From a plant-floor signal
to production that holds.
Intlix connects plant, product, quality, maintenance and supply context across IT and OT — so teams catch exceptions while they are still exceptions, not downtime, scrap or a missed delivery.
How we create value
Industry context changes the engineering.
The operating friction
Signals live across sensors, technical documents, PLM, MES, ERP, quality systems and supplier networks. They almost never arrive as one coherent operating picture.
We use that friction to define the context, integration and controls the engineering has to solve — rather than handing over a generic list of use cases.
How Intlix intervenes
Engineer backward from the outcome.
Start from the decision
Map the workflow, the people in it, the systems it touches, and the measure it is judged on.
Assemble the context
Connect the data, documents and systems that decision actually depends on.
Put intelligence in the path
Apply models, agents and automation where they improve the call — not wherever they fit.
Run it in the open
Keep human authority, evaluation, observability and running cost visible after go-live.
The operating journey
Six steps, one accountable chain.
Observe
Product, machine and supply signals
Detect
Anomalies and emerging risk patterns
Explain
Operating context and supporting evidence
Decide
Operator and planner authority
Execute
MES, ERP, maintenance and logistics
Improve
Reliability, quality and unit economics
What we build
Four engineering motions.
One operating outcome.
Connect IT and OT context
Plant data, enterprise systems, documents, edge and cloud integration.
Engineering teams see the operating condition in context.
Engineer operational intelligence
Predictive models, anomaly detection and knowledge retrieval.
A raw signal becomes an explanation and a recommended action.
Integrate the authorized action
Maintenance, quality, planning, supplier and logistics workflows.
Intelligence reaches the operator or system that can act on it.
Run for industrial reliability
Edge and cloud operations, observability, security and resilience.
It stays dependable in the environment where production happens.
Let's talk about your manufacturing workflows.
Bring us the decision that is slow, manual or hard to defend. We will map what it would take to engineer it properly.
Start the conversationSector questions
What manufacturing teams
ask us first.
Do you work with OT as well as IT?
Yes, and the boundary between them is where most of the risk lives. We respect the separation, and never place anything on the control network that has not been agreed with the engineers who own it.
What if our plants run different systems?
That is typical after any acquisition history. We normalize at the data layer rather than forcing a single MES on every site, which is faster and far less disruptive.
How do you prove quality models actually work?
Against labelled defect history and then in shadow mode against live production, so the model's judgment is compared with the line's own before it influences anything.
Does this need new sensors?
Often less than expected. Most plants already generate more data than they use, and we would rather exhaust that before asking for capital expenditure.
What about downtime during rollout?
Deployment is planned around your maintenance windows, and anything touching production runs in observation mode before it takes any action.