Applika – Turn GitHub Repos into Store-Ready Assets

Applika analyzes GitHub repositories using Gemini 3 to automatically generate ASO-optimized, policy-compliant, and localized app store content for iOS and Android.

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Inspiration

As a mobile developer, I experienced firsthand how time-consuming it is to prepare ASO-compliant and localized app store content.

Writing titles within strict character limits, researching keywords, adapting descriptions for different cultures, and following platform policies often took more time than building the app itself. This inspired me to build Applika — a system that understands applications directly from their source code and transforms them into market-ready store assets.

What it does

Applika analyzes GitHub repositories and automatically generates:

  • ASO-optimized titles, subtitles, and descriptions
  • Platform-specific metadata for App Store and Google Play
  • Policy-aware content including Privacy Policy and Terms of Use placeholders
  • Culturally adapted localization for multiple languages
  • Visual design briefs for store assets

Instead of relying on user-written prompts, Applika uses a code-as-context approach to understand what an application actually does.

How we built it

Applika is built using a MERN-based architecture:

  • Frontend: React, Vite, Framer Motion
  • Backend: Node.js and Express
  • AI Engine: Google GenAI SDK with Gemini 3 Pro and Gemini 3 Flash
  • Database: MongoDB Atlas (Free Tier)
  • Authentication: GitHub OAuth using Passport.js
  • Deployment: Render (Free Tier)

Gemini 3 Pro is used for deep reasoning and code understanding. When quota limits are reached, the system automatically switches to Gemini 3 Flash for fast parallel processing.

To improve accuracy and efficiency, only relevant repository files are selected and structured in the githubService layer before being sent to the model. This reduces token usage and improves reasoning quality.

Challenges we ran into

One of the biggest challenges was enabling Gemini to analyze large and diverse repositories without exceeding token limits.

To solve this, we implemented a custom file-filtering and prioritization system that focuses on meaningful files such as manifests, configuration files, and core logic components.

Another major challenge was complying with strict and frequently changing App Store and Google Play policies. This required building a flexible validation layer.

Deploying a stable system on free-tier infrastructure was also challenging and required careful performance optimization.

Accomplishments that we're proud of

  • Building a fully functional end-to-end system from repository analysis to store-ready assets
  • Successfully integrating deep code reasoning with Gemini 3
  • Achieving fast localization for multiple languages in parallel
  • Deploying a live, publicly accessible demo on free-tier services
  • Creating a privacy-aware system that does not store user source code

What we learned

Through this project, we learned that:

  • Effective localization requires cultural adaptation, not direct translation
  • Structured context dramatically improves LLM reasoning performance
  • Token optimization is critical for scalable AI systems
  • Combining reasoning-focused and speed-focused models leads to better results
  • Transparency and security are essential when working with user code

We also gained valuable experience in designing production-ready AI pipelines.

What's next for Applika – Turn GitHub Repos into Store-Ready Assets

Our long-term vision is to make Applika a complete AI-powered launch platform for mobile developers.

Next steps include:

  • Advanced ASO keyword trend analysis
  • Automated A/B testing for store metadata
  • Support for additional marketplaces and platforms
  • Integrated visual asset generation
  • Team collaboration and workflow tools

Our goal is to reduce global app launch friction and help developers reach international audiences faster.

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