Capture S

An ongoing 0→1 project: solving a real-life habit problem with AI

Capture S is an ongoing project that helps users turn screenshots from a passive archive into an active tool for memory, intent, and action. It helps users understand what exactly they saved, why they saved it months ago, and where to find it later with an AI layer on top. Later, they can search through it in natural language, even if they only remember a tiny piece of context around what they saved.

screenshot is not just an image

I interviewed 4 people who use screenshots for everyday planning, shopping, references, work, social content, recipes, places, and text-based information.

The interviews focused on three areas:

the moment of saving
the moment of returning
the gap between what the screenshot contains and what the person remembers later

screenshot = Intent, but also more context

During the walkthroughs, I noticed that screenshots often held more than the thing someone wanted to save.

Understanding the original intent often depended on more than one visible object - it came from a mix of details, signals, and surrounding context.

AI is the missing context layer

Over time, saved screenshots become hard to search because people remember parts of it rather than exact exact image, date, source, or reason they saved it

Instead of relying on users to remember why they saved something, AI can extract objects, text, source, time, and surrounding clues - turning fragments of memory into searchable clues.

Product principles

Context over image
Action-oriented
Low-effort input
Searchable memory
AI as an assistant

iterations

Every successful products are built through assumptions, tests, and tradeoffs. For Captures, that iteration process was anything but simple, yet it's what ultimately set the app's tone and shaped its visual direction.

Minimum components for maximum results

A compact system of visual tokens and reusable components helped ensure that AI executed the design I intended, instead of generating random UI variations.

It gave the AI enough context to produce screens and flows that stayed close to the original design direction, while keeping the final output under my control.

First tests showed Analysis issues

The AI analyzed the screenshot before the user explained why they saved it. It interpreted the screenshot on its own and when user context was added later, the final data often became messy and inconsistent.

To fix this, I moved the context step to the very beginning of the flow. User adds a note first, then the AI analyzes with that context in mind.

the app features

01. Capture Intent

User picks a screenshot and adds a short note in their natural language. The AI uses that context to reflect the real meaning behind it.

02. Recall by meaning

Users search "that cozy cafe I wanted to visit." and AI matches by meaning and intent, so results surface even when the user can't remember how they originally saved it.

03. Preserve context

Every screenshot is enriched with useful metadata. The structured library makes it easy to browse or recover something specific - even months later, even if the original page is gone.

Next steps

Notifications

Screenshots often capture things tied to a moment like a gift idea or an event. A smart notification layer could surface these at the right time, reminding users before a birthday, a trip, or a deadline. The app already understands intent so notifications would close the loop between saving and acting.

Bulk analysis

Use bulk image analysis as part of the onboarding experience, so users have a smoother transition from phone camera rolls to the App experience. Users see their own content organized and searchable right away, which makes the transition feel natural instead of effortful.

Smoother motion

The current motion handles the core flows well, but native mobile interaction has a quality bar that's hard to hit in early-stage tools. Moving toward Swift might unlock proper smoothness and transitions, gesture-driven transitions, and the kind of polish that makes an app feel finished.