We've said it before and we'll say it again: Manufacturers don't lack data. They lack data they can trust and act on in the moment it is needed.
Today, HiveMQ announces the launch of a new platform that helps manufacturers reduce downtime, improve efficiency and replicate proven operational improvements from one plant across the enterprise by getting the data foundation right.
Plant floors generate continuous signals from machines, sensors and systems but that data is often fragmented, inconsistent and missing the context that explains which asset produced it, which process, shift or product it relates to, and crucially whether the reading is valid.
Build AI on top of that data foundation and this uncertainty becomes an operational risk: every insight, recommendation and action inherits the weaknesses in the data beneath it.
HiveMQ is the industrial data platform for agentic AI, bringing data streaming, intelligence and governed action together in a single system. The platform allows manufacturers to connect, contextualize, analyze and act on operational data in real time, turning plant-floor signals into trusted intelligence that people and AI agents can use to take governed action and make better operational decisions.
Build operational intelligence on a proven MQTT foundation
With this launch, HiveMQ expands beyond industrial data streaming. Built on the trusted MQTT foundation we have run in mission-critical environments for more than 12 years, HiveMQ Platform connects data across existing OT and IT systems, gives it consistent meaning and analyzes it while operations are still running.
The platform allows people and systems to act on that intelligence close to operations, where it can have an immediate impact.
Rather than requiring manufacturers to model their entire industrial data estate upfront, HiveMQ Platform lets them start with one operational problem, contextualize the data needed to solve it and then replicate the proven approach across plants.
The platform is designed for operational questions such as why a line's performance has dropped, whether a quality deviation requires immediate intervention or how to prevent an equipment fault from becoming prolonged downtime.
Move industrial AI from pilot to production
Manufacturers face growing pressure to apply AI and automation to improve efficiency and performance. But AI is only as reliable as the operational data underneath it. Getting that data into the right context, and into the right workflow at the right time, remains one of the biggest barriers to moving AI from pilot projects into production.
The AI systems we build for our customers are only as good as the operational data underneath them. HiveMQ gives that data a reliable path from the edge to the cloud, keeping it streaming, contextualized and governed before a single model or agent touches it. That reliability under real production conditions, combined with the functionality to shape and govern data in flight, is why we want the new HiveMQ platform underneath the AI workloads we deliver for our joint customers.
Sadik Bakiu
Co-Founder & CEO, DataMax
The gap between context and action is the make or break point for industrial AI, stalling pilots before they can deliver meaningful impact or ROI.
In pharma and life sciences manufacturing, the problem is rarely a shortage of data. It is that context arrives too late to act on. HiveMQ puts structure and intelligence where the data is produced, on the plant floor, instead of waiting for everything to land in a central system first. That is precisely the gap our clients describe when they try to move AI from pilot into production.
Lukasz Drejka
Executive VP Commercial, C&F
Connect, Contextualize, Analyze and Act to improve operations in real-time
Each layer of HiveMQ Platform enables industrial organizations to:
- Connect to bring the right data together: Create a resilient, secure, real-time backbone across existing OT and IT systems, from edge to cloud, so teams can bring new plants and use cases online faster with less integration work.
- Contextualize to make data trustworthy and reusable: Define data models and governance once and apply them consistently, so teams spend less time preparing data and can replicate proven use cases across plants.
- Analyze to identify problems while they can still be fixed: Calculate trusted KPIs close to the source and surface deviations while operations are still running, so teams can diagnose problems faster and act before downtime, energy use or scrap grows.
- Act to turn intelligence into repeatable operational improvement: Put trusted intelligence into governed, human-supervised workflows, so teams can respond faster, preserve expert knowledge and repeat proven decisions safely across shifts and plants.
See how the HiveMQ platform is being used for production-line performance monitoring - and how it is transforming how manufacturers work with industrial data - in our webinar, Introducing the HiveMQ Platform for Trusted Intelligence and Governed Action.
Try the platform today