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KI-Training & -Erdung

Build the corpus. Refresh the context.

Build versioned public-web corpora for training and evaluation. Keep RAG and agent context current through APIs, scheduled collections, or a managed pipeline shaped around your sources, schema, and update policy.

Scope your AI data pipelineKI-Datengrundlagen erkunden

Start with the workload

Choose the data operation your AI workload needs.

A useful data plan begins with what the system must do, then defines the source universe, record shape, and update behavior for that job.

Batch data operation

Build a corpus another team can reproduce.

Define the source list, capture window, inclusion rules, schema, normalization, duplicate policy, and dataset version before scaling volume.

  • Domain-specific pre-training or fine-tuning inputs
  • Versioned bulk records with source and capture metadata
  • Quality and gap reporting against agreed acceptance rules
Plan a corpus sample

AI data foundations

Start with the data supply your workload needs.

Choose a ready dataset, historical web snapshot, multimodal collection, request-time API, or fully operated program. Each path keeps the source, record shape, refresh pattern, and delivery responsibility clear.

Multimodale Ausbildung

Videodaten für KI

Build scenario-focused collections of video clips, audio, transcripts, and metadata for video language models, multimodal systems, and physical AI.

Explore video data
Physical AIVLA-Videodaten

Scenario-focused clips with action, environment, point-of-view, and time-boundary context.

Ready and configurableKI-Datenpakete

Evaluate package families by sample records, schema, history, refresh cadence, and delivery.

Ready-to-useDatenmarktplatz

Browse structured datasets with clear source, field, freshness, format, and delivery information.

Recurring contextDatenfeeds

Keep supported structured records current through a defined refresh cadence and delivery path.

Historical contextWebarchiv

Backfill knowledge bases and research timelines from preserved public-page snapshots.

Operated deliveryVerwaltete Daten

WebScrapingAPI handles collection, extraction, quality monitoring, source maintenance, and delivery.

Record designBilder, Video & Audio

Inspect the media object, renditions, transcripts, metadata, and provenance your pipeline receives.

Request-time collectionBring live public-web context into your own pipeline.

Use Data-API for supported structured sources, plan a bounded multi-page job with Crawl-API, or compare every agent-facing access path in one place.

Explore Agentic Web Access

Two data horizons

Training and grounding need different data specifications.

They can use the same source foundation, but the collection moment, record unit, quality focus, and change policy should not be interchangeable.

01 · Controlled snapshot

Training & evaluation corpus

A defined set that can be reproduced, reviewed, versioned, and compared across model or pipeline iterations.

Collection
Fixed capture window or agreed batch.
Record unit
Document or structured observation with a stable key.
Quality focus
Coverage, schema, duplicate handling, parse states, and manifest integrity.
Change policy
A new dataset version is issued when the specification or source snapshot changes.
Downstream
Your team owns labeling, tokenization, training, evaluation, and model behavior unless separately scoped.

02 · Refreshable context

Grounding, RAG & agents

A defined source collection refreshed on an agreed schedule, or a supported page retrieved when the application requests it.

Collection
Scheduled refresh or request-time retrieval.
Record unit
Source-linked content or structured response with capture context.
Quality focus
Freshness, retrieval state, provenance, changed content, and observable gaps.
Change policy
New, changed, unchanged, and unavailable states can be represented in the delivery plan.
Downstream
Your team owns chunking, embeddings, indexing, retrieval logic, citations, and response behavior unless separately scoped.

Record specification

Every record should explain where and when it came from.

“AI-ready” is not a file extension. A useful record specification defines provenance, capture context, schema, versioning, quality states, and known gaps before the data enters a model workflow.

01
Observed content stays distinct

Keep collected fields separate from normalized, inferred, summarized, or generated values.

02
Version keys make change visible

Use a stable record key and content checksum to identify updates without hiding the prior state.

