See Every
Defect Before
It Ships
Replace costly end-of-line rejects with real-time computer vision that catches defects at the source — on your existing hardware, deployed in weeks.
The Problem
Your Current Process
Is Hemorrhaging Margin
High-volume manufacturing runs on tight tolerances. The moment a defect escapes the line, the cost compounds — scrap, rework, recall risk, and customer trust eroded.
- 0172%of defects discovered downstream
Defects Caught Too Late
End-of-line inspection catches failures after hours of value has been added. A single bad part can invalidate an entire batch by the time it surfaces.
- 0223%average human inspection error rate
Inconsistent Human Inspection
Visual inspection accuracy degrades over an 8-hour shift. Fatigue, lighting variation, and subjective judgment introduce unpredictable escape rates.
- 03$50K+median cost per recall or rework event
Reactive, Not Preventive
Scrap reports tell you what went wrong yesterday. Without real-time feedback, process drift compounds invisibly until a costly rework event forces action.
The Solution
Before Dotte.
After Dotte.
| Feature | Before Dotte | After Dotte |
|---|---|---|
| Detection latency | 8–24 hrs (end of line) | < 100 ms (inline, real-time) |
| Inspection consistency | Variable — depends on operator | 99.97% repeatability |
| Defect classification | Manual, single-category | Multi-class CV model, auto-labeled |
| Integration | Siloed inspection station | MES / SCADA data feed |
| Deployment time | 6–18 months (legacy systems) | Weeks on existing hardware |
| Cost per inspection point | High OpEx (headcount) | Fixed SaaS, scales to $0 marginal |
How It Works
From Camera Feed
To Closed Loop
Dotte Product is designed to be operational in weeks, not months. No lengthy system integration project. No ripping out existing infrastructure.
- 01
Connect
Day 1–3We connect to your existing line cameras — IP cameras, GigE vision, USB — no new hardware required in most deployments.
Supports RTSP, ONVIF, GenICam protocols - 02
Train
Day 3–10Our team annotates defect examples from your actual production samples. The model learns your specific tolerances, not generic benchmarks.
Minimum 200 labeled samples per class - 03
Deploy
Day 10–14Inference runs on-premises on an edge compute node we provision. Latency under 100ms. No cloud dependency for production.
Edge-first, cloud sync for dashboards - 04
Improve
OngoingEvery false positive and missed defect feeds back into model retraining. The system's accuracy improves continuously on your line's real-world distribution.
Active learning pipeline, weekly model updates
Limited Early Access
Don't Let Another
Shift Slip By
We're onboarding a limited cohort of manufacturers for our pilot program. Early partners get dedicated onboarding support, model customization, and preferred pricing — locked in before general availability.
- Dedicated onboarding engineer
- Custom defect model training
- Pilot pricing locked for 24 months