Your leadership asked a bigger question: can this scale across manufacturing? This deck covers how the platform handles product variability, new products, added inspection points, governance, and the performance metrics your IT team wants.
The POC proved the hardest part: telling apart subtle cosmetic differences under real line conditions. Every other inspection you raised runs on that same foundation. Your six topic areas are the right questions to settle before scaling. This slide maps where each one is answered.
Shades, black tolerance, confidence, pink, and the colors that carry more risk.
Revisions, new generations, and exactly what retraining each takes.
Buffing, assembly, and additional checkpoints onboarded onto the platform.
New-product onboarding, maintenance cadence, and per-product rules.
Confusion matrix, precision/recall, false-positive and false-negative rates.
Your full inspection inventory, assessed point by point for feasibility and effort.
Each inspection is its own model: cosmetic today, buffing or assembly next. They all sit on one shared foundation for capture, edge inference, deployment, governance, and reporting. A new inspection inherits all of it on day one. That reuse is what separates a platform from a one-off build.
The model learns the decision boundary between colors from labeled examples, captured across the variation your line actually produces. It does not rely on fixed RGB values, which is why it holds up where a rules-based system fails.
We collect samples across the full range a shade actually shows on your line, so the model learns to separate adjacent tones instead of memorizing one "ideal" swatch.
We handle low-contrast black variation with controlled lighting (diffuse, grazing, cross-polarizer) and training on real lot, lighting, and material differences. Where two blacks read as identical to a calibrated sensor, we flag it and route to a person rather than invent a distinction.
Each classification reports a per-class confidence. Below an agreed threshold the unit routes to a person for a re-scan. That behavior produced zero escapes in the POC.
Neutral tones (blacks, grays), closely adjacent shades, and glare-prone metallics carry more risk. Pink against rose or skin tones is handled the same way: train on the confusable set, then validate that separation. A color-risk assessment names these before deployment.
Retraining effort scales with how much changed. In every case it is a data task, not an engineering rebuild, and the architecture stays the same.
A small design tweak or a new finish: add labeled examples, fine-tune, re-validate. The existing model absorbs it in days.
A significant redesign gets its own image set and a retrain, or a new product class. The same pipeline we proved in the POC, pointed at the new geometry.
Typically hundreds to low-thousands of labeled images per new variant, depending on complexity. We set the exact number per product so onboarding effort is never a surprise.
Each candidate you raised is a new model on the same platform. We assess every one through the same four-question lens, so "is this feasible?" gets an evidence-based answer.
Surface finish and texture consistency in Custom Manufacturing. Feasible as a surface-anomaly model; it likely needs raking light and higher resolution, which we scope in the assessment.
Presence, correct component, orientation. This is the closest to proven work: assembly-mismatch detection was one of the three checks the POC validated.
Missing-part detection, additional QA checkpoints, OCR at new points. Each is a model; the platform underneath is already built.
One repeatable path from a new inspection to a governed production model, looping back whenever your business changes.
Representative images from the line.
To your spec, on your data.
Held-out test, agreed thresholds.
Live at the edge, human in loop.
Drift & accuracy, continuously.
When drift or change appears.
Registered, approved, reversible.
Re-enter at Collect: add the shade, fine-tune, re-validate. The rest of the loop is unchanged.
Re-enter at Collect with the NPI image set, retrain that product, validate, deploy.
A fresh model runs the loop once, then joins the platform's monitoring and governance.
The first inspection pays to build the platform. Every one after it reuses the capture rig, the edge deployment, the MLOps, the governance, and the dashboards. What is left to build is the model and its data.
Builds the platform.
Reuses everything.
+ model & data.
+ model & data.
One platform, many checks.
Each version is tagged with its training-data hash, its validation metrics, and its approver. You can see exactly what's running and roll back to any prior version instantly.
Continuous monitoring flags when accuracy drifts or a new variation appears. That flag is the retrain trigger. You retrain on evidence, not on a fixed schedule.
A new or retrained model passes validation and a human sign-off before it ever reaches the line. Your quality team stays release-of-record; the AI advises.
Inspection criteria and thresholds are configurable by product family, so different products carry different rules and models. Rule management scales through platform config instead of new code.
We measure every model on a held-out set with the full confusion matrix. Those four numbers produce every metric your data team wants, and we set the operating point with you.
| Predicted DEFECT | Predicted OK | |
|---|---|---|
| Actual DEFECT | TPcaught | FNescape, costly |
| Actual OK | FPfalse alarm, re-scan | TNpassed |
Daniel and Citlali are building the beginning-to-end inspection inventory. For every point on it, we return a structured assessment, so leadership gets a costed blueprint to decide against.
This deck and the next working session, to align on the platform vision, the architecture, and the path to production. No cost.
A fixed-scope engagement that produces your blueprint: production architecture · product-onboarding & retraining strategy · model governance & maintenance · performance-metrics & reporting framework · a prioritized inspection roadmap across your inventory · a phased production SOW. It gives leadership a costed plan before any large commitment.
Phase 1: productionize cosmetic inspection, live on the floor. Phase 2: expand to additional inspection points (assembly, buffing, OCR, missing components). Phase 3: scale the platform across manufacturing with ongoing support, governance, and enhancements.