A desktop tool that gives photographers automatic metadata and keyword suggestions without ever touching the original files.
A Lightroom-compatible desktop tool that analyzes photo folders via the Google Vision API and generates metadata, keyword suggestions, and XMP sidecars, all without touching the original files.
Photographers spend hours tagging and keywording large folders of images so they're searchable in Lightroom and clients can actually find what they need. The work is repetitive, and most automation tools either touch the original files or break the Lightroom workflow.
sideA needed something that could read a folder, classify the images, and produce metadata Lightroom would pick up — without ever modifying the photos themselves.
Built a desktop app in Python with a clean GUI so photographers don't need a terminal. Integrated Google Vision for classification and keyword extraction.
Output goes into separate sidecar files (metadata.csv, XMP sidecars) so the originals are untouched and Lightroom can ingest the metadata natively.
The capabilities sideA now has.
Folder-level analysis
Point it at a folder, get classification and keyword suggestions for every image inside.
Lightroom-native output
Generates XMP sidecars and a metadata.csv so the work shows up wherever Lightroom looks.
Originals never touched
All output is sidecar — the source photos are read-only.
Desktop GUI
No terminal, no scripting — a simple app a photographer can run.
What was a multi-hour manual keywording session becomes a folder-scan job that runs in the background.
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