Data for AI

Fresh web data for AI models and agents

Collect, normalize and refresh public web datasets for training, evaluation, RAG and business automation.

Real-timeCollection for fresh public web signals
RepeatablePipelines for dataset refresh and evaluation
StructuredOutputs ready for model and warehouse workflows
ScalableProxy-backed access for large jobs

Infrastructure

Data pipelines for model teams

Use collection APIs and proxy access to build datasets that stay fresh.

Training and fine-tuning

Collect domain-specific public data for model improvement and evaluation.

RAG enrichment

Refresh knowledge sources with current pages, listings, reviews and search results.

Agent workflows

Give automation systems reliable access to the public web data they need.

Quality controls

Use stable schemas, repeatable jobs and reviewable usage records.

Workflow

AI data collection flow

01

Define target sources

Choose domains, search surfaces or data categories.

02

Collect through APIs

Run jobs through proxy-backed scraper and SERP infrastructure.

03

Normalize records

Convert web pages into structured rows and metadata.

04

Refresh datasets

Repeat collection and monitor usage inside the console.

Resources

AI data resources

Use these guides to decide how to structure data collection for model workflows.
Guide

Dataset refresh strategy

Plan recurring collection windows for model freshness.

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Checklist

Public web data QA

Validate coverage, duplication and schema stability.

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