Client Success Story

AI Fashion Social Mobile App: From Inherited Code to Production

Screenshot of the AI-Fashion Social Mobile App project interface

Project Overview

From Legacy Code to a Production-Ready Social Ecosystem

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

Strategy & Execution

How We Stabilized the Codebase & Shipped AI-Powered Tagging

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.

Challenges in Scaling Content Discovery & Stability

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.

Interface screenshot demonstrating Working Beta and Product Foundation features

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.

Client Testimonials

What Our Clients Say

Feedback on how our innovative solutions help achieve business results.

Clutch logo

Xedrum delivered a production-ready AI-powered application within the agreed timeline and budget, including a working release on Apple TestFlight. The team was highly proactive in resolving issues, adapting to evolving requirements, and ensuring the AI functionality aligned with real user needs. Throughout the engagement, Xedrum consistently treated the project as a priority and demonstrated a strong understanding of both technical and business concerns.

Celine LeibfriedCEO at Unscripted
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Xedrum successfully implemented a robust system that allows users to upload data, execute automated jobs, and retrieve results efficiently. The team delivered all components on schedule, maintained clear communication through weekly and ad-hoc meetings, and demonstrated professionalism throughout. The final solution met both technical and operational expectations.

Tomer PeretzCTO at Osirix
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Xedrum's engineers worked closely with the client's project manager, integrating seamlessly into the existing workflow. The team consistently delivered planned milestones, maintained strong communication through regular online meetings, and brought a personal, collaborative approach that made them feel like an in-house AI team rather than an external vendor.

FredericCTO at Touch2Seen
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Xedrum operated as an extension of the client's internal team, taking ownership of both delivery and day-to-day execution. Their engineers quickly understood the product context, collaborated across time zones, and proactively contributed ideas to improve the solution. The team maintained consistent velocity, adapted to changing priorities, and helped move the product forward without adding management overhead.

Arnon ZamirCTO at Aristo
Clutch logo

Xedrum successfully implemented a robust system that allows users to upload data, execute automated jobs, and retrieve results efficiently. The team delivered all components on schedule, maintained clear communication through weekly and ad-hoc meetings, and demonstrated professionalism throughout. The final solution met both technical and operational expectations.

Richard BatesCEO at Acumen Data

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Hi, I'm Danylo Melnychuk

CEO at Xedrum

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