Collect large volumes of publicly available web content to power AI model training, experimentation, and data-driven research.
Modern AI systems are only as good as the data they are trained on. CrawlKit enables teams to reliably gather diverse, real-world web data at scale — transforming the open web into a continuously evolving training resource.
High-quality training data is the foundation of every successful AI project. However, sourcing large, diverse, and up-to-date datasets from the web is often slow, fragmented, and difficult to maintain.
CrawlKit simplifies this process by allowing teams to systematically collect publicly available content across websites, industries, and regions — creating datasets that reflect how information actually exists on the web.
This approach helps AI models:
CrawlKit supports a wide range of training data use cases across AI and machine learning workflows.
Teams use CrawlKit for training data collection in a variety of AI-driven applications:
Build domain-specific or fine-tuned language models using up-to-date web content.
Train models for classification, summarization, translation, or sentiment analysis.
Improve relevance by learning from real product, content, and market data.
Train assistants on real questions, answers, and contextual content.
Extract entities, relationships, and attributes from web-scale data.
Unlike static datasets, web data is:
CrawlKit supports both early-stage experimentation and production-scale AI systems.
Get started in minutes with our simple API.
const response = await fetch('https://api.crawlkit.sh/v1/crawl/raw', {
method: 'POST',
headers: {
'Authorization': 'ApiKey ck_xxx',
'Content-Type': 'application/json'
},
body: JSON.stringify({
url: 'https://example-blog.com/articles'
})
});
const { data } = await response.json();
// data.body contains the raw HTML for training data extractionWhy teams choose CrawlKit for training data collection.
Collect thousands or millions of data points across multiple sources without manual intervention.
Adapt your data collection strategy as your model evolves — without rebuilding your pipeline.
Keep training datasets aligned with the current state of the web.
Build datasets spanning e-commerce, media, business, research, real estate, HR, and more.
Reduce the time between idea, dataset creation, and model training.
Training Data Collection with CrawlKit is commonly used by:
If your AI system depends on understanding real-world web content, this use case provides a strong foundation.
The quality of your AI starts with the quality of your data. CrawlKit helps you move beyond limited, static datasets and unlock the full potential of the web as a training resource — enabling smarter models, faster iteration, and better results.