Build AI prototypes on your phone, no engineers needed
Libyan AI firms cannot scale tool development because skilled ML engineers are emigrating to Europe and the Gulf due to instability, creating a crippling talent shortage.
RaedML is a React Native mobile app that lets Libyan founders visually design, test, and deploy AI features using pre-trained models optimized for Arabic and regional use cases. It works offline for design work and syncs when connectivity returns. This removes the need for scarce ML talent by providing one-click model training, API generation, and cloud deployment.
AI entrepreneurs and tech founders building AI tools in Libya
Mobile-first design built for Libyan infrastructure realities with heavy emphasis on Arabic language models, low-bandwidth optimization, and local industry presets (agriculture, oil & gas, public services).
friendly
Drag-and-drop interface to design AI workflows directly on mobile
Curated library of models pre-trained for Arabic dialects, Libyan license plates, crop disease detection, etc.
Deploy your AI as an API or mobile SDK with auto-generated documentation
Design and configure pipelines without internet, syncs when connected
Test your model on device with camera/gallery input
Export clean Python or JavaScript code for your custom model
Mobile dashboard showing API usage, costs and performance
Share projects with co-founders for real-time mobile editing
50+ industry-specific starting points (chatbots for government services, predictive maintenance, etc)
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| phone | text | No |
| name | text | Yes |
| industry | text | Yes |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| name | text | No |
| pipeline_json | text | Yes |
| status | text | Yes |
| deployed_url | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| project_id | uuid | No |
| endpoint_url | text | Yes |
| model_type | text | Yes |
| monthly_cost | int | Yes |
| created_at | timestamp | No |
Relationships:
/api/projects/syncSync mobile project changes to backend
/api/deployTrigger cloud deployment of pipeline
/api/modelsGet available regional models
/api/inferenceRun test inference on deployed model
No custom training
10k inference calls/month
Custom
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 220 | 6% | $462 | $5,544 |
| Month 6 | 1,850 | 9% | $5,830 | $69,960 |
No ML engineers? No problem. Design, train, and deploy AI features visually from anywhere with RaedML — built for Arabic, low bandwidth, and Libyan realities.
Seed beta through Libyan university computer science groups and the Tripoli Tech Meetup community. Offer free Scale tier for 60 days to the first 25 users who deploy a real project. Partner with local agriculture cooperatives and energy firms who need computer vision and predictive tools.
Very easy to use
Extremely limited models and no production deployment path
Full deployment pipeline, Arabic support, and Libyan industry templates
Powerful no-code platform
Not mobile-first and weak ML capabilities
Native mobile experience with specialized ML components for Africa
Curated library of fine-tuned Arabic and North African domain models that improve as more users contribute anonymized training data.
Smartphones have reached critical penetration in Libya while powerful on-device ML (MediaPipe, TensorFlow Lite) and affordable cloud inference APIs have become available at the same time as the talent crisis peaks.
Performance limitations on lower-end Android devices common in Libya
Heavy optimization, progressive model loading, and fallback to cloud inference
Users may lack technical confidence to build AI
Extensive Arabic video tutorials, templates, and guided workflows
High cost of GPU inference during growth
Usage-based limits and partnership deals with Replicate and Together.ai
Success: Minimum 40% of participants say they would use the app weekly
Success: 120 active users and 25 deployed projects in 30 days
Success: 1,200 downloads and $1,200 MRR in first month
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