AI-assisted quality inspection for manual manufacturing lines.
DehazeLabs helps manufacturers turn manual visual inspection into defect detection, grading support, operator review queues, and production quality analytics. The workflow runs from image capture through labeled dataset to operator dashboard — starting narrow, proving value, then expanding. Lighthouse pilot in leather manufacturing; the same pipeline applies across textiles, electronics, automotive materials, packaging, and logistics QC.
Manual visual inspection has three failure modes that compound as production volume grows. First, inconsistency: experienced inspectors disagree on borderline defects — and that disagreement is invisible until a customer complaint surfaces it. Second, no analytics: defects are tallied on paper or spreadsheets, so the pattern data that would identify upstream process problems never reaches the people who can fix them. Third, throughput pressure: when production accelerates, inspection time per unit compresses, and escape rate increases.
AI-assisted inspection doesn't replace the inspector. It gives them a prioritized review queue — images ranked by predicted defect probability and severity — so their attention goes to the units that most need human judgment. It also creates the structured defect dataset that makes batch-level analytics and continuous improvement possible for the first time.
The 4-week lighthouse workflow
Defect taxonomy — start narrow
The taxonomy is defined in Week 1 after seeing real samples. Starting with 5–8 classes is correct. Expanding before you have sufficient labeled examples per class is the common mistake that degrades model performance. A simplified example for surface-material inspection:
Taxonomy is refined after first data review. Do not finalize before seeing real samples from the line.
What the operator dashboard shows
The dashboard is not a model output viewer. It is a review queue — the interface through which the inspector's attention is directed to the units that most need human judgment, and through which their decisions are captured and made queryable.
Grade outcome, not just defect type
The dashboard captures both the predicted defect label and the inspection decision: acceptable, rework, or reject. This is what makes batch-level quality analytics possible — not just "how many scratches" but "which batches produced the most reworks, and what do those units have in common." That pattern data is what connects the inspection workflow to upstream process improvement.
Capture protocol — the thing that determines everything
Bad image capture ruins model performance more reliably than any model choice. The Week 1 facility visit produces a standardized capture protocol: lighting type and angle (raking light for surface defects; diffuse overhead washes out scratches), camera angle and working distance, background material, sample handling to prevent adding handling marks, and image naming convention that encodes batch and station metadata.
For most manual inspection stations, a standardized capture setup costs under $500 in materials. Investing in the protocol in Week 1 determines whether the labeled dataset is usable in Week 2.
From paper tally sheets to a queryable defect dataset.
Manufacturing Quality AI — common questions.
Does this replace human inspectors?
What materials and product types does this work for?
What does a lighthouse pilot actually deliver in 4 weeks?
How consistent does our current inspection process need to be before this works?
What cameras and lighting do you need?
Manual inspection with no defect analytics, inconsistent grading, and growing volume?
The lighthouse pilot is designed to be fast and low-risk: narrow scope, light price, real results in 4 weeks, and an anonymized case study that justifies a production rollout. Tell us about your facility and inspection workflow — we'll tell you whether this maps to your situation.