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Case study

AI-Fashion Social Mobile App

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A Berlin-based fashion social mobile app for enthusiasts to share outfits, discover styles, and interact through core social features with AI-powered tagging.

Industry:

Fashion/Social Media

Location:

Germany, Berlin

Duration:

Ongoing

Budget:

$25K

Case study

Our Approach

This was a startup-style engagement: move fast, ship a stable beta, and design the architecture to scale without burning money.

Our approach focused on: Completing and stabilizing an inherited codebase, improving maintainability and delivery speed.

Implementing AI-assisted tagging (LLM API) to improve content organization and discovery.

Shipping a production-ready mobile experience, aligned with social app expectations (feeds, profiles, interactions).

Building scalable infrastructure with cost trade-offs in mind (ready to grow, but lean for early-stage usage).

Setting up monitoring and maintenance workflows to keep the app stable post-launch.

Staying highly responsive to a startup environment with flexible hours and proactive problem solving.

When issues appeared (including developer fit), we handled it quickly by swapping developers fast and smoothly, keeping delivery on track.

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The Problem

The founder needed a reliable partner to: Add a meaningful AI feature (not 'AI for hype'), specifically automatic tagging to help users categorize and discover fashion content.

Take over partially completed work from another developer and finish the MVP.

Deliver a working beta quickly, without sacrificing core quality.

Build an infrastructure that would be scalable, but still optimized for early-stage budgets.

Ensure ongoing maintenance, monitoring, and feature rollouts as the product evolves.

AI-Powered Tagging (LLM Integration)

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Full-Stack Product Delivery

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Scalable Infrastructure with Cost Trade-Offs

WORKING BETA AND PRODUCT FOUNDATION

We delivered a working beta and product foundation that included:

KEY FEATURES IMPLEMENTED:
  • 01
    AI-Powered Tagging (LLM Integration)

    We implemented AI-driven tagging via an LLM API to support automatic tagging of user-generated fashion images/content, improved content organization and searchability, and better discovery experiences for users browsing styles and categories. This feature helped the app feel smarter and more 'social-feed ready' without requiring a complex custom ML pipeline at MVP stage.

  • 02
    Full-Stack Product Delivery

    We completed both frontend and backend development, turning early work into a cohesive product with stable performance and clean workflows.

  • 03
    Scalable Infrastructure with Cost Trade-Offs

    We designed infrastructure to handle expected growth while keeping early-stage costs under control, with clear upgrade paths as traction increases.

  • 04
    Monitoring + Maintenance Setup

    We set up monitoring and support processes to detect issues early and keep the app stable during testing and rollouts.

  • 05
    Ongoing Feature Rollouts + Startup Guidance

    We supported the founder with best-practice advice, product decisions, and a flexible roadmap approach as priorities shifted.

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Danylo MelnychukCEO at Xedrum
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