cloud and AI are combined only where they serve that scope.

01
Perceive · Continuously
Signals come in
Sensors, machine history, quality flows: the architecture plugs into what already exists.
02
Detect · In real time
Drift surfaces
Process deviations, product defects and abnormal behaviour are qualified with teams, without confusing a signal with a diagnosis.
03
Anticipate · Before the stop
Failures announce themselves
Teams can examine trends and scenarios, with the missing data and uncertainty that accompany them.
04
Decide · At the right moment
Interventions get arbitrated
Maintenance priorities, process settings, replenishment: decisions arrive equipped, humans keep the final say.

What structures the work

01Signal and responsibility map
02Prototype or review interface according to scope
03Progressive OT/IT integration
04Recovery and operating rules
05Team documentation

Field questions

01What ROI can we expect from industrial AI?

02Are our sensor datasets sufficient?

03How do you integrate with MES or ERP systems?

04Do shop-floor teams need AI expertise?

NeuroVista, Paris

Start from a shop-floor decision

Tell us about a line, flow or document, the available data and the role teams need to retain.

Get in touch