03
Gaps are part of the delivery

Report unavailable, failed, excluded, or needs-review states instead of silently dropping them.

illustrative_record.jsonlschema 1.4
// Synthetic example for specification review
{
  "record_id": "doc:7d3b:20260716",
  "source_url": "https://docs.example.com/deploy",
  "captured_at": "2026-07-16T08:30:00Z",
  "requested_context": {
    "locale": "en-US",
    "market": "global"
  },
  "content_type": "technical_article",
  "title": "Regional inference deployment",
  "content": "Observed page content…",
  "content_hash": "sha256:b81c…8a0e",
  "previous_hash": "sha256:2f10…c143",
  "record_state": "changed",
  "quality_state": "accepted",
  "quality_flags": [],
  "source_review_state": "scoped",
  "use_note_ref": "scope:ai-grounding-pilot",
  "schema_version": "1.4",
  "scope_id": "ai-grounding-pilot"
}

Shape fields, transformations, format, retention, and delivery around the workload.

Identity & provenance

Stable record key, source URL, source class, request context, and capture time.

Observed content

Selected page content or structured public fields, kept distinct from downstream transformation.

Version & state

Schema version, content checksum, prior checksum, and new, changed, unchanged, unavailable, or review state when scoped.

Quality & gaps

Extraction status, required-field checks, duplicate state, exclusions, and failed collection context.

Governance controls

Source scope, review state, retention, access, and intended-use references travel with the delivery so reviewers can trace each record through the workflow.

01Source eligibility

Requested source, access state, and project scope are reviewed before expansion.

Scoped
02Retrieval state

Successful, unavailable, excluded, or failed collection remains observable.

Reported
03Schema conformance

Required fields, types, and format checks follow the agreed record definition.

Checked
04Field fidelity

The acceptance plan defines how sampled records are reconciled with source observations and how discrepancies are classified.

Defined
05Duplicate handling

Stable keys and content hashes support the selected duplicate rules.

Tracked
06Change detection

New and changed content can be distinguished when history is in scope.

Versioned
07Delivery integrity

File, batch, or response delivery is checked against the selected output definition.

Verified

Measurable quality

Measure freshness and gaps—not “AI readiness.”

The quality scorecard changes with the workload. A batch corpus needs reproducibility and manifest integrity; a grounding collection needs observable freshness and change states.

For training & evaluation

Define capture window, source coverage, required-field completeness, source-to-record sample checks, discrepancy states, duplicate policy, parse failures, record counts, and dataset version.

For grounding & retrieval

Define last successful capture, refresh window, changed and unavailable states, provenance coverage, source-to-record sampling, failed retrievals, and late-delivery handling.

Use the scorecard to separate collection quality from model quality: inspect source coverage, freshness, versions, and delivery first, then measure retrieval, citation, and application results in your evaluation suite.

Refresh data without hiding what changed.

When change tracking is part of the specification, each collection can compare a stable record key and content checksum. The result is an explicit state—not an overwritten file with no history.

Snapshot 01First observed version

Source, capture time, content, schema, and checksum establish the baseline.

state: new
Refresh 02Content unchanged

The page remains available and the content checksum matches the prior capture.

state: unchanged
Refresh 03New version observed

The record retains its identity while the changed content receives a new checksum.

state: changed
Refresh 04Source not observed

A missing response is classified as unavailable, excluded, or failed according to the agreed policy.

state: unavailable | excluded | failed

Operating model

Select the handoff that matches your AI data operation.

Use infrastructure and APIs when your team owns corpus preparation, or hand off a defined collection program. Model and retrieval behavior remain downstream.

Maximum collection control

Keep your crawlers and corpus pipeline.

WSA supplies proxy access. Your team operates retrieval, rendering logic, parsing, normalization, duplicate handling, quality, source-change maintenance, delivery, and every downstream AI step.
VerantwortungOwner
Proxy network accessWSA
Targeting, rotation/session strategy & requestsIhr Team von Experten
Crawling, rendering & extractionIhr Team von Experten
Normalization, quality, versioning & source maintenanceIhr Team von Experten
Chunking, embeddings, training & evaluationIhr Team von Experten

Best for teams with mature collectors and their own data-quality operation.

Explore proxy infrastructure

Source governance

Scale AI data with the source context intact.

Bring source access, intended use, data categories, retention, and jurisdiction into the collection plan before volume grows.

01Source eligibility

Select publicly accessible sources, access conditions, geography, and explicit exclusions before scale.

02Data categories

Define the fields and content types needed; minimize personal or sensitive data that the workload does not require.

03Intended use

Keep each delivery aligned to its defined training, evaluation, retrieval, or agent workload.

