AI vision inspection that adapts to your line
Detect cap, fill-level, label, and bottle defects without fighting brittle thresholds.
Built for bottling teams that need line-ready inspection, better exception review, and traceability that still holds up when lighting, speed, and SKU variation change.
Live line review
Detecting recurring closure drift on Station 02
The problem
Inspection breaks down between manual review and brittle rules
Good systems should behave like industrial tooling, not fragile demos. The challenge is keeping inspection reliable when production conditions move.
Manual inspection is inconsistent across shifts and speed changes.
Rule-based systems become brittle when bottles, labels, or lighting vary.
False rejects create waste, rework, and avoidable line friction.
Root-cause analysis is slow when image review and event history are disconnected.
SKU variation makes inspection setup and tuning harder to maintain.
Defects are sometimes caught too late to prevent larger production impact.
What better looks like
A tighter inspection workflow for real plant conditions
Industrial credibility over generic startup polish
The goal is not to look like a broad AI platform. The goal is to look like a serious inspection workflow built for the line.
Fewer buzzwords. More signal.
Use cases
High-frequency defect workflows worth validating first
Focused on categories where visual defects create recurring waste, friction, or traceability gaps.
Cap / closure inspection
Detect missing caps, skewed closures, damaged tamper bands, and closure placement issues before defective bottles move downstream.
Closure defects can lead to leaks, contamination risk, customer complaints, and unnecessary line holds when inspection is too brittle.
Fill-level inspection
Monitor underfill, overfill, and inconsistent fill presentation across products, bottles, and production conditions.
Fill-level issues affect compliance, giveaway, waste, and confidence in downstream quality checks.
Label inspection
Identify missing labels, skew, wrinkles, poor placement, and print or artwork conformity issues across SKU variations.
Label errors create compliance risk, rework, and customer-facing defects, especially on lines with frequent changeovers.
Empty bottle inspection
Detect empty bottles, missing content, and line events where containers pass inspection points without the expected product state.
Empty bottle escapes can signal upstream issues and undermine confidence in the full inspection workflow.
Bottle / glass quality inspection
Surface visible bottle and glass defects such as cracks, chips, contamination, cosmetic damage, and presentation issues.
Container defects can drive safety concerns, spoilage, rejects, and expensive investigations after production has moved on.
Cycle stability
< 50 ms
Review signal
Operator + QA
Status color
Emerald = in tolerance
Exploring AI inspection for your line?
Share your current defect categories, false reject behavior, and what still requires manual review.