Industrial Operator by CleverdistIndustrial Operator by Cleverdist
Product
  • Protect Dispatch Window
  • Confirm True Readiness
  • Orchestrate Flexibility
  • Recover Line Speed
  • Catch Process Drift
  • Recover Hidden Capacity
  • Catch Transfer Losses
  • Keep Cranes Moving
  • Prevent HVAC Recovery
  • Clear Weak Assets
PricingQ&AAbout
info@cleverdist.com

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 WindowConfirm True ReadinessOrchestrate Flexibility

Manufacturing

Recover Line SpeedCatch Process DriftRecover Hidden Capacity

Logistics

Catch Transfer LossesKeep Cranes Moving

Mobility

Prevent HVAC RecoveryClear Weak Assets

Food & Beverage · Packaging Lines

Catch process drift before defects, waste, or quality holds appear

IO reasons across process conditions, material behavior and quality signals to identify risky trajectories before defects, scrap, rework or quality holds become visible.

Relevant for packaging-material production where seal quality, thickness variation, print quality, tension drift and waste are driven by interacting process conditions that can remain nominal individually until defects appear.

2-6hearly warning before visible defect
10-30%scrap and rework reduction
5-15%quality-loss reduction
EUR 200k-800killustrative annual value per site

Use case context

The defect becomes visible only after the process has already been drifting

Packaging-material operations can remain inside broad control limits while the true optimal process window is already moving away from quality stability.

Seal quality, thickness variation, print quality, tension drift and waste may appear as separate defect families even when they originate from the same process trajectory.

By the time inspection confirms the defect, meaningful output may already require rework, downgrade, quality hold or scrap.

The value is to detect and explain the risky trajectory early enough for process teams to correct it before product defects become the first visible signal.

Concrete trigger

A line remains within control limits, but the combination of temperature drift, line speed adjustment, material behavior and web tension suggests output quality is moving out of its optimal window.

Pain points

What this costs when drift is only recognized after quality has moved

The problem is not a single alarm. It is a process trajectory that becomes risky before quality inspection, scrap reporting or customer-sensitive holds make the issue obvious.

Visible quality losses

  • Scrap and rework accumulate before the root process condition is identified.
  • Quality holds or downgrades are triggered after output has already been produced.
  • Different defect families are tracked separately even when they share a common process-drift origin.

Hidden process instability

  • Operators stay in a degraded but alarm-free operating window too long.
  • Frequent small parameter changes create instability when the real drift mechanism is not understood.
  • Improvement teams lack a clear explanation of which combinations of speed, material, temperature and tension matter most.

Correction windows close late

  • The line keeps producing while the best stabilization moment passes.
  • Process teams react to the visible defect instead of the trajectory that caused it.
  • Evidence arrives after output has already moved toward rework, downgrade or scrap.

How IO reasons

IO models the process expert who sees the bad trajectory before the product shows it

This mission is not defect counting. IO reasons across process movement, material context and quality outcomes to detect when individually acceptable signals combine into a risky trajectory.

Detects subtle trajectory change

IO tracks combinations of parameter movement that stay nominal individually but become risky together over time.

Links drift to defect families

IO reasons about how the same process trajectory may lead to seal, thickness, print, tension or waste problems depending on material and operating context.

Explains which signal combination matters

IO separates normal variation from risky combinations of speed, temperature, tension, material and inspection signals, so teams know what to correct first.

Identifies the correction window

IO highlights when an early adjustment is likely to stabilize the process before deviation becomes visible in product quality.

IO governance

The user decides how much authority IO has

Process drift detection can start with supervised interpretation, then move toward governed follow-through only where product, process and compliance boundaries are explicit.

Default mode: IO frames drift risk, process owners validate the adjustment

  • Detect: IO spots drift patterns likely to affect quality if left uncorrected.
  • Explain: IO shows which parameter combination and material context make the current trajectory risky.
  • Validate: process and quality teams confirm whether the interpretation matches line reality and product sensitivity.
  • Act: approved setpoint, speed or material-handling adjustments are made before deviation spreads.

Because quality risk is product- and process-specific, human validation should remain central even when drift detection becomes highly automated.

Delegated mode: IO supports approved quality-protection follow-through

  • Trigger early drift alerts before product deviation appears.
  • Recommend which parameter family should be adjusted first.
  • Escalate lines or batches approaching a quality-hold condition.
  • Prepare evidence for whether output needs closer inspection.
Governed follow-through
  • Track whether corrective adjustments actually stabilize the process.
  • Support approved closed-loop adjustments only under tightly governed conditions.
  • Keep customer-risk, release, hold and compliance-impacting actions explicitly approved.
  • Escalate whenever the situation leaves the authorized product, material or process boundary.

Expected benefits

Fewer defects, less waste and faster process stabilization

Expected value depends on line volume, defect cost, inspection delay, material sensitivity and how early risky trajectories can be explained and corrected.

Lower scrap and rework

Reduce quality losses by correcting drift before defects accumulate.

Faster process stabilization

Stabilize the process earlier instead of waiting for inspection results, scrap reports or quality holds to confirm the problem.

More targeted interventions

Guide process teams toward the right adjustment instead of broad reactive tuning.

Discuss this case

Could the defects you track separately come from the same drifting process conditions?

This mission is relevant when teams can diagnose drift after the fact, but cannot continuously detect and explain dangerous trajectories early enough across all lines and shifts.

  • Which process and output-quality signals are already linked today?
  • Where does deviation appear late relative to the process change that caused it?
  • Which defect families may share the same process-drift origin?
  • Which speed, material, temperature and tension combinations matter most?
  • Which quality-protective actions must remain explicitly approved?
  • Which scrap, rework or quality-loss target would justify the first mission?

Want to map the drift patterns behind your packaging-quality losses?

info@cleverdist.comLinkedIn