Build training and RAG datasets from the web
By The ClickSet Team, Product & Research, ClickSet
ClickSet turns raw web content into clean, structured tables optimized for LLM fine-tuning and retrieval-augmented generation. You can run entity extraction, summarization, and topic clustering across thousands of records, then export to CSV or JSON or pipe directly into training infrastructure through the API.
For most ML teams, the model is the easy part. The hard part is assembling clean, well-sourced data, and that work routinely balloons from a two-week estimate into a quarter of cleaning and verification. ClickSet collapses that timeline by gathering, structuring, and enriching web data in one place, so your dataset is ready to train or index instead of ready to clean.
Why data prep eats your timeline
Web content is messy, duplicated, and inconsistently formatted. Turning it into a training-ready table means scraping, deduplicating, normalizing, and verifying sources, and each of those steps is its own project.
Doing this with one-off scripts is brittle. Sources change, formats drift, and the pipeline breaks quietly, so teams spend more time maintaining collection code than improving the model.
How ClickSet does it
- 1
Define the dataset
Describe the records and fields you need. ClickSet maps a schema and gathers matching content from public web sources at scale.
- 2
Structure and deduplicate
Raw content becomes clean rows with automated deduplication and validation, so you start from a usable table rather than a pile of HTML.
- 3
Enrich with AI operations
Run entity extraction, summarization, or topic clustering across thousands of records to add the labels and structure your pipeline expects.
- 4
Export or pipe in
Export to CSV or JSON, or stream the dataset directly into fine-tuning or RAG infrastructure through the API.
What powers it
Bulk processing
Gather and transform thousands of records at once instead of crawling source by source.
AI-assisted schema mapping
Describe the columns you need and ClickSet structures messy content to match.
AI operations
Apply extraction, summarization, and clustering across the whole dataset, not row by row.
API access
Read the finished dataset through one endpoint and feed it straight into training or retrieval systems.
What you get
- A training-ready table instead of weeks of raw collection.
- Automated deduplication and validation built into the pipeline.
- Extraction and labeling applied across thousands of records at once.
- Direct export or API streaming into your model stack.
Keeping datasets fresh over time
A training corpus or retrieval index is not a one-time build. Sources publish new content, facts change, and a model grounded in last quarter's data slowly drifts out of date. Teams schedule refreshes so the underlying table is rebuilt on a cadence, which means a retrieval system answers from current information instead of a stale snapshot.
Because every dataset is reproducible from its definition, experiments stay honest. You can rebuild the exact corpus behind a model, swap in a new source, or expand the schema, then compare results knowing the only thing that changed is the data you intended to change.
Who uses this
Building training and RAG datasets is an engineering and data science workflow. ML and AI teams use it to assemble fine-tuning corpora and retrieval indexes, and data science teams use it to keep those datasets fresh.
Frequently asked questions
What formats can I export? +
You can export datasets to CSV or JSON, or read them through the API and pipe them directly into fine-tuning or retrieval-augmented generation infrastructure.
Can ClickSet deduplicate and clean the data? +
Yes. Content is structured into clean rows with automated deduplication and validation, so you begin from a usable table rather than raw, repetitive HTML.
Can I keep the dataset up to date? +
Yes. You can schedule refreshes so a retrieval index or training corpus is rebuilt on a cadence instead of going stale after the first pull.
What does it cost? +
ClickSet starts on a free plan and uses a usage-based model with a prepaid balance and per-key spend caps, so cost tracks the size of your dataset. See the pricing page for current details.
Further reading
See how ClickSet handles your data workload
Describe the dataset you need, enrich it with hundreds of LLMs, and query the result through one API. Start on the free plan.