Automotive · Paint-shop defect detection
Defects that dry in the booth cost ten times as much at rework
Banao builds computer-vision paint inspection that grades every body inside the booth — while the paint is still wet. Runs, sags, orange peel, dust inclusions, and fish-eye are flagged before the body leaves the spray zone, so rectification takes minutes rather than hours of flatting and re-spraying.
The model runs on booth-mounted cameras against your actual paint conditions: booth lighting, colour variants, and substrate mix. It does not require a separate inspection station or a line pause.
CP Plus— vision inspection layer deployed on existing booth cameras, no line modification required.
The first call is free · 45 minutes · no obligation
What we build
What Banao's paint-shop inspection delivers
Paint-shop AI that only catches textbook defects under ideal conditions is not useful on a real line. Every item below is built for live booth conditions.
In-booth detection, wet paint
The model grades defects while the body is still in the spray zone. A run caught wet takes a rag and thirty seconds; the same run caught dry requires full flatting and a re-spray pass — a cost difference measured in hours per body.
Defect-specific models per paint type
Solid, metallic, pearl, and matte finishes fail in different ways. We train per-paint-type models — metallic flake direction inconsistency requires different signal extraction than a sag on a solid base coat, and the two should not share a threshold.
Dust and contamination zone mapping
The system maps contamination by panel zone and correlates with air-flow and temperature logs, so process engineers can trace a dust-spike to a specific booth zone instead of chasing it around the paint store.
Grading calibrated to your QC standard
We set grade thresholds against your internal acceptance criteria and your customer-facing quality gate — so the model does not stop bodies your inspectors would pass, and does not clear ones they would reject.
Shift and booth-condition analytics
Defect rates broken down by shift, booth, colour family, and ambient condition give process and quality engineers the pattern behind the number — not just a daily count.
Operator override and correction loop
Painters and QC leads can flag any model call they disagree with from a tablet at the rectification point. Those corrections feed back weekly, keeping the model aligned with seasonal booth-condition changes and new paint formulations.
Receipts
In production, with real booths
Metrics shown dotted (··) are being verified in our case-study metrics pack. We publish numbers once confirmed, not before.
Booth-camera vision for paint and assembly inspection
CP Plus manufactures surveillance hardware at scale and needed defect detection that worked with cameras already fixed in the production environment. Banao added a vision inspection layer to existing infrastructure, covering both assembly and paint-shop quality gates, without line reconfiguration or new camera cabling.
Dogfooding
Our own AI survives production before yours does
Banao runs a ~300-person engineering company on the same AI systems it builds for clients. InterviewGod screens our own engineering hires; Vikaas drives our own demand generation. No AI we sell is theoretical — it has already had to perform inside our own operation.
A paint inspection model that passes a lab test but fails under real booth lighting is a familiar failure mode. The discipline we hold ourselves to is the same standard we apply to every line deployment.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When paint-shop AI is not worth building yet
We have scoped paint-shop inspection for lines where the honest answer was not yet. Here is when we say that:
- Unstable booth conditions: if lighting, temperature, or air-flow vary too widely and cannot be stabilised, model accuracy degrades faster than defect rate falls. We assess this in the Discovery Sprint before anything is committed.
- Very low throughput: on a hand-built or low-volume line, a trained QC eye with a structured inspection sheet costs less than a camera system and stays more flexible. We'll tell you that before you spend.
- No defined acceptance standard: if grading criteria vary between inspectors or shift-by-shift, a model cannot be calibrated to a standard that does not exist yet. The right first step is fixing the criteria, not deploying a model against them.
How we start
How we start — see the defects before you commit
We do not propose a paint-shop system without first looking at your actual booth conditions, paint types, and defect history.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your booth camera feeds and paint images, test model feasibility against your hardest defect classes, and produce an accuracy baseline and cost-benefit case you keep regardless of whether you proceed. If you go ahead, the Sprint fee is credited against the build.
- 02
Build
Model training on your actual paint types, defect classes, and booth conditions. Integration with booth cameras, your MES or PLC, and your QC workflow. Camera placement and lighting recommendations included where needed.
- 03
Production & continuous improvement
Live deployment with painter and QC-lead override, a shift-level defect dashboard, and a correction loop that keeps the model aligned as paint formulations and booth conditions change with the seasons.
FAQ
Frequently asked questions
Can the model handle multiple paint colours and types on the same line?
Yes. We train per paint type — solid, metallic, pearl, matte — and the system switches model and threshold settings as bodies move through. Colour changeovers are standard on mixed-model lines and the architecture handles them without manual reconfiguration between jobs.
Our booth lighting varies by shift. Will that break accuracy?
Lighting variation is the most common accuracy risk we find in paint inspection audits. The Discovery Sprint specifically tests your booth lighting range. Where variation is controllable, we recommend stabilising it before training; where it isn't, we build resilience to it directly into the model.
What is the detection latency — will it hold up the body on the line?
Inference runs on hardware inside or adjacent to the booth. Detection and grading complete in under a second per panel zone. The model runs in parallel with normal body movement and does not require a line pause or additional dwell time at the inspection point.
Do we need to replace our existing booth cameras?
Not usually. Banao works from existing cameras where resolution and placement are adequate. The Discovery Sprint audit checks whether your current setup can deliver a usable signal; new cameras are only recommended when the existing hardware genuinely limits detection accuracy.
How does the operator correction loop work without slowing the team down?
The override UI is a simple accept/reject stamp on the flagged image, accessible on a tablet at the rectification point. It adds under five seconds per correction. Those corrections feed the model's improvement cycle weekly — the system improves without asking operators to fill in forms or stop the line.
Get started
Catch the defect wet, not at rework
Bring your worst defect class and your booth layout. In 45 minutes we will tell you whether in-booth vision inspection is buildable for your line — and what the rework saving looks like.
Book a Discovery Sprint