Datasets for AI
Move from an AI data brief to an evaluation-ready package with sample records, source coverage, schema, history, and refresh cadence already visible.
Package families
Each package family organizes public web data around the evidence a model must learn, retrieve, judge, or act on. Select the closest starting point, then inspect its actual records.
Build a versioned corpus around the domains, languages, entities, labels, and content forms that shape model behavior.
Prepare retrievable documents and structured facts with URLs, capture time, version context, and a refresh plan.
Create stable, source-linked test material for relevance, freshness, extraction, classification, and answer-quality workflows.
Supply the public facts, inventories, listings, results, and change signals an AI agent needs before it makes a decision.
Connect images, video, audio, transcripts, captions, and metadata in records designed for multimodal training and evaluation.
Package evaluation
Evaluate a representative sample and its data dictionary against your model task. The useful questions are concrete: what is one record, where did it come from, when was it observed, and how will it change?
{
"document_id": "doc_1482",
"title": "Product support guide",
"content": "...",
"source_url": "https://example.test/guide",
"language": "en",
"captured_at": "2026-08-07T10:15:00Z",
"schema_version": "rag.document.v1"
}Run representative objects through chunking, retrieval, joins, labeling, or evaluation before sizing the full package.
Does one record work?Review field types, identifiers, source context, missing states, timestamps, and schema versioning.
Can every state be interpreted?Compare domains, markets, languages, record types, observation windows, and available historical depth.
Does the package represent the task?Align one-time backfill or recurring updates with the point at which model knowledge becomes stale.
When must the data change?Three operating paths
Choose a ready-to-use package, refine a configurable package, or move to a managed data supply program. The path changes the starting scope—not the need for inspectable records and dependable delivery.
Select the closest source, schema, history, and refresh profile, inspect sample records, and move into ingestion with fewer design decisions.
Refine the source universe, fields, markets, languages, historical window, refresh cadence, and delivery while retaining a clear package baseline.
Use a dedicated operating plan when the program requires custom sources, matching, enrichment, quality rules, or delivery orchestration.
WebScrapingAPI operates collection, quality monitoring, source-change maintenance, versioning, and delivery so your team can focus on the model workflow and acceptance criteria.
Format and refresh
The package handoff joins records with the context your data platform needs: a defined format, partitions, schema version, observation time, file manifest, and refresh behavior.
Preserve nested content, arrays, and record context for application and document workflows.
Move flat entities and observations into familiar analytical and warehouse workflows.
Use typed, columnar files for analytical processing and data-lake ingestion.
Product choice
AI Data Packages sit between the open catalog and a fully custom program. Choose the adjacent product that matches how much source selection, collection, and transformation your team wants to own.
Browse prepared collections across business domains and data types.
Find dated public-page snapshots for backfill and time-aware research.
Evaluate source, record, history, refresh, and delivery around an AI workload.
Have our team collect, validate, maintain, and deliver a recurring data feed.
Request supported source-specific structured results from your application.
Collect related eligible public pages through one bounded crawl.
Planning the broader AI data stack? Connect packages to training, RAG, evaluation, agent access, and multimodal delivery.
Explore Data for AIEvaluation and pricing
Begin with a representative sample and a bounded scope. Pricing follows the data that creates model value: source coverage, record volume, history, transformations, refresh cadence, and delivery.
Test retrieval, chunking, joins, transformations, labels, or benchmark logic on representative records before you size the full supply.
FAQ
Use these answers to align the model workload, sample, package scope, operating path, and delivery plan.
An AI Data Package is a prepared web data asset organized around a model workload. It brings the source scope, sample records, schema, history, refresh cadence, quality context, and delivery format into one evaluation path.
Packages can support training and fine-tuning, RAG knowledge bases, evaluation sets, agent grounding, and multimodal programs. The right package begins with the model task, the evidence it needs, and the way that evidence will be consumed.
Yes. Review representative sample records with the data dictionary, source context, timestamps, field presence, and missing states so your model and data teams can test the package against a real ingestion or evaluation workflow.
Configuration can shape the source universe, entities, markets, languages, fields, historical window, refresh cadence, format, and delivery destination. Start from the closest package and refine only the dimensions that change model value.
Packages can be delivered as JSON, CSV, or Parquet for one-time ingestion or recurring refreshes. The delivery includes a clear file organization, schema version, timestamps, and manifest for dependable downstream processing.
The refresh plan defines which records are collected again, how new and changed records are represented, and when each delivery arrives. WebScrapingAPI handles collection monitoring, source-change maintenance, quality checks, versioning, and delivery.
Choose Managed Data when your program needs a purpose-built source mix, custom matching or enrichment, specialized quality rules, or an operated delivery workflow beyond the closest package. Our team runs the full data supply against your production brief.
Your AI data brief
Bring the workload, target evidence, source universe, historical window, refresh need, and destination. We will map it to the closest package and prepare a representative evaluation path.