ARKHEIA

Manufacturing operational intelligence

Your machines already know what's wrong. Now your data can prove it.

A governed AI layer over your plant-floor data — it surfaces the scrap, changeover and fleet opportunities a plant manager actually wants, scores how far each answer can be trusted, and only acts on the data that has earned it. Sits on top of the MES and historians you already run.

Reading 1.1M cycles 13 machines 8 months synthetic dataset

Look what it pulls out of your machine data

Operational value, surfaced from the cycles you're already recording.

No new sensors, no new line. The agent reads eight months of cycle data across the fleet and hands a plant manager the three questions they'd ask first — where the scrap is, where the changeover time is hiding, and which machines are quietly outperforming the rest.

◆Scrap intelligence
units / yr
Estimated annual scrap, concentrated in two reasons — not spread evenly as the shift reports suggest.
Material flaw41%
Tool wear30%
Setup / first-off15%
Other14%
■Changeover opportunity
avg / change
Median changeover runs long on three of the presses. SMED on the worst two recovers an estimated 6 hrs of run-time a week.
each changeover, last 9peaks = SMED targets
▲Fleet benchmarking
spread
Best and worst press differ by 19% on effective cycle time — same part, same tooling. The gap is a playbook, not a mystery.
PR-07100%lead
PR-0392%
PR-1186%
PR-0981%lag

Synthetic dataset (1.1M cycles, 13 machines, 8 months), scripted for the web. Figures are illustrative — no live model is called and nothing leaves your browser.

The turn — can you trust the data?

Not every number on your floor deserves the same trust.

The insight above is only as good as the data underneath it. Some of it is sensor truth — the machine counted it. Some of it is a human typing a reason code at the end of a long shift. Most tools treat both the same. This one scores each source, and the gap changes the answer.

1Data-trust score, by source
Cycle time sensor
Counted by the machine on every shot — complete, consistent, no gaps.
trust0%
Shot / cycle count sensor
Machine-derived tally — the ground truth for throughput and tool wear.
trust0%
Operator downtime reason human-entered
Typed at shift-end. 34% blank, defaults to “material flaw,” rarely revisited.
trust0%
Scrap reason label human-entered
Free-text + dropdown, inconsistent across shifts — biased toward the easy code.
trust0%
2The decision this changes

“Press PR-09 has the most scrap. The operator logs say it's material flaw. Should we switch material supplier?”

▲ If it trusts the operator label
Yes — switch supplier. Chase the material.
Leans on the human-entered reason code (trust 41%). Triggers a costly supplier change… that fixes nothing.
would have been wrong
◆ Weighted by data trust
No — it's tool wear on PR-09.
Down-weights the label; leans on sensor truth instead. Scrap spikes track the shot-count curve between tool changes — not the supplier. Fix: tighten the tool-change interval.
right call

Same data, two answers. The operator label sent you after the supplier; the sensor truth pointed at the tool. AI insight is only trustworthy when it knows which inputs to trust — and most tools never tell you.

The reveal — governed decisions

It acts on trustworthy data. Anything leaning on low-trust data is held for a human.

Knowing which data to trust is only half of it. The agent's recommendations are gated by that trust score: a clean, sensor-grounded call can execute; a call that depends on low-trust human labels is held for review — the fail-safe. Every governed decision, acted or held, leaves a tamper-evident receipt.

1Recommendations, gated by data trust
Tighten tool-change interval on PR-09 to cut scrap ~18%.
data trust 97% ✓ Auto-applied — grounded in sensor shot-count + cycle time
Schedule SMED workshop on the two long-changeover presses.
data trust 95% ✓ Auto-applied — changeover timings are machine-derived
Switch material supplier based on logged “material flaw” scrap.
data trust 41% ▲ Held for review — leans on low-trust operator labels
2Detection & the receipt
Trust signal: LOW risk on the auto-applied calls — grounded in sensor data, inside baseline. The supplier-switch was flagged and held, never executed.
Decision receipt — PR-09 tool interval✓ verified
actoragent:plant-intel
actionapply tool-change interval
evidencesensor: shot-count + cycle time
data-trust0.97 (sensor-derived)
authoritypolicy:scrap-autonomous
sha2567e1c…a04d
Decision receipt — supplier switch▲ held
actoragent:plant-intel
actionhold + escalate to human
evidencehuman label, trust below floor
data-trust0.41 (operator-entered)
authoritypolicy:procurement-supervised
sha256b0f5…9c3e
Illustrative values.

Scheduled analyses · ask the data

The questions you ask every week become saved, governed analyses.

Once a question earns its place, it runs on a schedule — trust-scored and receipted each time, surfacing the answer where the work is. The one-off questions still go straight to the data.

◆
Where is scrap concentrating this week, weighted by data trust?
Every Monday 06:00 · trust-weighted · receipted
Scheduledlast run: 2d ago
■
Which presses are drifting on changeover vs. the fleet leader?
Daily 22:00 · sensor-grounded · receipted
Scheduledlast run: 14h ago
▲
Flag any recommendation that leans on low-trust labels.
Continuous · holds for human review · receipted
Scheduledlast run: live
Ask any question of your plant data — in plain language — and save the good ones as scheduled analyses. (Live in the product; canned here for the web.)

Why you can act on it

The capability is what you saw — scrap, changeover and fleet intelligence pulled straight from your cycles. The data-trust scoring and the governance are why you can act on it: it knows which numbers to believe, it only moves on the ones that earned it, and it proves every decision.

◆Insight first

Reads the cycles you already record and surfaces the operational value — scrap concentration, changeover loss, fleet spread — without a single new sensor.

▲Trust scored

Separates sensor truth from human-entered labels and scores each source, so a low-trust reason code can't quietly steer a costly decision.

✓Governed & proven

Acts only on trustworthy data, holds the rest for a human, and leaves a tamper-evident receipt on every governed decision — acted or held.