Enhance existing datasets with additional attributes sourced from the web to unlock deeper insights and better decision-making.
Raw datasets are rarely complete. CrawlKit helps teams enrich their existing data with up-to-date, real-world information from the web — turning static records into living, high-value assets.
Most datasets start with gaps.
Missing attributes, outdated fields, or limited context can significantly reduce the value of otherwise useful data. Dataset enrichment solves this by augmenting existing records with additional signals collected from publicly available web sources.
With CrawlKit, enrichment becomes a continuous process rather than a one-time task.
This allows teams to:
Dataset enrichment is the process of enhancing existing records by adding new attributes, metadata, or contextual information.
Instead of rebuilding datasets from scratch, CrawlKit enables you to layer web-sourced data on top of what you already have — improving quality without disrupting existing workflows.
Examples include:
CrawlKit supports enrichment across a wide range of data types and domains.
Teams use CrawlKit to enrich datasets across many data-driven workflows:
Improve model inputs by adding real-world attributes and context.
Enhance reporting with more complete and accurate dimensions.
Increase relevance by enriching entities with additional signals.
Build richer profiles for internal tools and analysis.
Add depth and validation to research datasets.
The web is the largest, most dynamic data source available.
By enriching datasets with publicly available web data, teams gain access to:
Traditional datasets degrade over time.
CrawlKit helps teams move from static snapshots to continuously enriched datasets that reflect changes in the real world. This shift enables ongoing optimization rather than periodic cleanup.
Whether you're enriching data once or maintaining long-term freshness, CrawlKit provides a foundation that scales with your needs.
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://company-website.com/about'
})
});
const { data } = await response.json();
// Extract company details to enrich your datasetWhy teams choose CrawlKit for dataset enrichment.
Reduce missing values and outdated fields across your datasets.
Richer inputs lead to more accurate predictions and insights.
Eliminate manual research and slow enrichment workflows.
Apply enrichment logic across thousands or millions of records.
Adapt enrichment strategies as data needs evolve.
Dataset Enrichment with CrawlKit is commonly used by:
If your AI system depends on understanding real-world web content, this use case provides a strong foundation.
If your decisions depend on the quality of your data, enrichment is not optional — it's essential. CrawlKit helps you turn incomplete records into comprehensive, actionable datasets.