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HOME / AI OPERATIONS
MANIFESTO

AI that reads the same data your MES writes.

A model can only reason over what your systems actually record. So we build the MES data first: timestamped events, lot context, and master data that matches across sites. Then the answers land on the operator's screen, where the shift is running.

LIVE DEMO

Ask it something you'd ask a shift supervisor.

Apollo is our MES query assistant. It reads the TrakSYS or AquiWeb tables your plant already writes to, so you can ask "why did Line 3 lose 40 minutes yesterday?" and get the stop reasons and the loss tree back. The console below runs on simulated packaging-line data.

01 · WHAT AI-FIRST MEANS HERE

What we actually build under the word "AI".

01
Continuous monitoring
Loss events land in the MES the moment a machine stops, so drift shows up inside the shift that caused it.
02
Issue prioritization
The loss tree ranks stop reasons by minutes lost, so the morning meeting opens on the top three causes instead of the whole list.
03
Insight extraction
Every finding carries its order, lot and equipment ID, so a supervisor can open the record behind the number.
04
Root-cause analysis
Attributes a defect across machine, material, method and operator using the genealogy the MES already stores.
05
Decision support
Recommendations appear in the operator's task list in TrakSYS or AquiWeb, at the step where the decision gets made.
02 · WHY WE START WITH THE DATA

The MES has to be working before the model is worth anything.

Most operations AI pilots fail on the data, and the model is rarely the problem. A single stop gets recorded three times: once in the ERP, once in the MES, once in the SCADA historian, each with a different idea of when it started. Nothing reconciles.

So we fix that first: ISA-95 L1 to L4 integration, master data governed centrally, and every event stamped once against an order and a lot. Only then does the model become worth building.

The unsexy truth: 70% of the value of "AI in operations" comes from the operational data backbone. We build that first.
03 · WHERE EACH CAPABILITY STANDS TODAY

A maturing capability stack.

Predictive maintenance, vision-based quality, autonomous scheduling and generative root-cause sit at very different stages. What you can run depends on how many of your machines are connected, how far your event history goes back, and whether your master data matches across sites.

Every engagement opens by sorting your plant into the four rows below. You get that sorting in writing before anyone signs anything.

  • Production todayOEE intelligence, loss attribution, contextualized analytics, decision support workflows.
  • In pilotPredictive quality and generative root-cause. We scope these against your event history and say so when it's too short.
  • Case by casePredictive maintenance at fleet scale, autonomous scheduling, vision inspection. These need sensor coverage and failure history most plants don't have yet.
  • Needed either wayA working MES, governed master data, and machines connected over OPC UA or MQTT. We do this at every site.

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.