Vision AIFor Manufacturers

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.

$23B+Annual scrap cost in US manufacturing
$50–120KAverage cost per recall event
WeeksTypical deployment timeline

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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.

  • 01
    72%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.

  • 02
    23%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.

Before Dotte.
After Dotte.

FeatureBefore DotteAfter Dotte
Detection latency8–24 hrs (end of line)< 100 ms (inline, real-time)
Inspection consistencyVariable — depends on operator99.97% repeatability
Defect classificationManual, single-categoryMulti-class CV model, auto-labeled
IntegrationSiloed inspection stationMES / SCADA data feed
Deployment time6–18 months (legacy systems)Weeks on existing hardware
Cost per inspection pointHigh OpEx (headcount)Fixed SaaS, scales to $0 marginal

Results From
The Floor

  • 87%reduction in scrap rate

    We were scrapping 3–5% of our output every shift. Dotte Product's inline detection caught the root cause within the first week. Scrap rate dropped to under 0.4% in 30 days.

    Director of OperationsTier 1 Automotive Supplier, Michigan
  • 22%throughput increase

    Our inspectors were burning out from high-volume visual checks. Dotte Product handles the classification — they handle escalations. Throughput is up 22% because we stopped bottlenecking at inspection.

    VP of ManufacturingConsumer Electronics OEM, Tennessee
  • 2 weekstime to first inference

    I was skeptical about another 'AI for manufacturing' vendor. Dotte Product had us running inference on our existing line cameras in two weeks. No new hardware, no long integration project.

    Plant ManagerPrecision Metal Fabrication, Ohio

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.

  1. 01

    Connect

    Day 1–3

    We connect to your existing line cameras — IP cameras, GigE vision, USB — no new hardware required in most deployments.

    Supports RTSP, ONVIF, GenICam protocols
  2. 02

    Train

    Day 3–10

    Our team annotates defect examples from your actual production samples. The model learns your specific tolerances, not generic benchmarks.

    Minimum 200 labeled samples per class
  3. 03

    Deploy

    Day 10–14

    Inference runs on-premises on an edge compute node we provision. Latency under 100ms. No cloud dependency for production.

    Edge-first, cloud sync for dashboards
  4. 04

    Improve

    Ongoing

    Every 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

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