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One platform, from the machine
to the decision

One place to move operational data, give it meaning, verify it and act on it, distributed across edge, site and cloud.

Connected is not the same as usable

Most of what an industrial estate publishes never gets used, and the reason is not collection. It is that the people closest to it cannot rely on what arrives.

No context

A value arrives with a tag name and nothing else. Which machine, which line, which batch, what normal looks like. None of it travels with the number.

No trust

Structures drift, sites name things differently, and nobody can say whether a reading is right. So teams build around the data rather than on it.

No reuse

The pilot works at site one and gets rebuilt at site two, because none of the modeling, governance or logic carried over.

No accountability

An agent acts on a value nobody checked. A tag was renamed or a sensor drifted, and afterwards nobody can reconstruct what the data really was.

Four steps, and each one makes the next possible

01

Connect

Move operational data reliably across OT, IT, edge and cloud, on one backbone instead of point integrations.

Explore Connect →
02

Contextualize

Give every signal shared meaning and a governed structure, so the data can be trusted before anyone builds on it.

Explore Contextualize →
03

Analyze

Turn trusted data into operational intelligence, computed where the data is created rather than in a warehouse.

Explore Analyze →
04

Act

Put that intelligence to work in governed workflows, with people in the loop wherever you want them.

Explore Act →

What changes when the data underneath works

One number, one definition, at every site

The metric means the same thing in every plant, so a comparison survives challenge and the Monday review runs on one set of figures instead of twenty spreadsheets that each got there differently.

See a deviation and fix it in real time

The problem shows up on the line that is running, not in tomorrow's report, so the people standing next to the machine can still do something about it.

Do it once, and every use case after gets easier

Model, govern and connect a site one time. The next project starts from what already exists instead of rebuilding the plumbing underneath it, so each one costs less than the last.

See it in the platform

Screenshot of the HiveMQ Platform.

Where this shows up in the business

HiveMQ removes the data constraint. Your team, your applications and your process work deliver the number.

Business outcome
What HiveMQ does
What you still need
Less unplanned downtime
Makes condition data available and trustworthy in time to act on it.
Your maintenance program and your condition models.
Higher OEE and yield
Makes the number consistent across sites and available while the line is still running.
Your continuous improvement team and line-level process work.
Lower cost per unit
Removes the data preparation that stalls these projects before they start.
Your energy and scrap initiatives.
Industrial AI that reaches production
Delivers inputs that are governed and continuously verified, and keeps the record of what each decision was made on.
Your use cases, your models, and your risk appetite for autonomy.

Proven where operations cannot afford to stop

The comprehensive data gathered through HiveMQ provides a fertile ground for developing and deploying a multitude of AI-driven use cases.

Gopalakrishnan Rajaram

Solution Architect, Ford

HiveMQ has delivered the stability and reliability that we require for 4 years now and it has not gone down.

Marius Hertfelder

Chief Software Architect, Mercedes-Benz

ISO 27001 certified · SOC 2 Type II · TISAX · 10+ years in production · See the security and compliance detail

Start with one operational improvement. Scale the results.

Start with the outcome that matters most. Prove value on the data you already have, then scale the pattern.