Industry workflows · Agents propose, humans approve

Watch the platform work

AI agents do the work. A named human approves every gate. Nothing writes to your ERP, MES or PLM on its own.

How to read each step AI agent — does the work Human gate — a named approver Artifact produced Elapsed time
Eight workflows in a ring around one agent core — select a node to jump to that workflow Equipment installation — jump to workflow 01 Equipment servicing — jump to workflow 02 Engineering change — jump to workflow 03 Fab maintenance — jump to workflow 04 Yield improvement — jump to workflow 05 Supplier onboarding — jump to workflow 06 Spare parts management — jump to workflow 07 Root-cause investigation — jump to workflow 08

eight workflows · one agent loop · click a node to jump

Workflow 01 · Equipment AI

Equipment installation

One live thread, order to sign-off — held to the ready-for-equipment date.

Before / after — equipment installation

Cost of the same error, by where it is found
NASA error-cost curve — why the thread starts at the order, not at hook-up
36,000+OEM spec pages, hand-transcribed $200–500khook-up per tool · $240–600M per fab ~1,200tools from 100+ vendors per fab FAT→SATdeltas surface at the curve’s worst point
Specs → dataon arrival · stale revisions blocked −60–80%manual takeoff on install docs $12–16Mone avoided slip week (est.) On clockimports held to the fab schedule
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Workflow 02 · Equipment AI

Equipment servicing

Ticket to cited remote fix — before the truck rolls.

Before / after — equipment servicing

Benchmarks: McKinsey gen-AI service cases; leaders vs typical OEM as cited in program data
First-dispatch accuracy
Typical OEM today vs leaders — the gap remote-first triage closes
Troubleshooting time
<1 min
was 30 minutes — McKinsey gen-AI service benchmark
First-contact resolution
0
McKinsey benchmark · billed per verified resolution
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Workflow 03 · Engineering AI

Engineering change (PCN / ECN)

Every notice lands scored, owned and LTB-sized — the day it arrives.

Before / after — engineering change

The race against the last-time-buy window
A 90–180-day window vs same-day impact analysis and LTB sizing
Inboxthe notice waits while the window closes By handBOMs · quals · WIP · customers Daysper impact analysis 1,000sof PCN/EOL notices per year
Same dayscored · owner-assigned · PR at the gate Days → minutesimpact analysis 0last-time-buy windows missed → ~0quality escapes from unmanaged change
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Workflow 04 · Manufacturing AI

Fab maintenance

The failure meets a calendar, not a siren — one human signs once.

Before / after — fab maintenance

From the predict-to-planned-swap relay; McKinsey predictive-maintenance benchmarks
Tool-down duration — the same failure, two ways
Unplanned 2 a.m. failure vs a planned swap inside the PM window
Unplanned downtime
−30–50%
McKinsey predictive-maintenance benchmark · machine life +20–40%
Downtime stakes
$1–10M+/hr
leading-edge fab downtime (est.) — a $5M–$380M tool idled per stockout
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Workflow 05 · Manufacturing AI

Yield improvement

Cross-stage root cause in hours, not nine days of archaeology.

Patterned wafer on an inspection stage under cleanroom light
Fig. 04 — the lot in question edge-ring signature · named at T+30 min

Before / after — yield improvement

Time to root cause on a cross-stage excursion
~9 days of MES ↔ FDC ↔ STDF archaeology vs the graph doing the join once
>$550Mscrapped in one 2019 excursion quarter (SemiAnalysis) $100k–$10M+per excursion Daysto author an automotive-grade 8D
1 jointhe graph does the lineage once Days → hourstime-to-root-cause Days → hours8D / PPAP authoring 100%claims evidence-cited
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Workflow 06 · Supply Chain AI

Supplier onboarding

The second source, qualified before the allocation window closes.

Before / after — supplier onboarding

Typical program result on 6–18-month JEDEC/AEC-Q qualification cycles
Supplier qualification cycle
Industry norm (upper band) vs AI-assisted target at −50%; typical result −30–50%
Qualification cycles
−30–50%
typical program result — every gate chased with evidence
ABF supply gap
10–20%
2026, effectively a sole film source — the risk this workflow retires
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Workflow 07 · Equipment AI

Spare parts management

$50–300M of spares, sized by the install base — not by averages.

Before / after — spare parts management

Program dashboard data; McKinsey aircraft-OEM anchor case
Aftermarket spares turns
ERP min/max era vs fleet-driven planning (measured program week)
Inventory freed
10–20%
typical result at equal-or-better SLA (est.) — anchor case −30%, ~$700M EBIT (McKinsey)
When a part is missing
6× longer downs
McKinsey — spares carry 30–50% gross margin vs 15–25% on new tools
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Workflow 08 · Engineering AI

Root-cause investigation

Ask the fleet anything — answers with receipts, the fix pre-drafted.

Before / after — root-cause investigation

Time to a cited answer
15+ minutes of searching per question vs a page-cited answer in seconds
42%of knowledge unique to one person (Panopto) ~19%of an engineer’s day lost to search (McKinsey) 15+ minper search, per engineer Weeksof senior time per fix package
15+ min → secondsper search, page-cited Fix ECOdrafted with retrofit economics 100%of answers cite or abstain
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