Fuel Your AI Models
With Clean Data.
Build massive datasets for LLMs, RAG, and Computer Vision. Collect millions of text and image samples without getting blocked.Zero bandwidth costs.
import requests
from langchain.document_loaders import WebBaseLoader
# Proxy for unlimited scraping
proxies = {
"http": "http://user:pass@<YOUR_GATEWAY_HOST>:7777",
"https": "http://user:pass@<YOUR_GATEWAY_HOST>:7777"
}
def fetch_training_data(url):
# Rotate IP automatically to avoid 403 Forbidden
response = requests.get(url, proxies=proxies)
if response.status_code == 200:
# Feed clean HTML to your vector DB
return process_for_rag(response.text)
# Scrape massive datasets without bandwidth limits
urls = ["https://wiki-source.com/ai", "https://news.com/tech"]
for url in urls:
data = fetch_training_data(url)
print(f"Ingested {len(data)} tokens.")Why AI Projects Fail with Standard Proxies
Training a model requires terabytes of data. Paying $10/GB for proxies makes building datasets impossible. We solved the cost and reliability problem.
Volume & Velocity
Scrape millions of pages per day. Our infrastructure handles high concurrency for massive dataset ingestion.
Anti-Bot Bypass
Residential IPs appear as real home users. Bypass Cloudflare and CAPTCHAs to access high-value data sources.
Flat-Fee Pricing
Don't let bandwidth costs kill your startup. Pay one monthly price for unlimited data transfer.
Essential for Modern AI Workflows
Whether you are fine-tuning Llama 3 or building a real-time RAG application, you need external data.
LLM Training & Fine-Tuning
Collect diverse text data from forums, news sites, and specialized wikis to train your models on niche domains (Medical, Legal, Coding).
- ✓ Scrape Common Crawl alternatives
- ✓ Multi-language data extraction
RAG (Retrieval-Augmented Generation)
Feed your Vector Database (Pinecone, Milvus) with real-time data. Ensure your AI chatbot always has the latest stock prices, news, or product details.
- ✓ High-frequency scraping
- ✓ Low latency response