MFG.01
Predictive Maintenance
Failure prediction from sensor histories, and the alarm-fatigue problem that decides whether anyone acts on it.
Open pageIndustry
Manufacturing AI
the ERP cannot see.
A person commits each order. Maintenance, inspection, planning and reconciliation for industrial operations — the system reads telemetry and correspondence, prepares the work order or the purchase order, and a person commits it.
In context
Maintenance, inspection, planning and reconciliation for industrial operations — the system reads telemetry and correspondence, prepares the work order or the purchase order, and a person commits it.
Where it breaks today
01
The chain runs on prose
Supplier emails, order confirmations, delay notices and shipping documents carry commitments and changes that never reach the planning system, because they arrived as text rather than as data.
02
Failures found late
A delayed shipment or a degrading machine is usually visible in the data well before anyone acts on it — and the cost of an unplanned stoppage arrives after the damage is done.
03
Master data that drifted
Lead times, yields and capacity were entered years ago. Plans built on them are wrong before any solver touches them, and the optimiser takes the blame.
OUR APPROACH
Reducing supply chain fragility
Where language models add capability in an industrial setting is the unstructured half of the operation: the correspondence, the shipping documents, the maintenance notes, the invoice that does not match the purchase order. We extract those into structured signals and feed them to the systems that already run the plant — the ERP, the maintenance system, the optimiser — rather than replacing any of them.
The boundary is consistent. The system predicts a failure window and raises a ranked work order; a technician decides. It reads a delay notice and proposes rerouting or reallocation; a planner confirms. It prepares a purchase order when a threshold is crossed; a buyer commits it. An agent placing orders unsupervised converts a data error into a financial one, and we do not build that.
HOW IT DEPLOYS
PHASE 01
Instrument the chain
Sensor telemetry, logistics feeds, supplier correspondence and ERP data are unified into a single operational picture, with master data checked against observed reality first.
PHASE 02
Wire the exceptions
Extraction and monitoring go live across procurement, maintenance and invoice reconciliation — wired into SAP, Oracle and your ticketing stack, with every exception typed and routed.
PHASE 03
Propose, then measure
Delays are detected and their consequences propagated; a planner confirms the response, a technician takes the work order, a buyer commits the PO. Whether downtime falls is measured on your outcome log.
What we build
01
Supply Chain Signals
AI data analysts that read logistics feeds, supplier correspondence and public risk sources, connect them to the parts and lines they affect, and propose rerouting or reallocation for a planner to confirm.
SEE SUPPLY CHAIN PLANNING →02
Procurement Preparation
Internal Ops agents that monitor inventory levels, prepare purchase orders when thresholds are crossed, handle routine supplier correspondence and write structured results back into SAP or Oracle. The conversation and the commitment stay with your buyers.
SEE INTERNAL OPS AGENTS →03
Predictive Maintenance
Cross-platform workflows that link sensor telemetry to your maintenance system, raising a ranked work order with the evidence attached ahead of a predicted failure window.
SEE PREDICTIVE MAINTENANCE →04
Invoice Reconciliation
Low-touch financial automation that extracts line items from Bills of Lading and freight invoices and reconciles them against the original POs, with discrepancies routed to a person rather than discarded.
SEE FINANCIAL AUTOMATION →Controls we implement
GDPR-ALIGNED CONTROLS
SEGMENTED FROM OT NETWORKS
READ-ONLY BY DEFAULT
ON-PREMISE INFERENCE
AUDIT LOG ON EVERY ACTION
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and the audits and attestations your sector requires remain yours to run — see our trust centre.
Manufacturing use cases
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MFG.02
Quality Inspection
Vision models on the line, where lighting, fixturing and a rare defect class matter more than the architecture.
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MFG.03
Supply Chain Planning
Planning under disruption, supplier risk signals, and why the optimiser is rarely the missing piece.
Open page
Questions
Questions about Manufacturing.
How does AI detect supply chain problems before they cascade?
By reading what the planning system cannot. Order confirmations, delay notices, shipping documents and public risk sources are parsed into structured events and cross-referenced against your shipment schedules and bill of materials. When a delay becomes probable, the system propagates the consequences — which lines, which orders, what the options are — and proposes rerouting or reallocation for a planner to confirm. Detecting that something has happened and propagating it quickly is achievable; predicting the next unprecedented shock is not, and we do not claim it.
Can AI agents handle procurement in SAP or Oracle?
Agents monitor inventory levels, prepare purchase orders when thresholds are crossed, handle routine supplier correspondence and write structured results back into SAP or Oracle. A person still commits the order — an agent placing orders unsupervised converts a data error into a financial one, which is not a trade we would recommend.
What is predictive maintenance orchestration?
It is the automated link between machine health and human action. Sensor telemetry connects to your maintenance system, so a degrading vibration, temperature or throughput signal becomes a ranked work order with the evidence attached — a predicted window, not a moment. Whether it reduces downtime is measured on your assets and your outcome log, not asserted in advance.
How does AI reconcile freight invoices against purchase orders?
Multimodal extraction reads line items from Bills of Lading and freight invoices — across formats and carriers — and reconciles them against the original POs. Clean matches flow through to your ERP; discrepancies are typed and routed to a person for review, never discarded.
Check the data
before the solver.
Start with an architecture review of your maintenance, planning or reconciliation workflow — and the master data underneath it.
30-minute technical call · no deck
