Build, Collaborate & Deploy AI Projects
Kodelyth is an open platform for AI, web & mobile development, SaaS services, and IT consulting. Join our community of innovators today.
AI-Powered Solutions Pricing
Choose the perfect plan for your AI development needs. All plans include access to our proprietary KodelythAI v1 platform.
For startups & small projects
- Basic AI Project Deployment
- Standard Software License
- 10GB Cloud Storage
- Email Support
- Basic Domain Hosting
- Premium AI Models
- Priority Support
- Custom AI Training
For growing businesses & teams
- Full KodelythAI v1 Access
- Premium Software License
- 100GB Cloud Storage
- Priority Email & Chat Support
- Advanced Domain Hosting
- VPS Management
- Custom AI Model Deployment
- 24/7 Monitoring
For large organizations & custom solutions
- Full KodelythAI v1 + Custom Models
- Enterprise Software License
- Unlimited Cloud Resources
- Dedicated Account Manager
- Custom Domain & Infrastructure
- Full Cloud Server Management
- AI Project Development
- SLA 99.9% Uptime Guarantee
Contact Business Team
Complete AI & Infrastructure Solutions
We provide end-to-end services for your digital transformation. Our proprietary KodelythAI v1 platform powers all AI deployments.
Our Core Features
AI Project Hosting
Host and manage your AI projects with version control, experiment tracking, and model deployment using KodelythAI v1.
Mobile Development
Build, test and deploy mobile applications with our integrated development environment.
SaaS Solutions
Create and deploy scalable SaaS applications with our cloud infrastructure and services.
IT Consulting
Connect with IT experts for consultation on architecture, security, and best practices.
AI Projects Powered by KodelythAI v1
DeepVision
NLP.js
EcoRoute
DeepVision Repository
Computer vision library for object detection and image recognition
import torch
import torch.nn as nn
import torchvision.models as models
class DeepVisionModel(nn.Module):
def __init__(self, num_classes=10):
super(DeepVisionModel, self).__init__()
self.base_model = models.resnet50(pretrained=True)
num_features = self.base_model.fc.in_features
self.base_model.fc = nn.Linear(num_features, num_classes)
def forward(self, x):
return self.base_model(x)
def predict(self, image):
# Preprocess image
# Run inference
# Return prediction
pass
if __name__ == "__main__":
model = DeepVisionModel()
print("Model initialized successfully")
Kodelyth AI Assistant
Powered by KodelythAI v1 - Get help with your code, debugging, and project setup