APEX runs the plant.People approve.
APEX (Autonomous Plant Engineering Expert) is WI.Plat’s autonomous AI-agent platform for water facilities. APEX Operator runs the plant — planning pumps and valves 24 hours ahead against level, flow and tariff. APEX Engineer designs each site’s control from its own drawings and tags. Both run above the existing SCADA, inside the utility’s network.
Two modules, one platform
Engineer designs the control. Operator runs it.
One module produces the site-specific control design; the other executes it every day. What Operator learns flows back to Engineer for the next site.
APEX EngineerAI agent for control design
Reads the plant’s P&ID, HMI, tags, SCADA and documents; asks the operator what it cannot find; writes the autonomous-control design — state, action, constraints, objective — and generates the design package for people to review and approve.
- P&ID · HMI · tag understanding
- Constraint elicitation
- Design package generation
APEX Operatorautonomous plant & pump-station operation · ex-WAS-AI
Forecasts demand and reservoir levels, optimises the pump-and-valve schedule against the hourly tariff with model predictive control, recommends or — once approved — executes the setpoints, and verifies the saving against the baseline.
- Demand & level forecasting
- MPC optimisation
- Recommend → approved control
- Savings M&V
The problem
Moving water is one of the largest electricity bills a utility pays.
Water and wastewater take about 4% of the world’s electricity, most of it in pumps. Which units run, at what speed, in which hour decides a large part of the operating budget.
Of global electricity demand is used by water and wastewater services.
Of a water utility’s electricity typically goes into pumping.
Electricity can reach up to 40% of a water utility’s operating cost.
Operation depends on the operator on shift
Fixed scenarios run on personal experience. Patterns differ from person to person, and energy is wasted in the gap between them.
Tariff and demand are rarely weighed together
Deciding how much to pump in a cheap hour versus an expensive one, against tomorrow’s demand, is a calculation people cannot run every 15 minutes.
Pumps run outside their efficient combinations
The same water can be delivered by several pump combinations whose specific energy differs by a third — the choice is often habit.
Equipment faults go unheard
Legacy vibration sensors miss valve-seating faults and cavitation. The acoustic signature of a pump’s health is not used.
APEX Operator · how it runs the plant
Operator uses cheap and expensive hours strategically.
Every 15 minutes Operator reads the plant’s state, re-forecasts demand and levels, and re-optimises the next 24 hours — inside the constraints the utility has approved.
Collect
Forecast
Optimise
Recommend or execute
Verify
Fill in the cheap hours, draw down in the expensive ones
Table view
The same water, moved like this
Same water, different bill
Table view
MPC operating structure
AI demand forecast
Clear-water tank and reservoir outflow, external customer take-off — 24 hours ahead.
AI candidate reductionauxiliary
Only verified pump combinations and operating ranges enter the search — a smaller candidate set, faster solve.
Optimisation solver · MIP
- Objective
- Minimise electricity cost or specific energy over the horizon
- Decision
- Target inflow per plant and reservoir · pump combination · VFD frequency · valve opening
- Constraints
- Clear-water and reservoir levels · pressure · equipment conditions
EPANET hydraulic check
Supply flow, pressure and head computed for the plan; hydraulic feasibility verified, corrections returned to the solver.
SCADA execution
Target inflow, pump combination, frequency and valve opening applied through the existing control layer.
APEX Operator · MPC simulation case
Same three days, same water — 15% lower energy charge on paper.
One supply system, three days, re-planned by MPC every ten minutes against the operator’s actual record — same demand, same power model, same tariff. Simulation only; not yet a verified saving.
| Indicator | Operator · actual | APEX MPC | Difference |
|---|---|---|---|
| Electricity consumption | 38,951.6 kWh | 33,767.9 kWh | −13.3% |
| TYPE1 energy charge | KRW 4,569,264 | KRW 3,880,370 | −15.1% apparent |
| Pump running hours · 3 units | 125.97 h | 104.83 h | −21.1 h |
| Pump starts · 3 units | 6 | 21 | +15 |
| Level-bound violations · 10-min points | — | 19 | FAIL · operating bounds |
Service reservoir level — operator vs MPC
Table view
Cumulative TYPE1 energy charge
Table view
Sustained in routine operation across 8 systems, 2024.
Automated alternation spreads the load across units.
Tighter level control against the daily target curve.
APEX Engineer · how the next plant gets designed
An AI agent that designs APEX Operator for each site.
We taught the machine the process itself — how an expert reads a site, asks what is missing, and writes the control design.
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Understand plant material
P&ID, HMI screens, tag lists, SCADA configuration and operating documents.
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Ask what it does not know
Asks the operator for the missing constraint or rule instead of inventing one.
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Design the autonomous control
State, action space, hard constraints and objective for each hydraulic system.
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Generate the design package
The Operator configuration for the site — people only review and approve.
People design · APEX learns
The expert’s own design process becomes training data.
APEX drafts · people review
Repeatable parts are reused; only site-specific constraints are asked.
APEX designs · people approve
The design package is generated; human effort moves to review and approval.
Common platform · security & autonomy
Inside the closed network — an on-premise agent.
Both modules run on one agentic AI platform — language model, agent workflow, knowledge graph and document store, all on-premise. In public procurement, that security is the barrier to entry.
On-premise sLLM
Answers operator questions, explains why a schedule was chosen and writes reports — without external cloud inference.
Closed network
Agent workflow
Classifies the request, calls the right skills and models, queries the data stores and composes the answer.
LangGraph
Knowledge graph
Four knowledge maps of the plant — facility, network, AI model and multimodal — so reasoning follows causality, not just correlation.
Neo4j
Multimodal RAG
Manuals, drawings and maintenance photos retrieved live to ground every answer in the site’s own documents.
Qdrant
IoT sensors
Acoustic and pressure sensing on the network and in the plant, feeding the same platform as SCADA.
Sensor network
AI models
Supervised and unsupervised models for forecasting and anomaly detection; MPC for optimal control.
CNN · AE · DQN · MPC
Tags, flows, levels, pressures and energy remain on the utility’s own infrastructure.
OPC UA/DA, Modbus TCP or API to the existing PLC/SCADA. A pump disabled for maintenance drops out of the optimisation.
The agent supports the operator with reasons — forecast, tariff, constraint. It does not issue commands on its own.
From proof to contract
Verified at one plant, selected for fourteen.
Reference WTP · 8 systems
APEX Operator (then WAS-AI) deployed and verified in daily operation.
Design basis established
Transition from Q-table reinforcement learning to model predictive control.
Wastewater plant proof of concept
Extension from water supply to wastewater treatment.
National water agency · 14 plants
APEX Operator awarded by Korea’s national water agency.
Deployment
From diagnosis to control, one gate at a time.
Each stage needs the utility’s written acceptance; a utility can stay in advisory mode as long as it wants.
Site assessment & data audit
Baseline definition
Design & integration
Shadow-mode validation
Advisory operation
Approved closed loop & M&V
Adoption
Three ways to bring APEX in.
From a conventional implementation to a contract paid only from verified savings.
APEX, installed
Paid from verified savings
Water AI-platform integration
Find out what your pumps could save.
Twelve months of electricity bills and a tag list are enough for a preliminary energy assessment.
