Detection finds the defect. Loopr explains it.

When defect rates spike, quality engineers spend hours digging through SCADA, ERP, MES, and maintenance logs to find out what changed. Loopr's AI agent does that investigation in minutes, then hands engineers a ranked list of likely causes with the evidence behind each one.

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Total Audience

15.290

How It Works

Investigate

On a defect spike, the agent pulls together quality events, process parameters, material data, maintenance history, and your own process documents.

Reason

It cross-references what changed: process deviations, equipment behavior, material lots, supplier or recipe changes.

Recommend

Engineers get ranked probable causes, the evidence supporting each, and suggested next actions. Nothing happens without human review.

Loopr Inspect Intelligence

The problem

What Your Team Sees

When a defect rate crosses threshold, Loopr does not just raise an alarm. It ranks the probable causes against process data, maintenance records and supplier logs — and shows its work.

Every finding cites its source — process data, maintenance records, supplier logs, your uploaded SOPs and rule books.

Human-in-the-loop by default. The engineer validates before anything moves.

Illustrative example from a roofing shingle manufacturer.

Defect spike detected

Line 3 · Shift B · 14:20

Defect mode

Missing granules

4.0%

from 2.0%

Threshold 3.0%

Ranked causes

Supplier batch change

Most likely

New granule lot introduced 3 days prior; incoming moisture well above baseline. Similar event resolved in a prior investigation.

Supplier log · LOT-4471

Incoming QC

CAPA-2213

Coating temperature instability

Elevated variance versus setpoint over the same window.

Process data · PLC

Roll pressure drift

Minor deviation from setpoint, low correlation.

Maintenance record

Recommended actions

Quarantine affected batch

Retest granule lot

Adjust coating temperature

Notify procurement

Validate & dispatch

Open evidence

Awaiting engineer review

Defect assessment

Works wherever process data explains defects

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Industry

Typical defects

Causal signals the agent checks

Building materials

Surface and coating defects

Material lot properties, coating temp, line speed

Automotive paint

Orange peel, runs, contamination

Spray pressure, viscosity, oven profile, humidity

Ceramics and tile

Orange peel, runs, contamination

Kiln temp curve, clay moisture, press pressure

Chemicals and coatings

Orange peel, runs, contamination

Reaction temp, mixing time, raw material purity

Pulp and paper

Orange peel, runs, contamination

Pulp consistency, machine speed, dryer profile

Same reasoning engine, deployed against your systems: vision inspection, SCADA, MES, ERP, CMMS, and your engineering documents.

Engineers Stay

In Control

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Evidence-backed.

Every recommendation shows the data behind it.

Advisory, never autonomous.

Humans review and approve all actions.

Built for manufacturing quality.

Not a general-purpose AI adapted after the fact.

Detection finds the defect. Loopr explains it.

How It Works

Works wherever process data explains defects

Engineers Stay

In Control

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