04Retention & deletion

Set the storage period, access, update, removal, and deletion workflow for the project.

05Observed vs. generated

Keep collected content distinguishable from translation, summarization, inference, labeling, or other generated transformations.

Clear operating split: WebScrapingAPI runs the selected collection and delivery layer. Your team applies the data within its model and application workflow, with any added transformations defined in the project plan.

Evaluation process

Prove the data shape before you scale.

Start with representative sources and records. The pilot should expose fields, provenance, gaps, update behavior, and ownership before production volume.

01 · Define

Name the workload

Set the AI job, representative sources, markets, content types, capture window, and intended downstream use.

02 · Sample

Inspect real records

Review source context, observed fields, quality states, exclusions, and a representative output shape.

03 · Specify

Define the record

Set schema, versioning, cadence, checks, gap handling, retention, destination, and operating model.

04 · Operate

Launch and maintain

WSA operates the selected layer and reports against the collection and delivery rules in the project plan.

A representative sample helps assess feasibility and data shape for the sampled sources. After sample review, the production proposal defines the launch sequence, capacity assumptions, refresh plan, and dependencies. Timing depends on source complexity and the agreed scope; the sample is not a promise of full-source coverage or model performance.

Evaluation questions

What AI data buyers ask before launch.

Sources, provenance, formats, updates, ownership, responsible use, and commercial scope—answered directly.

What kinds of AI workloads can this data support?

The solution can support training and evaluation datasets scoped to defined sources and intended uses, recurring knowledge-base or RAG collections, and request-time access for agent workflows. Each needs its own source, schema, quality, refresh, and ownership specification.

Which sources, markets, and content types can be included?

Use this page to plan text and structured-web workloads across training, evaluation, RAG, and agents. For video clips, audio, transcripts, and media metadata, continue to Video Data for AI; the multimodal data guide explains the underlying media record. Source and market coverage is shaped around the selected product or managed program.

Can source attribution and capture context be preserved?

Yes. Add source URL, capture time, locale or market, content type, schema version, record key, collection state, and quality flags to the record specification so provenance travels with every delivery.

Which output formats and delivery methods are available?

Use documented API responses for request-time access and an agreed file or batch delivery for scheduled programs. Current API formats are listed in the product documentation. For managed delivery, record format, packaging, batch size, compression, and destination are confirmed during scoping; the JSONL record shown here is illustrative, not a default format promise.

How are page changes, removals, and failed refreshes represented?

When history and change tracking are part of the delivery, stable keys and checksums distinguish new, changed, unchanged, unavailable, excluded, and failed states. Choose full snapshots, incremental updates, or change-only delivery to match the downstream workflow.

Does WebScrapingAPI chunk, embed, index, label, or train the data?

The standard delivery includes collection and structured output. If you also need chunking, embeddings, indexing, labeling, or another transformation, add it to the project brief so the output arrives ready for your pipeline.

Who maintains collection when a source changes?

WSA maintains documented endpoints and the custom connectors it operates for scheduled or managed delivery. Proxy customers maintain their own collectors; web-access API customers maintain their custom extraction, corpus preparation, and downstream pipeline.

How do we assess sources for an AI data program?

Start with public accessibility, then review source terms, data categories, intended model use, retention, and jurisdiction. We record the selected source scope and exclusions in the collection plan.

Can this service improve model accuracy or eliminate hallucinations?

Better inputs make evaluation easier. WSA provides source context, freshness, versioning, and observable quality states so your team can measure how the data changes retrieval, citation, and application results.

What determines scope and cost?

Self-serve products follow published plans. Custom scope reflects source count and complexity, record volume, markets, capture window, cadence, fields, transformations, duplicate and version policy, quality checks, history, retention, format, and delivery.

Where can procurement review data handling and service terms?

Review our Datenschutzerklärung - 1001-Spiele, GDPR information, and Servicevereinbarung für Kunden. For a custom delivery, the project plan records source scope, data handling, retention, access, change handling, and delivery roles.

Validate the data brief

Define the sources, freshness, and schema your AI system actually needs.

Share representative sources, workload type, markets, output requirements, and update window. We will assess feasibility and propose the right collection boundary.

Scope your AI data pipelineKostenlos starten