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

Logistics · Bulk Liquid Terminals

Catch suspect transfer losses before the next operation hides the cause

IO follows active and recent bulk-liquid transfers, compares the operational evidence, and guides terminal teams toward the most likely cause before the discrepancy becomes an end-of-day reconciliation problem.

Relevant for bulk liquid terminals where small transfer inconsistencies can become unexplained losses, manual investigations, or commercial disputes once the batch is closed.

Minutes-hoursoperational window to detect and act
0.1-0.3%typical unexplained-loss range
4 hypothesesfailure hypotheses evaluated
EUR 250k-1.5Millustrative annual exposure

SEO slug: industrial-ai-agents-logistics-bulk-liquid-terminals-transfer-loss-detection

Use-case summary: Follow active and recent transfers, compare tank, flow, route and reporting evidence, and guide teams to the most likely cause before reconciliation becomes a dispute.

Use case context

The problem is not only the loss. It is losing the moment when the cause was still visible.

Bulk liquid terminals move product across tanks, pumps, lines, meters, drains, valves and loading points. A small inconsistency can stay invisible during the transfer and only appear later as an unexplained stock gap.

The cause is rarely obvious from one signal. A similar discrepancy may come from a small leak, meter drift, a drain or valve left in the wrong configuration, or inconsistent reporting between systems.

The real IO mission is not to explain the loss after everyone has moved on. It is to follow the active or recent transfer, detect when the evidence starts to diverge, and identify the operational gate before the next order, tank switch, route change or document closure hides the cause.

IO models the reasoning of terminal operators and custody-transfer specialists: what they compare, which hypotheses they eliminate, which local checks they request, and when they document or escalate the case.

Concrete trigger

A transfer is still active, or has just closed, and expected tank movement, flow totals, valve states, pump activity, batch events and reporting data no longer tell the same story. IO flags the suspect pattern and proposes the next operational check before reconciliation turns it into a dispute.

Pain points

What the terminal loses when transfer discrepancies are explained too late

The cost is not only the missing product. Late explanation creates manual investigation work, weak evidence, repeated failures and commercial friction around custody-transfer accuracy.

Unexplained product loss

  • Small deviations accumulate into meaningful stock gaps.
  • The original operating context is harder to recover later.
  • Loss patterns may repeat before the cause is isolated.

Slow root-cause clarity

  • Teams rebuild transfer history manually.
  • Operations, inventory and finance may read the same gap differently.
  • Corrective action is delayed until the explanation is credible.

Custody-transfer disputes

  • Evidence is weaker once the batch is closed.
  • Meter drift and reporting gaps look like physical loss.
  • Carriers, customers or internal teams challenge the figures.

Repeated operational patterns

  • A drain, valve or route configuration may be repeatedly mis-set.
  • A meter or asset may generate recurring discrepancies.
  • The pattern is missed when each case is investigated separately.

How IO reasons

IO models the terminal expert who compares the transfer story across systems

This mission is not a reconciliation dashboard. IO evaluates competing operational hypotheses and recommends the next useful check, hold, verification or evidence step.

Builds the expected transfer story

Compares planned batch movement, source and destination tanks, route, pump activity, meter totals, valve states and timing.

Detects divergence early

Looks for emerging mismatch between tank movement, flow evidence, batch events and reporting data while checks can still be made.

Evaluates failure hypotheses

Treats leak, meter drift, mis-set hardware and reporting inconsistency as hypotheses to test, not as separate promises.

Recommends the next lever

Suggests inspection, pause, hold, reroute, meter verification, evidence documentation or escalation based on the most likely explanation.

IO governance

The user decides how much authority IO has

IO proposes the next useful check in supervised mode, and can later gate the next operation under approved discrepancy policies.

Supervised mode: IO proposes, the terminal team confirms

  • IO spots a suspect transfer pattern before reconciliation closes the case.
  • IO shows which hypothesis best fits the tank, flow, route and reporting evidence.
  • Operations or inventory control confirm local conditions and inspect the proposed point.
  • The team holds, verifies, documents, reroutes or escalates with a clear reason.

This is the natural starting point when transfer decisions affect safety, custody transfer, product ownership or customer commitments.

Delegated mode: IO gates the next operation under approved policies

  • Alert during the suspect transfer, not only after reconciliation.
  • Set a tank, transfer, or route segment to exception status until verification is completed.
  • Hold downstream orders such as the next pipe transfer, truck unloading, tank switch, or release step when the policy allows it.
Evidence and procedure levers
  • Open an approved discrepancy procedure with the likely hypothesis attached.
  • Trigger mandatory field verification of a meter, drain, valve lineup, route segment, or tank level.
  • Prepare the evidence package for custody-transfer review.
  • Keep higher-risk actions supervised unless explicitly authorized by terminal policy and existing interlocks.

Expected benefits

Earlier explanation, fewer repeated losses, stronger reconciliation evidence

Expected value depends on transfer volume, product value, current unexplained-loss rate, available signals and how quickly terminal teams can act on suspect patterns.

Lower unexplained loss

Detect transfer inconsistencies earlier and reduce repeated physical or operational losses.

Faster investigations

Start with the most likely operational hypothesis instead of rebuilding every transfer from scratch.

Better custody-transfer evidence

Capture the relevant tank, flow, route and reporting evidence while it is still available and fresh.

More focused corrective action

Escalate repeated patterns on specific meters, routes, valves, drains, products or operating states.

Discuss this case

Could a suspect transfer be buried by the next operation before your team can prove what happened?

The first step is to frame the mission: which transfers matter, which signals are available, which hypotheses experts already evaluate, and which actions should remain supervised or become delegated under policy.

  • Which tank, meter, valve, pump, drain, route and batch-event signals are already available?
  • Where do unexplained losses repeatedly appear after transfer closure?
  • Which expert checks determine whether the cause is physical, instrumental, operational or reporting-related?
  • Which actions can IO recommend, prepare or trigger under approved discrepancy procedures?
  • Which loss, investigation-time or dispute-reduction target would justify the first mission?

Want to map this to your terminal transfers and discrepancy procedures?

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