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String Catalog
✓ verifiedFreemium
Git-integrated localization tool automating app string, release-note, and store-listing translation for mobile dev teams.
👁 2.3K/mo
Pricing
No public pricing
Hobby: $15/mo (3,000 base string keys, up to 40 languages)
Pro: $45/mo (7,500 base string keys, unlimited release notes)
Business: $75/mo (large apps, frequent releases)
No public pricing
Historical Data Pack: $49.9
Base Plan: $14.9/month
Advanced Plan: $24.9/month
Enterprise Plan: $34.9/month
Core features
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Connects to GitHub, GitLab, or Bitbucket for reviewable translation diffs
- ✦Automates translation of app strings, release notes, and store listing copy
- ✦Supports 40+ languages with brand-voice and protected-term controls
- ✦Offers human review queues and shareable no-login review links
- ✦Keeps native Apple and Android localization file formats
- ✦Provides a cost calculator based on strings, languages, and release frequency
- ✦Natural language to SQL conversion
- ✦Commits and Pull Requests Dashboard
- ✦Advanced Developer Skills Analysis
- ✦Strategic Investment Balance Monitoring
- ✦Collaborative Developers Map
- ✦Benchmarking Comparison with Other Teams
- ✦Smart Notifications
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Shipping localized app builds without slowing down release cycles
- →Translating App Store and Google Play release notes each launch
- →Keeping store listing metadata aligned across markets
- →Reviewing AI-generated translations before merging via Git
- →Scaling from one free language to full multi-market localization
- →Generating SQL queries from text descriptions.
- →Visualize historical graphs of code evolution
- →Assess development team performance using RSI and EMA
- →Understand developer skills and identify areas for improvement
- →Categorize commits by type (fixes, refactoring, etc.) to analyze investment balance
- →Identify individual and collective contributors within the team
- →Compare team performance with industry benchmarks
- →Receive weekly and monthly reports with AI-extracted insights
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