AI edit suggestions for Lightroom catalog workflows

A photographer finishes a 400-shot wedding or commercial shoot, imports the catalog into Lightroom Classic, and then spends the next four hours culling and editing — making the same adjustments over and over on similar shots with no system to tell them which edits actually worked across their past catalogs or suggest a starting point based on scene type. The complaint 'suggestions to optimize effects and improve efficiency while editing photos are missing' and 'the program needs to add more artificial intelligence to make editing easier and smarter' reflects a real workflow gap: the feedback loop between past editing decisions and current editing is completely manual.

This gap persists because Adobe's AI investments in Lightroom are focused on generative fill and subject masking — features that make good press — rather than on the unsexy problem of learning from a specific photographer's edit history and surfacing patterns. Adobe also serves millions of users with wildly different styles, so a personalized suggestion engine would need per-user training data, which is structurally awkward to build and maintain inside a monolithic product.

What photographers currently do is either apply a preset (which is static and ignores scene context) or manually copy settings from a similar shot (which requires remembering which shot that was). Neither approach gets smarter over time. A system that reads a photographer's exported catalog, identifies which edits they made on which scene types, and surfaces a ranked starting point for new imports would save a working photographer several hours per shoot. At two to three shoots a week, that compounds fast.

This is a business because the catalog is the asset — a photographer with three years of Lightroom history has a goldmine of personal editing data that no generic preset can replicate. Once you're embedded in their catalog workflow, switching cost is high and the value delivered per shoot is measurable.

What to build

Build a desktop companion app for Lightroom Classic that reads the user's catalog database, clusters their past edits by scene type and lighting condition, and surfaces a ranked list of edit starting points for newly imported photos, applied as Lightroom presets with one click.

Where to start

Launch as a free catalog analyzer that tells photographers which of their presets they actually use and which scene types they edit most — no AI suggestions yet, just a usage report. This builds trust and captures the catalog data needed to train the suggestion model.

The hard part

Lightroom Classic's catalog is a local SQLite database with an undocumented and occasionally changing schema — reliably parsing edit history across catalog versions without corrupting anything is technically risky and will scare off cautious early adopters.

How it makes money

One-time purchase of $79 with a paid upgrade path for each major Lightroom Classic version, matching how photographers already buy plugin tools in this ecosystem.