DataGOL for Manufacturing — the factory that sees what's coming
DataGOL's AI connects your operational systems, live machine streams and paper trail — packing slips, work orders, inspection sheets — into one intelligence layer you can simply ask. Your factory stops reacting to yesterday and starts running ahead of tomorrow.
Pillar 01 — Operational data connectivity
Is the operation running the way it should? ERP, MES, work orders and HR are joined into one live view of staffing, fill rates, SLAs and margin, so you can staff the right lines, hit fill rates, keep SLAs and see where the margin really is.
Pillar 02 — Predictive maintenance and streaming data
Is a machine about to break down? Live sensor data spots the warning signs early, so maintenance happens before the failure, not after. Stream IoT sensor data, store it in a time-series database, model it with LLMs and XGBoost, and act on a predictive forecast — 97.5% efficiency in reducing equipment downtime.
The AI layer — every document on your floor, readable by AI
Packing slips, bills of lading, certificates of analysis and inspection sheets are read by AI, which pulls out the fields that matter and links them to your ERP records. No templates to build, no manual keying.
AI agents built on DataGOL
A Scheduling Agent fills idle capacity, a Maintenance Agent opens the work order first, a Document Agent reads the paper trail, and a Quality Agent connects defects to causes — each with a person approving what matters.
Frequently asked questions
What does DataGOL do for manufacturers?
DataGOL connects a plant's operational systems — ERP, MES, work orders and HR — together with live machine sensor streams and scanned paper documents into a single AI intelligence layer. It answers two questions from one platform: is the operation running the way it should, and is a machine about to break down.
How does DataGOL's predictive maintenance work?
Vibration, temperature, load and cycle signals stream continuously off the floor into a time-series database at full resolution. Gradient-boosted XGBoost models forecast failure and LLMs explain why and what to do, so maintenance is scheduled by machine condition instead of by the calendar.
How much unplanned downtime can predictive maintenance remove?
DataGOL reports 97.5% efficiency in reducing equipment downtime with AI-driven predictive forecasting — unplanned downtime falling from about 8.4 hours per week before deployment to roughly 0.21 hours per week after.
Which systems does DataGOL connect to?
ERP (orders, inventory, cost), MES (production execution), work order systems (jobs, routing, status), HR (workforce, skills, shifts) and live IoT machine sensors. DataGOL joins and models every source so people and AI agents query the same governed, AI-ready live data.
Can DataGOL read paper documents like packing slips and certificates of analysis?
Yes. Packing slips, bills of lading, certificates of analysis and inspection sheets — scanned, photographed or emailed, including handwritten notes — are read by DataGOL's AI, which extracts the fields that matter and links them to ERP records. There are no templates to build and no manual keying.
What AI agents does DataGOL provide for the shop floor?
A Scheduling Agent that fills idle capacity around downtime, tooling and crew skills; a Maintenance Agent that turns a failure forecast into a scheduled service window with parts staged; a Document Agent that digitizes slips and certificates and flags PO mismatches; and a Quality Agent that links defects to lots, machines and shifts and drafts the CAPA. A person approves what matters.
Can I ask questions about my plant in plain English?
Yes. DataGOL turns a plain-English question into a query across operations, machine and document data and shows its sources — for example who should run a line tomorrow given absences, which orders will miss their SLA this week, what each job really earned after labor and downtime, or which machine is most likely to fail in the next two weeks.
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