S-01 · DARK FACTORY CONSULTING — MANUFACTURING AI TRANSFORMATION

Equipment, processes, spreadsheets — all in their own silos.Ever struggled to tell which data is the real one?

We bring scattered plant and company-wide data into one manufacturing AI OS. Anyone can find the latest information at once, and we build systems where AI delivers real results on standardized data. A factory that isn't standardized can't become autonomous — the journey to a dark factory starts here.

FIELD 200+ SITES / 16 YRS · SOLUTIONS 20 · AI AUTONOMOUS FACTORY ×2 · AI→PLC DIRECT CONTROL VERIFIED

01PROBLEMSCATTERED DATA — HARD TO FIND · USE · PROTECT

Scattered data is hard to find,
hard to use, hard to protect.

Plant data is scattered across equipment, MES, ERP, spreadsheets and chat — finding it, using it and protecting it all become bottlenecks.

P-01

Hard to tell which data is current

People burn time hunting for the truly latest value across equipment logs, daily production sheets and spreadsheet copies.

P-02

Hard to put data to work in AI

Without standardization and integration, you just repeat PoCs — adopting AI never turns into results.

P-03

Hard to control access

With no clear permissions by role and rank, sensitive information like drawings, cost and quality risks being exposed.

P-04

Hard to know where to start

The need for AI is clear, but it's unclear what to standardize first and what to automate first.

02WHAT IT ISDIAGNOSE · DESIGN · BUILD

Here's what Dark Factory Consulting does.

WACE's manufacturing-AI engineers go on-site, study your data structure, diagnose and design what to standardize and integrate so AI can be put to work, and own the build. Smart Factory Build and the Autonomous Factory both start from this same diagnosis — the consulting output flows straight into system build, AI agents and autonomy. We centralize scattered data into one manufacturing AI OS and build systems that deliver results on the floor from day one.

Scattered data centralizing into a single core

FIG — SCATTERED → CENTRALIZED

SOL-01

Scattered data onto one standard

We find data managed separately by equipment, department and system, unify it in one place, and standardize and cleanse it so AI can use it right away. The whole company works from the same source, the same standard.

BASESCATTERED → CENTRALIZED · 834+ STANDARD TABLES · 311+ ONTOLOGY RELATIONS

SOL-02

Standardized data into real results

We connect it straight to proven AI use cases. Data made on the floor accumulates as a company asset, and running staff training and one-on-one facilitation alongside speeds up adoption.

USE CASESPREDICTIVE MAINTENANCE · ANOMALY DETECTION · QUALITY PREDICTION · REPORT AUTOMATION

SOL-03

Gathering it isn't the same as securing it

We clean up duplicate and non-standard data and centrally control access by role and rank, so anyone can find the latest data fast and use it safely.

PATH01 DATA INTEGRATION → 02 STANDARDS & PERMISSIONS → COMPANY-WIDE USE

03IN ACTIONREAL PRODUCT SCREENS

After adoption, this is how
the factory works.

Once data governance is in place, this is how the way the factory works changes. Below are real VEXPLOR screens.

D-01 · Manufacturing

Automated predictive maintenance

AI catches early signs of equipment trouble in real time and alerts the right person the moment risk crosses a threshold.

STACK — PREDICTIVE MAINTENANCE LSTM · ANOMALY DETECTION · THRESHOLD ALERTS

D-02 · Manufacturing

Automated root-cause analysis for quality

AI cross-checks process data to flag defect patterns early and presents cause and response with the evidence attached.

STACK — 4-WAY RAG · ONTOLOGY EVIDENCE CHECK

D-03 · Manufacturing

AI → direct equipment control

It syncs digital-twin predictions with the live floor and pushes AI decisions down to the PLC to control equipment directly.

STACK — 3D TWIN · AI→PLC DIRECT CONTROL VERIFIED

D-04 · Manufacturing

Automated floor and production reporting

AI automatically compiles daily and weekly output, utilization and inventory, adds a summary, and sends it at a set time.

