The fastest-growing use of web data in 2026 is feeding AI. LLM fine-tuning, retrieval-augmented generation (RAG) and real-time AI agents all need clean, continuously refreshed external data - and most teams underestimate how much data infrastructure sits beneath a working AI feature.
This whitepaper sets out how to source and structure publicly available web data for AI use cases - responsibly, at quality, and at the freshness real-time AI demands.
Key takeaways
Why AI teams come to web data
Models are only as current and accurate as the data behind them. Static training sets go stale, and RAG systems are only as good as the external data they retrieve in the moment. That is why AI teams increasingly need managed, continuously refreshed web data feeds rather than one-off dumps.
This paper connects the AI requirement to the data engineering it actually demands - the part that is easy to underestimate.
Who this whitepaper is for
This whitepaper is for the teams building AI features that depend on external, real-world data.
Doing it responsibly
AI data sourcing raises real legal and quality questions. We focus on publicly available, non-personal data and encourage legal review - the same principles in our guide to web scraping law. To build an AI-ready data feed, see our Data as a Service offering.
- What AI training, fine-tuning and RAG pipelines need from web data
- Structuring and cleaning web data for model consumption
- Keeping data fresh enough for retrieval and real-time agents
- Quality and provenance - why messy data breaks AI quietly
- Staying on public, non-personal data and the compliance basics
Frequently asked questions
Yes. Enter your details and we will email you the PDF.
AI, ML and data teams building LLM fine-tuning, RAG pipelines or AI agents that depend on external web data.
Yes. It covers staying on publicly available, non-personal data and the compliance basics, and recommends legal review for your specific use case.