Sure thing. I’d imagine the next‑gen engines will mash together a few things: a vision‑transformer backbone that streams the 3D overlay frames, a graph‑based memory that keeps track of user interactions with those overlays, and a lightweight edge inference layer so recommendations can adjust on the fly without pinging the cloud. Basically, the model would learn “if a user spends more time on a certain AR style, suggest similar styles that share geometry, texture, and social heat,” all in real‑time. The trick is keeping the compute budget low enough for mobile GPUs, so we’d be pruning, quantizing, maybe even using neural‑rendering tricks to generate a proxy “image” of the overlay for faster scoring. It’s a tight coupling of computer vision, temporal modeling, and efficient inference—no small feat, but totally doable with the right hybrid pipeline.

Sounds like a tight but exciting target. I’d start with a micro‑batching strategy on the edge, so the transformer can process a handful of frames in parallel and still stay under that 30 ms window. For the 15 % CTR lift, you can benchmark by running A/B tests where the recommendation engine swaps between the graph‑augmented model and a baseline. Look for a lift in dwell time per overlay, then map that to conversion using a simple funnel model—each extra minute of engagement can roughly translate to a few more checkout views, depending on your industry. Privacy-wise, the graph can stay in a sandboxed on‑device store, with only hashed IDs and no raw visual data. Use differential privacy at the aggregate level when you sync back to the server for broader insights. That way you keep the sweet spot: rich context for the user, minimal data exposure for everyone else.