AI data collection
Use CyberYozh proxies for a variety of workflows and use cases related to AI agent training and data collection. Access local services without restrictions, use the open-source Yozh Scraper to get data from there, and train AI agents using clean IPs for predictable results.
Why do you need proxies for web scraping
AI agent training and AI data collection require a clean, predictable connection you can control and direct. If the agent has to train on specific local data, you need the IP of the country or city where that data is located; otherwise, you may get broken data.
Without a clear, geo-targeted IP, AI agents can:
behave uncontrollably and unpredictably
produce unpredictable results
face CAPTCHAs and local restrictions
get broken sessions or broken data
fail to log in or authenticate
That’s where CyberYozh proxy infrastructure helps.
CyberYozh includes:
mobile proxies for secure authentication and social data collection
residential proxies for fast geo-targeted access and automation
virtual numbers for authentication and virtual cards for payments
quality checks for all instruments before you deploy them
Yozh Scraper to collect data and train agents without issues
CyberYozh API to automate all its processes.
To obtain clean data for your agents, you must appear to be a regular user from a regular country, while respecting the website’s robots.txt to avoid issues. If the data is stored in a local service and database, accessing it via a clean local IP helps you avoid data distortion and restrictions. Moreover, AI agents must be directed via predictable connection endpoints to control their behavior.
Efficiency
Use cases
Text dataset collection. Articles, news, blogs, forums, and reviews.
Data collection for NLP models. Natural dialogues, comments, localized content, and user responses.
Image and media datasets. Scraping photos, videos, product cards, object images, UI elements, and more.
Training recommendation systems. Collecting data on products, user interactions, demand dynamics, and ratings.
Datasets for computer vision models. Images of objects, locations, documents, packaging, menus, and product categories.
User behavior analytics. Collecting anonymized behavior patterns, clicks, views, and interests.
Geodata and mapping datasets. Collecting locations, POIs, infrastructure, routes, and schedules.
Multilingual datasets. Using IPs from different countries to collect data in the corresponding languages.