Stockpiper
Mobile App

Project Type
Concept Study
Industry
E-commerce
Services
Mobile App & AI Design

Overview

Stockpiper explores small shop inventory through a mobile experience built around a barcode-first stock screen with large one-handed actions. A shop-floor inventory companion for label capture, catalog cleanup and source-backed stock questions. The proposed journey connects shelf scan, catalog cleaner, ask stock notes, count session. Four original phone showcases establish the visual direction, while the case study defines the supporting feature set and a proposed Java architecture.

Stockpiper Mobile App hero

Original mobile interface concepts with fictional content. AI responses illustrate proposed interactions; they are not live model results.

Challenge

Shop staff need scanned labels, voice notes and item records connected while proposed stock changes remain explicit. On a phone, the person also needs to capture information quickly, understand where an answer came from and return to the original material without navigating through a dense desktop interface.

  • Keep shelf scan and catalog cleaner connected within a short mobile flow.
  • Make label reading correctable when the model misreads the input.
  • Explain inventory retrieval through visible references or user-selected preferences.
  • Keep changes, sharing and data retention under explicit user control.

Solution

The design begins with a barcode-first stock screen with large one-handed actions, using large touch targets, short task sequences and contextual sheets. Extract candidate item fields from packaging labels. Suggest consistent titles and categories for approval. The companion views keep drafts, original material and accepted decisions distinct. A indigo, mint and cream visual system gives this app its own character within the collection.

  • Extract candidate item fields from packaging labels.
  • Suggest consistent titles and categories for approval.
  • Answer questions from recorded stock data with source entries.
  • Group unresolved count notes without automatically changing quantities.

Results

  • 4

    Original mobile
    showcase views

  • 12

    Designed
    feature areas

  • 4

    Proposed AI
    workflows

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Stockpiper
Mobile App

Project Type
Concept Study
Industry
E-commerce
Services
Mobile App & AI Design

Overview

Stockpiper explores small shop inventory through a mobile experience built around a barcode-first stock screen with large one-handed actions. A shop-floor inventory companion for label capture, catalog cleanup and source-backed stock questions. The proposed journey connects shelf scan, catalog cleaner, ask stock notes, count session. Four original phone showcases establish the visual direction, while the case study defines the supporting feature set and a proposed Java architecture.

Stockpiper Mobile App — hero

Original mobile interface concepts with fictional content. AI responses illustrate proposed interactions; they are not live model results.

Challenge

Shop staff need scanned labels, voice notes and item records connected while proposed stock changes remain explicit. On a phone, the person also needs to capture information quickly, understand where an answer came from and return to the original material without navigating through a dense desktop interface.

  • Keep shelf scan and catalog cleaner connected within a short mobile flow.
  • Make label reading correctable when the model misreads the input.
  • Explain inventory retrieval through visible references or user-selected preferences.
  • Keep changes, sharing and data retention under explicit user control.

Solution

The design begins with a barcode-first stock screen with large one-handed actions, using large touch targets, short task sequences and contextual sheets. Extract candidate item fields from packaging labels. Suggest consistent titles and categories for approval. The companion views keep drafts, original material and accepted decisions distinct. A indigo, mint and cream visual system gives this app its own character within the collection.

  • Extract candidate item fields from packaging labels.
  • Suggest consistent titles and categories for approval.
  • Answer questions from recorded stock data with source entries.
  • Group unresolved count notes without automatically changing quantities.

Results

  • 4

    Original mobile showcase views

  • 12

    Designed feature areas

  • 4

    Proposed AI workflows

Let’s Crafting

Visual Impact

Start a Project