Phota Labs uses AI to turn everyday photos into professional-looking shots

Phota Labs has launched an iOS app that positions itself as a personal AI photo assistant, automatically reworking everyday snapshots so they look professionally shot. The company argues the market already has plenty of editing tools, and instead wants to automate the process outright — turning a shot you took into a better one without user effort.

The a16z-backed startup was founded by Cecilia Zhang and Zach Xia, who met at Adobe while working on computational photography products including Project Indigo, Lightroom, and Photoshop. Their thesis: editing tools can enhance a photo, but they can't rescue an image that was never captured well, so Phota focuses on fixing the shot itself while keeping people's natural appearances intact.

Zhang describes the experience as proactive, with the assistant reviewing the camera roll and imagining how a photographer would have captured the scene on the spot. Concretely, that can mean converting a wide-angle group photo into a tighter portrait or turning a selfie into a regular shot. Users pick from photography styles that control the intensity, from subtle edits that preserve the original lens and composition to aggressive transformations adding fisheye lenses, alternative angles, and entirely new perspectives.

The founders distinguish their approach from ChatGPT and other generative image tools, which they say can distort a person's appearance — especially on blurry photos where the model never learned what the person actually looks like. Zhang says Phota built its own personalization technology that learns a subject's identity and preserves it through any transformation. Edits also carry invisible watermarks identifying them as AI-altered when shared on social platforms.

Phota Labs is initially targeting heavy photo-takers who share to social media but want to skip tedious editing, with an eventual broader audience in mind, including retirees making photos for grandchildren and parents assembling polished family albums.

What to watch: whether identity-preserving transforms hold up on the hardest cases — motion blur, group shots with multiple faces — and whether invisible watermarking satisfies platform and regulatory disclosure norms as AI-edited media spreads. Open questions also include how much of the value comes from the underlying models versus the founders' Adobe-honed computational photography stack, and whether a subscription photo assistant can compete with general-purpose chatbots already shipping image editing for free.