Industrial Operator by Cleverdist
Product
  • Protect Dispatch Window
  • Recover Line Speed
  • Catch Transfer Losses
  • Keep Cranes Moving
  • Prevent HVAC Recovery
  • Clear Weak Assets
PricingQ&AAbout
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Industrial Operator

Autonomous AI for industrial operations.

Supervised or Autonomous
On top of existing systems
Built-in governance

IO in the real world

References

Supporting multi-plant combined-cycle operations with IO
Naturgy logo

Naturgy + IO

Supporting multi-plant combined-cycle operations with IO

Centralized operations across combined-cycle power plants, with IO reasoning above existing plant systems.

11combined-cycle sites
17gas turbine units
10-25%hidden capacity identified
+5-15%throughput gain potential
€1.3Min avoided investment
€700k-€1Mannual value potential
10M+I/O parameters
50+More than 50-country collaboration
AllMultilingual shifter support
70%up to 70% fewer expert escalations

Deployed in real industrial environments — not demos. Built on 10+ years of mission-critical automation expertise.

Swiss-tech
Industrial-grade engineering
Vendor-agnostic

Differentiation

We model thinking,
not tasks.

Others chain AI agents in workflows. IO captures how your experts actually reason. That's why it scales where others don't.

Others: Linear Workflow
IO: Industrial Reasoning
STEP 01STEP 02STEP 03
Read our technical approach (PDF)

Governance & Accountability

Your pace. Your policies.

Governance that scales with confidence. Some teams need human-in-the-loop today. Others are ready for delegated execution. IO supports both, with explicit policies, full audit trails, and the flexibility to evolve at your pace.

IO proposal queue — human confirms or rejects each recommendation before execution

Human in the loop

AI thinks. You decide.

Full visibility at all times. IO surfaces recommendations — every action requires a human to approve before anything happens.

Governance policy editor — browse hierarchy and set scoped policies for delegated execution

Delegated Execution

AI acts within your rules.

Delegation is explicit, scoped, and reversible. You define what IO may or may not do — and responsibility always remains human-owned.

  • AI cannot decide or act
  • Every action remains human-validated
  • Full audit trail for regulators
  • Delegation is explicit, scoped, reversible
  • Your rules define what AI may or may not do
  • Responsibility remains human-owned

Architecture

The journey with us is simple.

We model your landscape.

Messy is fine. Our onboarding tools create the context (ontology) AI needs. We work directly with you or with your trusted integrators.

Seamless integration across your ecosystem

SCADA / DCS
Historians
MES
ERP
EAM / CMMS
APM
Quality / LIMS
Planning / APS
Documents
APIs

Examples include

SiemensWinCC OAIgnitionAVEVAABB 800xADeltaVHoneywell ExperionYokogawa CENTUM VPFactoryTalkGE ProficyPI SystemSAPIBM MaximoServiceNow...and more
Download IO Secure Architecture (PDF)

Ready?

Start your pilot.

One mission. Clear success metric. Governed rollout.

Book an intro
IOby Cleverdist

Autonomous AI that operates within your governance, at any scale.

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IO Use Cases

Energy

Protect Dispatch Window

Manufacturing

Recover Line Speed

Logistics

Catch Transfer LossesKeep Cranes Moving

Mobility

Prevent HVAC RecoveryClear Weak Assets

Mobility · Stations & Transport Hubs

Anticipate thermal load before comfort becomes expensive to recover

IO follows station activity, passenger flow, weather, air-quality signals and HVAC response to adjust earlier, reduce recovery peaks, and keep comfort stable without overcooling or overventilating.

Relevant for enclosed stations and transport hubs where comfort, ventilation and energy use depend on fast-changing passenger flows, external weather and thermal inertia.

15-60 minanticipation window before peak load
10-20%target HVAC energy reduction
30-60%HVAC share in complex stations
EUR 50k-300killustrative annual saving

SEO slug: industrial-ai-agents-mobility-stations-transport-hubs-thermal-load-hvac-optimization

Use-case summary: Anticipate station thermal load from passenger flow, weather, air quality and HVAC response so comfort stays stable with less recovery energy.

Use case context

The problem is not temperature. It is reacting after the station has already started to drift.

Complex stations must maintain comfort and air quality while passenger density, train arrivals, outdoor temperature, humidity and zone usage keep changing.

The HVAC system may react correctly according to its local control rules, but still too late. By the time temperature, humidity or air quality moves outside the ideal range, the system needs stronger corrective action.

That late correction creates energy peaks, overcooling, unstable comfort and more manual intervention from operators.

The real IO mission is to follow the station context before the deviation becomes visible: passenger load building up, weather changing, specific zones heating faster than usual, ventilation demand increasing, or equipment response becoming weaker than expected.

IO models the reasoning of station operations and HVAC specialists: when they pre-adjust, which zones they prioritise, which comfort limits they protect, when they avoid aggressive cooling, and when they escalate a system that is not responding normally.

