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
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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 WindowConfirm True ReadinessOrchestrate Flexibility

Manufacturing

Recover Line SpeedCatch Process DriftRecover Hidden Capacity

Logistics

Catch Transfer LossesKeep Cranes Moving

Mobility

Prevent HVAC RecoveryClear Weak Assets

Energy · Utilities

Know if the operation is truly ready, not just compliant

IO reasons across asset condition, field work, staffing and operations to show how individually-acceptable issues combine into a fragile operating state before a critical period begins.

Relevant for utilities where many assets, teams and dependencies must align before peak periods, storm response or planned-outage return, and where a complete checklist does not prove the operation is actually ready.

2-4hearlier readiness-risk detection
30-60%fewer late operational discoveries
Cross-systemassets, field work, staffing
EUR 500k-2Millustrative annual incident-avoidance value

Use case context

Everything is marked ready, but the operation is not

Before critical periods, utilities rely on checklists across assets, maintenance, staffing and operations. On paper everything is complete.

Real readiness depends on how those elements interact under load. A degraded transformer, an unresolved field task and thin crew availability can each look acceptable on their own, yet combine into a real operational risk.

The problem is not missing information. It is the absence of reasoning across that information while there is still time to act.

IO models the expert who asks whether the operation is truly ready, connecting weak signals across systems into a readiness-risk picture and prioritising what must be fixed first.

Concrete trigger

A peak-demand period, storm response or planned-outage return is about to start. All readiness checks are green, but a combination of minor asset, field-work and staffing issues creates a fragile operating state. IO surfaces the combined risk and the priority actions before the window opens.

Pain points

What it costs when readiness is judged from completion instead of diagnosis

The cost is not a missing checklist item. It is entering a critical period with combined risk that no single owner saw, then diagnosing it live under pressure.

Hidden fragility before operations

  • Checklists show completion but hide combined risk.
  • Teams work from fragmented views of readiness.
  • Weak signals are dismissed because each one looks isolated.

Late diagnosis under pressure

  • Operations start with vulnerabilities still buried.
  • Time is lost diagnosing issues during the critical period itself.
  • Corrective actions land too late or on the wrong priority.

Readiness effort spent on the wrong priorities

  • Teams clear what is administratively open, not what most threatens the window.
  • High-impact dependencies wait behind low-impact paperwork.
  • The same fragile combinations reappear period after period.

How IO reasons

IO models the expert who asks whether the operation is truly ready, not just complete

This mission is not a readiness checklist. IO interprets how signals combine, judges what threatens the operating period, and recommends the action that most improves true readiness.

Reads beyond checklists

Treats completion status as one signal among many, not as proof that the operation is operationally ready.

Connects weak signals

Combines small issues across assets, staffing, field work and operating state into meaningful readiness-risk scenarios.

Prioritises the readiness-critical fix

Identifies what must be resolved now to secure the period, rather than what merely remains open administratively.

Tracks readiness as the window approaches

Monitors how the diagnosed risk evolves up to the deadline and whether corrective actions actually close it.

IO governance

The user decides how much authority IO has

IO can start as a supervised readiness advisor, then enforce the approved follow-through under policy while the go/no-go readiness call stays with operations leadership.

Supervised mode: IO frames readiness risk, operations leadership decides the gate

  • Detect: IO identifies readiness gaps that could become operationally significant in the next critical window.
  • Frame: IO explains how individual issues combine into a fragile or unsafe operating state.
  • Validate: operations teams confirm whether the diagnosis matches live field reality and constraints.
  • Act: approved corrective actions, escalations or operating constraints are launched before the period begins.

This is the natural starting point because readiness is fundamentally a judgment problem, and the final go/no-go stays human-approved.

Delegated mode: IO enforces the readiness gate under approved policies

  • Raise a readiness alert when combined signals indicate fragility.
  • Set a site, feeder or asset to readiness-exception until the diagnosed risk is resolved.
  • Block a clean all-ready status while unresolved combined risk exists.
  • Auto-escalate as the window deadline approaches without resolution.
Governed follow-through
  • Open escalation or work-order workflows with the supporting evidence attached.
  • Route each priority action to the right asset, field or staffing owner.
  • Track whether corrective actions actually resolve the diagnosed risk.
  • Keep the final go/no-go readiness decision human-approved unless explicitly authorised by policy.

Expected benefits

Enter critical periods with fewer hidden blockers and more credible readiness decisions

Expected value depends on how many asset, staffing and field-work dependencies must align, how often hidden fragility appears late, and how much follow-through authority IO is allowed to exercise.

Fewer incident-driven escalations

Combined risks are resolved before the window instead of during it, reducing live failures and emergency callouts.

Readiness effort spent where it matters

Priority actions target the risks that most threaten the operating period, not the longest list of open items.

Defensible readiness decisions

Go/no-go is backed by cross-system diagnosis and an evidence trail, not by checklist completion percentage.

Discuss this case

Are you ready, or just compliant?

The first step is to frame the mission: which readiness signals exist, where hidden fragility has appeared too late, and which readiness decisions must stay explicitly management-approved.

  • Which asset, maintenance, field-work and staffing signals are already available before critical windows?
  • Where have recent peak, outage or storm periods exposed fragility that was visible but not interpreted together?
  • Which weak-signal combinations most often turn into live operational problems?
  • Which readiness actions can IO recommend, prepare or trigger under approved procedures?
  • Which go/no-go readiness decisions must remain explicitly management-approved?
  • Which incident, downtime or response-time target would justify the first mission?

Want to map this to your readiness reviews across assets, field work and staffing?

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