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MANIFESTO

Not dashboards.
Decisions.

AI in operations is only as good as the operational data backbone underneath it. We start with architecture and integration — then make AI useful at the line, not the boardroom.

LIVE DEMO

See it answer a real question.

Apollo reads your MES backbone in real time. Ask an operational question and it answers against live line data — with the chart and the root cause, not another dashboard to interpret.

01 · WHAT AI-FIRST MEANS HERE

Five practices, not five products.

01
Continuous monitoring
Operational signals — not weekly reports. The line tells you when something's drifting before the shift ends.
02
Issue prioritization
Faster triage on the loss reasons that matter. Operators spend their attention where it pays back.
03
Insight extraction
Automated, contextualized, and shopfloor-readable. Findings tied back to master data — not orphaned charts.
04
Root-cause analysis
From symptom to source across machine, material, method, and operator dimensions — without spreadsheet archaeology.
05
Decision support
Recommendations operators can act on in their flow — surfaced where work happens, not on a dashboard somebody opens once a quarter.
02 · THE BACKBONE PRINCIPLE

AI sits on top of MES. Not the other way around.

Most smart manufacturing and Industry 4.0 AI pilots fail at the data layer. The model is fine. The data is fragmented, untimestamped, missing context, or rebuilt three times across the ERP, MES, and SCADA without a shared definition of "an event."

We start with architecture and integration. A clean L1→L4 stack with master-data alignment, lot-level context, and timestamped events. Then AI becomes useful — and operational excellence through digital transformation becomes measurable, not aspirational.

The unsexy truth: 70% of the value of "AI in operations" comes from the operational data backbone. We build that first.
03 · HONESTY CLAUSE

A maturing capability stack.

We won't oversell. Smart manufacturing AI is delivering real gains — but predictive maintenance, computer vision quality, autonomous scheduling, and generative root-cause are at different levels of maturity, and readiness depends on your data backbone, asset criticality, and operating model.

Every engagement starts with a client-specific assessment: what's ready today, what's emerging, what requires foundational work first. We tell you which is which.

  • Production todayOEE intelligence, loss attribution, contextualized analytics, decision support workflows.
  • MaturingPredictive quality, generative root-cause, AI-assisted operator support.
  • Emerging / case-by-casePredictive maintenance at scale, autonomous scheduling, vision-based inspection.
  • Required regardlessClean MES, master-data alignment, integration discipline.

Want an honest read on AI readiness in your operation?

A 30-minute diagnostic on the operational data backbone you have today, and what AI capabilities it can support tomorrow.