Manufacturing IT Services and Software

Usually, though the method varies more than in most sectors. Modern MES and ERP platforms expose APIs. Machines typically speak OPC UA or Modbus, sometimes a proprietary protocol, and older equipment may expose nothing beyond a serial output. We establish what each system and machine actually provides during discovery rather than assuming, because manufacturing integration is where estimates go wrong most often.

Both, usually. Anything that must keep running when the internet drops — machine control, operator interfaces, line-side data capture — stays local or at the edge. Reporting, analytics, planning and integration sit in the cloud comfortably. The architecture that fails is one that puts a real-time dependency across an internet link, because a plant does not stop production for a network outage.

It depends entirely on what the machine tells you. Modern equipment with vibration, temperature and current sensors gives models something to work with. Older machines may only expose run hours and fault codes, in which case prediction becomes closer to scheduled maintenance with better timing. Retrofitting sensors is often worth it on critical assets and rarely worth it on everything. We are candid about which of your assets fall into which category before building.

Sometimes. Existing cameras are often positioned for security rather than inspection, and lighting is usually the limiting factor rather than resolution. Consistent, controlled lighting matters more than an expensive sensor. We assess the current setup during discovery and will tell you where a camera and a light cost less than the model does.

The useful applications are documentation and knowledge, not control. Drafting SOPs and work instructions, retrieving answers from decades of manuals, maintenance logs and engineering notes, and summarising shift handovers. That last point matters commercially — a great deal of process knowledge is held by people approaching retirement and written down nowhere searchable. Generative AI does not belong anywhere near machine control or safety systems, and we will not build it there.

For a defined scope we target a working MVP in 30 days and a core production build in 90. Manufacturing extends when machine integration, validation in regulated production or a multi-site rollout are involved. We pilot on one line or one site before extending, because what works on one machine rarely works unchanged on twelve.