What Innovomind is
Innovomind is an industrial software company focused on turning recurring manufacturing problems into focused operational systems. The work is centered on practical plant-floor issues: machine data, production losses, cycle time, piece counts, inspections, maintenance context, shift handover, and the decisions teams need to make while production is still running.
Industrial background
Innovomind is led by an industrial software and automation profile with more than 25 years of experience supporting manufacturing environments. That background includes acquiring data from PLCs and equipment, storing and transforming plant-floor data, building operational reports, developing process optimization logic, supporting industrial applications, and maintaining the infrastructure those systems depend on.
This matters because industrial software is not only a web interface problem. Useful plant systems depend on how data is captured, how reliable the source is, how operators and supervisors use the information, and whether the result improves a real operating decision.
Where we are strongest
- PLC and equipment data acquisition
- Industrial data pipelines, ETL, replication, and SQL Server databases
- Process optimization logic and custom operational algorithms
- Production, maintenance, inspection, and performance reporting
- Troubleshooting and maintaining industrial applications built in-house or by third parties
- Industrial infrastructure support across servers, virtual machines, domain services, and plant-floor networks
Current R&D: Sensemation
Innovomind is also developing Sensemation, a data-acquisition core intended to make PLC and equipment signals available through webhooks and web APIs so they can be used by automation workflows and downstream applications.
The practical direction is simple: start with useful machine signals, expose them in a controlled way, and let that data support alerts, logging, tracking, persistence, and operational decision logic.
How we approach industrial systems
- Start from the operating problem before designing the screen.
- Use software only where it improves visibility, ownership, or decision speed.
- Keep systems focused enough to be used by production and maintenance teams.
- Build around the data that can actually be captured from machines, people, and existing systems.
- Avoid generic dashboards that show metrics without helping teams decide what to do next.