E

AVIA · Training Loop

Training closesthe data loop

A model trained on curated data is only the halfway point. What it misses on the line comes back as reviewed, corrected samples — and the next round starts smarter.

THE DATA LOOPruns forever · each lap costs lessDATA IN · MODEL OUTand back againevery lap starts smarterCapture01 · CAPTUREInspect02 · INSPECTCurate03 · CURATEAI pre-labels04 · AI PRE-LABELSHumans review05 · HUMANS REVIEWTraining feeds back06 · TRAINING FEEDS BACK

The Data Loop

The six stagesof the data loop

The operating rhythm of an AVIA deployment — the first round bootstraps the model, and every round after keeps improving it.

  • 01
    CaptureFootage and images come in from lines, cameras, and warehouses.
  • 02
    InspectThe whole dataset maps into one embedding space — clusters, gaps, and sparse edges made visible.
  • 03
    CurateDiagnostics flag suspected mislabels, near-duplicates, outliers, and low coverage.
  • 04
    AI pre-labelsCold-start from zero samples: a text prompt, a click-box, a reference image, or a batch run.
  • 05
    Humans reviewPeople verify and correct the drafts; every correction is kept as training signal.
  • 06
    Training feeds backTrain, deploy, and let production misses return as the next batch's priorities.

Feedback

Deployment isa data source

Missed defects and low-confidence calls on the line are captured, reviewed, and corrected — then ranked by active learning into the next training batch.

  • Misses come back as samples, not anecdotes
  • Corrections are kept as training signal
  • Each round's priorities come from the field
ACTIVE LEARNING LOOPDATASET · 100k+ samples● labeled ◦ uncertain · diversetrainTraina fast modelround 01 · labeled 0.1%share shrinks each roundthe model re-enters the loop · uncertainty + diversity choose what's next

One feedback round

From a missed defectto redeployment, in five steps

01

Capture misses

Production misses and low-confidence calls are collected.

02

Review & correct

People verify and correct each one.

03

Rank

Active learning ranks them into the next batch.

04

Retrain

The model trains on the enriched dataset.

05

Redeploy

The improved model returns to the line.

When the loop closes

Measured resultsfrom the loop

4 → 0.3

Missed defects per month at a PCB plant

−70%

Wafer review time

6 wks → 1 wk

Warehouse video review cycle

From the AVIA customer deck

FAQ

Frequently askedquestions

Yes — it is a one-stop annotation and training platform. The loop runs from curated data to a trained, redeployed model; you bring the footage and the review decisions.

Run the loop onyour production data

See a feedback round run end to end on your own production data.