Concrete trigger

A concourse, platform or underground zone is still within comfort limits, but passenger flow, weather, air-quality trend and HVAC response suggest that the zone will soon require aggressive recovery. IO flags the risk early and proposes the next operational adjustment before comfort drifts.

Pain points

What the station loses when HVAC reacts after the load has arrived

The cost is not only energy. Late HVAC response creates unstable comfort, unnecessary peak demand, avoidable manual tuning and poor visibility on why the same zones keep becoming difficult to control.

Energy waste

  • HVAC reacts after thermal load has already built up.
  • Recovery requires stronger cooling or ventilation.
  • Overcooling is used as a safety margin against discomfort.

Comfort instability

  • Temperature and air-quality conditions oscillate during peak periods.
  • Some zones recover too slowly while others are overcorrected.
  • Passenger experience becomes harder to manage consistently.

Peak demand and equipment stress

  • Late response pushes systems into high-intensity operation.
  • Equipment is staged aggressively instead of gradually.
  • Energy peaks appear during predictable passenger or weather events.

Repeated operating patterns

  • The same zones may drift during similar traffic or weather conditions.
  • Operators compensate manually without capturing the pattern.
  • Root causes remain unclear: load, control strategy, ventilation balance or equipment response.

How IO reasons

IO models the station expert who sees the thermal load before the BMS has to fight it

This mission is not a generic energy dashboard. IO evaluates the station context, predicts where comfort will become expensive to recover, and recommends the next useful pre-adjustment, verification or operating limit.

Builds the expected station load

Compares passenger flow, train schedule, zone occupancy, weather, air-quality signals, historical response and HVAC equipment state.

Detects approaching drift

Looks for early mismatch between expected comfort, actual sensor trends and the effort required from cooling or ventilation systems.

Evaluates operating hypotheses

Treats crowd load, weather shift, ventilation imbalance, control setting, equipment weakness and sensor issue as hypotheses to test.

Recommends the next lever

Suggests setpoint bias, pre-cooling, pre-ventilation, fan staging, zone prioritisation, demand limitation, equipment verification or escalation.

IO governance

The user decides how much authority IO has

IO can start as a supervised advisor for comfort and energy decisions, then apply bounded HVAC adjustments only inside approved comfort, air-quality, safety and energy policies.

Supervised mode: IO proposes, station operations confirm

  • IO spots a zone or station area likely to drift before comfort or air-quality limits are reached.
  • IO shows why the risk is building: passenger load, weather, ventilation trend, equipment response or control behaviour.
  • Operators confirm local constraints, comfort priorities, event context and any operational limitations.
  • The team adjusts setpoints, ventilation balance, staging, zone priority or operating mode with a clear reason.

This is the natural starting point when comfort, passenger experience, energy use and operational policies must be balanced carefully.

Delegated mode: IO adjusts within approved comfort and energy policies

  • Apply small setpoint shifts within approved comfort bands.
  • Start pre-cooling or pre-ventilation before predicted passenger peaks.
  • Stage fans, air-handling units or cooling capacity gradually instead of waiting for recovery mode.
  • Rebalance cooling or ventilation between zones when one area is likely to drift.
Energy and guardrail levers
  • Limit unnecessary peak HVAC intensity when comfort and air quality remain protected.
  • Return zones to normal profiles after the peak has passed.
  • Trigger mandatory verification when equipment response does not match expected behaviour.
  • Escalate to operators when comfort, air quality or safety limits may be at risk.
  • Keep emergency modes and safety-critical overrides outside IO authority unless explicitly authorised by policy and existing control-system protections.

Expected benefits

Stable comfort with less recovery energy

Expected value depends on station size, HVAC energy share, passenger variability, available signals, current control strategy and how much authority IO is allowed to exercise.

Lower HVAC energy use

Reduce overcooling, late recovery actions and unnecessary high-intensity operation.

More stable passenger comfort

Anticipate load changes earlier and avoid temperature or air-quality oscillations.

Lower peak demand

Stage HVAC response progressively before the station enters aggressive recovery mode.

Better operational visibility

Show why specific zones repeatedly become difficult to control and whether the cause is load, control strategy, ventilation balance or equipment response.

Discuss this case

Could your station act before HVAC enters recovery mode?

The first step is to frame the mission: which zones matter, which signals are available, which comfort and air-quality limits must be protected, and which HVAC actions should remain supervised or become delegated under policy.

  • Which station zones create the highest comfort or energy pressure?
  • Which passenger, weather, train schedule, sensor, HVAC and energy signals are already available?
  • Where does the system repeatedly overcool, overventilate or recover too late?
  • Which expert checks determine whether the cause is load, control logic, ventilation balance or equipment response?
  • Which adjustments can IO recommend, prepare or apply within approved operating bands?
  • Which kWh, peak-demand, comfort-stability or intervention-reduction target would justify the first mission?

Want to map this to your station zones, signals and HVAC policies?

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