STACK — SHOP-FLOOR POP · AUTO-COMPILED REPORTS

D-05 · COMPANY-WIDE EXPANSION — ROADMAP

Automated document and contract review

On upload, it auto-checks against standard clauses and flags risk items.

D-06 · COMPANY-WIDE EXPANSION — ROADMAP

Automated inquiry and review triage

AI sorts incoming inquiries and reviews by type and owning team and summarizes them.

Screen data is shown for illustration · Company-wide expansion (D-05·06) is marked roadmap — not presented as complete

04WHO IT'S FOR4 PROFILES

It fits companies like these.

T-01

Manufacturing AX transformation

Manufacturers moving beyond the smart factory to a stage where AI runs the plant.

T-02

Manufacturing data governance

Companies looking to unify data scattered across equipment, MES, ERP and spreadsheets.

T-03

Real AI results

Companies that want proven AI to deliver results instead of repeating PoCs.

T-04

Company-wide expansion

Companies extending data centralization and automation beyond production to other departments (roadmap track).

05PROCESSENGINEERS ON-SITE · 5 STEPS

A standard process,
run by engineers themselves.

Kickoff

We align on goals, scope and success metrics (KPIs) before we begin.

Data & floor diagnosis

We map equipment, process and system data and pick out what's usable for AI.

Use-case design

We design what to standardize, integrate and use — and how — tailored to your company.

Load, integrate, build

We load cleansed data into the AI system and connect it to equipment and operational workflows.

Closeout report

We compile the diagnosis, design and execution roadmap into a final report.

DIAGNOSE DATA → STANDARDIZE [ 834+ STANDARD TABLES · 311+ ONTOLOGY RELATIONS ] → INTEGRATE → AI USE CASES [ PREDICTIVE MAINTENANCE · ANOMALY DETECTION · QUALITY PREDICTION · REPORT AUTOMATION ]FIG — DATA-TO-AI PATH · STAFFING: 1–3 WACE MANUFACTURING-AI ENGINEERS ON-SITE · OPTIONAL STAFF TRAINING · 1:1 FACILITATION

06ESTIMATE3-AXIS · NO LIST PRICE

Here's how we price it.

SCOPE

What's included

Data and floor diagnosis · AI use-case design · build and integration · closeout report.

TEAM1–3 WACE MANUFACTURING-AI ENGINEERS · TIMELINE SCOPED TO YOU

FORMULA

Three pricing axes

Data scale and integration breadth / scope of use-case design (number of AI use cases) / build and integration depth.

3-AXISDATA SCALE · USE CASES · DEPTH

PACKAGE

Bundled discount

We scope an exact quote after a meeting, and bundling consulting with training earns a discount.

OPTIONGROUP-TRAINING BUNDLE DISCOUNT · 1:1 FACILITATION EXTRA DISCOUNT — GROWS AS YOU BUNDLE

Pricing page — see the full basis →

07WHY WACEDOMAIN DEPTH × STANDARDS × PRODUCT

There's a reason it has to be WACE.

W-01

Manufacturing depth, proven

16 years and 200+ manufacturing-site engagements. AI→PLC direct control verified, two AI autonomous factories built — territory a generalist AX vendor struggles to enter.

PROOF200+ SITES / 16 YRS · AI→PLC VERIFIED · AUTONOMOUS FACTORY ×2 SITES

W-02

Built on national standards and patents

We lead the consortium developing Korea's manufacturing-data standardization reference model (AAS/IEC 63278), hold 1 registered and 2 pending manufacturing-AI patents, and run a corporate R&D lab with ISO 9001.

IPPAT REG 1 + PENDING 2 · IEC 63278 · R&D LAB · ISO 9001

W-03

Product-backed consulting

We consult on top of a Manufacturing AI OS with 20 industrial solutions, 834+ standard tables and a manufacturing-tuned 4-Way RAG, so diagnosis flows straight into build and operation.

BASESOLUTIONS 20 · TABLES 834+ · 4-WAY RAG

CASE-15%Defect rate — Company H (die casting)

CASE99%Inventory accuracy — Company K (automotive electronics)

EXPECTED-90%Data processing time

EXPECTED-15%Process operating cost