Capability 05 · AI Capability Integration

What AI can and can't do in industry
We only do the part that can be verified

Industrial sites aren't short on AI concepts — they're short on reasons to let it touch production logic. Our principle is plain: AI only makes suggestions, never issues commands — flagged defects are confirmed by a person, classified alarms are handled by a person, and analytical conclusions must be checkable against data. That way, when it's wrong, nothing stops; it just gets ignored.

Talk about a specific scenarioStart with program refactoring →
¥8,000+ (≈US$1,100+)Single-scenario pilot, results in 2–4 weeks
Human reviewEvery AI output passes through a person
ReversibleNot wired into the control loop; can stop anytime
Pilot before rolloutProve one point before discussing the whole line

Three scenarios that already work today

Each outputs a human-reviewable list or recommendation — none change control logic.

AI review of program defects

A rule base (uninitialized variables, double-coil writes, missing interlocks, timer reuse, E-stop logic depending on software, etc.) does a first pass, then a model does semantic re-checking on suspicious sections, producing a defect list ranked by severity, each with location, cause, and suggested fix. The program isn't touched until a person confirms.

Intelligent alarm-message processing

Re-categorize historical alarms as "symptom / cause / action," merge synonymous alarms, identify false and cascading alarms, and generate draft handling suggestions with multilingual output. After rollout, alarm volume typically drops 30–50% — not by deleting alarms, but by removing duplicates and noise.

Equipment data analysis

Use historical data for downtime attribution and parameter-drift early warning: which signal went abnormal N minutes before a fault, which parameter combinations correlate with high defect rates. The output is a "list of leads worth a look" — not automatic adjustment.

Four things we don't do, stated up front: no "AI auto-generates PLC code straight onto the line"; no unattended automatic program changes; no large models wired into the control loop; no training general models on customer data. These four aren't caution — they're basic requirements for an industrial site. A place with a 10-millisecond scan cycle has no room for reasoning that's only "roughly right."

How a pilot runs (2–4 weeks)

  1. Pick one point. Choose a recurring problem with historical records (e.g., false alarms at a station, intermittent downtime on a machine). Don't start with "plant-wide intelligence."
  2. Provide data. Program blocks, alarm history, and any needed trend data; anonymized and sent to us, or we run remotely on your machines.
  3. Build a baseline. First use rules and manual work to label a batch of "correct answers" as the yardstick — without a baseline there's no way to prove the AI helps.
  4. Run a round, review it together. Output a list and go through it item by item with your engineers: how many hits, how many false positives, what was missed.
  5. Decide whether to roll out. If the pilot results are poor, we stop — and the cost stops at the pilot. That's written into the contract.

Pricing (excl. tax)

Pilots are priced per scenario; rollout is priced by equipment count or annual fee, quoted separately.

ScenarioScopeReference price
Program defect review (single machine)≤3,000 lines, defect list + human review¥8,000–15,000 (≈US$1,100–2,100)
Alarm-message processing≤2,000 historical alarms, incl. multilingual output¥15,000+ (≈US$2,100+)
Equipment data analysis pilotSingle machine / line, incl. baseline labeling and validation¥30,000+ (≈US$4,200+)
Annual (incl. model maintenance)Continuous ingestion of new data, quarterly review¥60,000+/year (≈US$8,500+)
Why us for this: AI teams usually can't get industrial data and don't know why alarm messages are written the way they are; automation teams have the data but not the engineering ability to tune a model into something usable. We have both sides in one room, so a pilot compresses to two or three weeks instead of starting with a six-month framework negotiation.

Bring a real problem to try

Tell us a problem that keeps recurring in your plant and has never been truly solved (false alarms, intermittent downtime, parameter drift). We'll first judge whether AI is useful for it — and we'll tell you straight if it isn't.

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