Adobe's Project Indigo Adds AI Photo Critique Tools
24 Jul 2026
Adobe is testing a new suite of AI-powered editing tools inside Project Indigo, its experimental iPhone camera app, in what could be an early signal of how consumer photo apps are racing to bundle generative AI directly into the capture-and-edit workflow.
What's happening
Project Indigo, launched last year by Adobe's Marc Levoy to give iPhone photography a more natural, SLR-like look, is getting version 1.1 — an update that adds a new "AI Playground" tab. The suite includes four sections:
- Photo Guidance — uses LLMs to critique framing, lighting, colors, and emotional impact, and suggests reshoots or edits to exposure and object placement.
- Object Editing — advanced background cleanup that lets users toggle removal of people, trash, wires, poles, fences, vehicles, and other clutter, plus AI-simulated depth-of-field blur.
- Styles — preset AI filter effects such as watercolor, pen and ink, ink line with color wash, monochromatic, and backlit-subject looks.
- Custom Edit — a prompt-based tool for describing detailed image manipulations in natural language.
Under the hood, these features currently run on Google's Gemini-based Nano Banana model rather than Adobe's own Firefly. Adobe says it isn't using Firefly right now but is open to swapping in other models — including Firefly — down the road.
The rollout, and the guardrails
Access is limited: the AI Playground tools are being tested free, with no sign-on required, for a small percentage of select users over the next few weeks. Levoy says the goal is "to learn how people use GenAI-powered editing," and Adobe may extend the experiment, expand the user pool, or run follow-on tests depending on how it goes. If the feature proves popular, Adobe has said it will eventually introduce a paid version.
On the trust front, images generated through Nano Banana carry Google's invisible SynthID watermark, and Adobe is working toward layering in C2PA Content Credentials metadata for images edited with Indigo's generative tools.
This isn't the first attempt at AI-assisted photo coaching — Google's Pixel camera coach, launched last year, offered similar but more generic framing suggestions. Object removal itself is old news, having been available in Apple Photos, Google Photos, and Adobe Photoshop for years. What's new here is bundling critique, style transfer, object editing, and custom prompting into one experimental mobile app.
Risks and open questions
A few things to watch:
- Third-party model dependency. Relying on Google's Nano Banana rather than Adobe's own Firefly introduces potential licensing or platform risk if Adobe later needs to switch providers.
- Unproven reliability. The AI critique and editing tools are experimental and limited to a small user base, so accuracy at scale hasn't been tested.
- Trust and provenance. Despite SynthID watermarking and planned C2PA metadata, user confusion or distrust around AI-manipulated images remains a live concern.
- Monetization is undecided. Adobe hasn't committed to pricing or a timeline for a paid tier, leaving the feature's long-term availability uncertain.
The report notes no specific numbers on tester counts or adoption, and it's unclear how Adobe's LLM-based critique meaningfully differs from Google's earlier camera coach beyond being framed as "less generic." Details on how C2PA metadata will actually be implemented, and how swapping models might affect performance, are also still unspecified.
Why founders should care
For founders building in photography, content creation, or consumer AI, this rollout is likely a useful signal rather than a direct threat. Adobe bundling multiple GenAI tools into a single app suggests consumer appetite for all-in-one AI-editing experiences may be growing — a trend worth tracking if you're building adjacent products.
Adobe's stated openness to swapping AI models (Nano Banana now, possibly Firefly later) also hints that model-agnostic architecture could become a more common consideration for AI product teams, reducing single-vendor lock-in risk. Similarly, the emphasis on watermarking and Content Credentials suggests that provenance and transparency features may increasingly be expected — not optional — in AI-editing products, which founders in this space should probably plan for early rather than retrofit later.
Finally, Adobe's cautious approach — a free, no-sign-on test limited to a small user slice before any monetization decision — offers a plausible playbook for startups validating their own AI features: gauge real usage patterns before committing to pricing or full-scale launch.