2 at-risk orders
- DEMO-S-001 Venue directory signD-1
- DEMO-G-001 Main stage signD-2
- Month-to-date on-time delivery96.4% · target 95%
2 purchase proposals
- Stainless square pipe 30mm 40EA10/22 urgent
- SMPS 24V 400W 12EA10/20
1 root cause found
- Heating element share of last month's claims44%
- Concentrated in LOT-2410-B731%
- High-risk customers to visit first98
Tomorrow's route: 5 stops
- Load603 / 900kg
- Customer time windowsAll 5 met
- Avg. route distance with VRP30.0 → 25.5km
Answered an employee question
How many days of annual leave do I get?
15 days if you work at least 80% of the year. From 3 years of service, 1 day is added every 2 years, up to 25 days.
Rules of Employment Article 14
3 signals to review today
- Preventive A/S rate13% · target 20%
- VRP route distance−15%
- Labor hours96h · target 74h
Let’s Work Wonders!
A day in a factory
working with agents
WondersLab connects ERP, spreadsheets, and call records, and builds AI agents that take on the work in each area of the plant. Scroll to follow the day from 7 a.m. to 6 p.m.: what the agents did, and what people decided.
Click a building to jump to that time.
07:30Shipping dock · Order & Delivery Agent
Before work starts,
delivery risks arrive first
Overnight, the agent checks ERP orders against production and shipping status, then sends a morning briefing on orders likely to run late, and why.
Sales decides whether to adjust customer schedules
2 at-risk orders
Actual screen (opens in a new window)- DEMO-S-001 Venue directory signD-1
- DEMO-G-001 Main stage signD-2
- Month-to-date on-time delivery96.4% · target 95%
09:10Materials warehouse · Materials & Purchasing Agent
Proposes orders
before materials run short
Reviews stock, supplier lead times, and the production schedule, then proposes what to order, when, and how much.
Purchasing approves the order
2 purchase proposals
Actual screen (opens in a new window)- Stainless square pipe 30mm 40EA10/22 urgent
- SMPS 24V 400W 12EA10/20
10:40After-sales center · After-Sales & Predictive Maintenance Agent
Finds out why
the same failures recur
Links failure history with sales and LOT data to find the root cause, then decides which customers to visit first and which technician to send.
The after-sales manager confirms assignments
1 root cause found
Actual screen (opens in a new window)- Heating element share of last month's claims44%
- Concentrated in LOT-2410-B731%
- High-risk customers to visit first98
13:20Delivery center · Delivery Optimization Agent
Plans tomorrow's routes
around promised times
Calculates a visit order that respects load limits, customers' requested times, and assigned zones, then sends it to the driver app once confirmed.
The delivery manager confirms the route
Tomorrow's route: 5 stops
Actual screen (opens in a new window)- Load603 / 900kg
- Customer time windowsAll 5 met
- Avg. route distance with VRP30.0 → 25.5km
15:00Office building · Internal Knowledge Agent
Answers policy questions
with source clauses
Answers employees' questions on policies and work standards with source clauses attached, and adds unanswered questions to the document update list.
The responsible department fills in missing documents
Answered an employee question
Actual screen (opens in a new window)How many days of annual leave do I get?
15 days if you work at least 80% of the year. From 3 years of service, 1 day is added every 2 years, up to 25 days.
Rules of Employment Article 14
18:00Control room · Management Insight Agent
Turns the day's signals
into business language
Compares KPIs across customer service, after-sales, inventory, delivery, and sales, and reports only the signals executives need to see.
Executives choose which improvements to pursue
3 signals to review today
Actual screen (opens in a new window)- Preventive A/S rate13% · target 20%
- VRP route distance−15%
- Labor hours96h · target 74h
Six agents shared the day,
and people made every decision
Your plant's data is connected in one place, and each agent repeats its job in its own area. Let's decide together which agent your plant should start with.
Review by agent
See what data each of the six agents reads and who receives its results. Pick an agent to see what it did that day and its live system screen.
Connected data
- ERP orders & POs
- Production & shipping status
- Materials & suppliers
- Call recordings & VoC
- A/S & LOT history
- Company rules & standards
AI agents
Who gets the results
- Sales & production
- Purchasing
- After-sales
- Delivery drivers
- All employees
- Executives
07:30Shipping dock
What it did today
Overnight, the agent checks ERP orders against production and shipping status, then sends a morning briefing on orders likely to run late, and why.
Sales decides whether to adjust customer schedules
Data it reads
- ERP orders
- Production & shipping status
- Unloading schedule
What it produces
- Delivery risk list
- AI daily briefing
Live screen
AI forecast briefingOpen demo ↗Start with
a single agent
Start with the one task that takes the most time, confirm the impact, then add agents on top of the same data.
Custom-built
We analyze your work and requirements, then plan and build an AI work system designed for your plant.
Data and system integration, built around your company's own work standards
Productized
We adapt agent products with a proven structure to your plant's data and workflows.
4–8 weeks
On-premise
Source data and sensitive information stay on your internal network, and we propose an AI infrastructure that fits your security environment.
Only de-identified data goes to external models
- 01
Work & data diagnosis
Map repetitive tasks, decision criteria, and where data lives.
- 02
Agent design
Separate what agents handle from where people approve.
- 03
Build & on-site rollout
Build on real data and validate with on-site staff.
- 04
Operate & expand
Improve monthly from usage logs and expand to adjacent work.
Let’s Work Wonders!
See it working
in your own plant
Explore live system screens on our demo site, or answer three questions to find out where to start.
You can also email us first. wyou@wonderslab.kr