AI for manufacturing
AI for manufacturing that knows your factory
Most AI tools you can buy today are general-purpose models bolted onto a chat box. They guess. Digel is different. It learns the specific shape of your plant: the sensors, the equipment, the work orders, the documentation, and the things your operators just know. It uses that context to answer questions, watch for trouble, and bring you findings you would not have asked for.
Why generic AI does not work in a factory
A general-purpose model has read the internet. It has not read your P&IDs, your alarm history, or the note your most experienced operator left at 3am about Pump 4. When you ask it about your line, it will write something plausible. Plausible is the worst answer in a plant.
An AI becomes useful in industry the moment it stops guessing. That requires three things you cannot get from a chat box alone: a structured picture of your equipment and how it connects, a live feed of what that equipment is doing right now, and access to the documents and decisions that explain why things are the way they are.
Digel builds that picture for you. The result is an AI that answers in the language of your specific plant, not in stock phrases that sound like every other industrial software demo.
What AI for manufacturing actually looks like in production
We have spent a year running pilots with Norwegian manufacturers across food, mattresses, doors, and poultry. Six concrete things AI does that earn its keep on a real plant floor:
Continuous monitoring
The agent watches every connected signal for the patterns your senior operator would notice.
Issue triage
A finished investigation is attached to every flagged item, written in plain language, with one click to act.
Conversational dashboards
Build a dashboard by asking for it. No clicks, no chart configuration, no ticket to BI.
Root cause analysis
Walk back through alarms, telemetry, and maintenance history together, in one investigation.
Document search
Get the procedure you need, not a folder of PDFs to dig through.
Automated shift reports
Generated from the data and published automatically, so the next shift reads the same record.
Every one of these is in production today with paying customers. None of them depend on a "big red automate button". The agent proposes, the human decides.
How it connects to your SCADA, MES, ERP, and CMMS
You probably already have an OT stack. Digel connects to it. Sensors and alarms come in from SCADA or historians. Work orders, parts, and procedures come in from your existing CMMS, ERP, or MES. Documentation flows in from wherever it lives.
Nothing is replaced by force. Digel can sit on top of your existing stack and add a reasoning layer, or it can replace the systems your team actively avoids. Most of our customers do both, in pieces, over time.
The goal is not to add another tool. The goal is to give your AI access to the data it needs to be useful, in a structure that lets it reason across all of it at once. That structure is what we call the industrial context graph, and it is what makes Digel grounded instead of guessing.
What changes for operators, maintenance, and plant managers
For operators
Fewer dead ends. Ask in plain language and get a real answer with the data attached. New people on the team get the same answers as the most experienced operator on the previous shift.
For maintenance
A queue of pre-investigated issues with the relevant work orders, sensor history, and prior repairs already pulled in. Less time chasing context, more time on the wrench.
For plant managers
A real picture of what is happening across the plant, drawn from data instead of from one person's gut feel. Reports published automatically, so the team reads the same record.
Dig deeper into specific use cases
This page is the overarching picture. The use-case pages below go into depth on the shapes of AI for manufacturing customers reach for most:
AI maintenance management →
AI CMMS with triage, condition-based scheduling, mobile, and an agent that reasons across maintenance history.
Root cause analysis →
Walk from symptom to cause across telemetry, alarms, and maintenance history in one investigation.
Operator onboarding and tribal knowledge →
Move the 2am phone call knowledge out of the heads and into a graph every shift can share.
Automated reporting →
Shift reports and production summaries drafted from the graph, with action points that open as issues.
Pilot and deployment
The fastest way to find out whether this works for your plant is to run a pilot. Two weeks, your own data. We come on-site for a day, model your processes in Digel, and you spend the rest of the time exploring it with your team.
After the pilot, you have three deployment options. Managed cloud, your private cloud, or fully on-premise with a local model for air-gapped environments. See the plans or read more in Your data, your infrastructure, your call.