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Built forAI & Data Teams

Web data infrastructure for AI agents + LLM training

Real-time structured web data for AI agents, RAG pipelines, and LLM training. Provenance-tagged, refreshed on your cadence, piped straight into your data stack.

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Real-timeFresh data for live agents
StructuredSchema-validated output
ProvenanceSource URL + timestamp per row
Any URLNo fixed source list
Common targets for AI training
  • Amazon
  • GitHub
  • Wikipedia
  • Reddit
  • HackerNews
  • ProductHunt
  • Crunchbase
  • LinkedIn pages
  • Booking.com
  • Real estate portals
  • Job boards
  • Documentation sites
Industry problems

Where AI teams hit the data infrastructure wall

Three structural gaps every ML team or AI startup eventually hits.

  • Months
    Lag in typical training datasets

    Stale data → hallucinations

    Most AI teams scrape once, train, and ship. The model hallucinates last quarter's prices, last year's specs, and outdated facts. Real-time data is the unlock.

  • Raw HTML
    Generic scraping API output

    Cleaning eats more engineering than the agent

    Generic scraping APIs return raw HTML. Cleaning, dedup, and structuring it consumes more engineering than the actual agent build.

  • 0
    Source URL + timestamp by default

    No provenance, no accountability

    When a model cites wrong data, you need source URLs and timestamps for audit. Generic APIs don't surface this — Scrapewise outputs it natively.

How Scrapewise solves it

What Scrapewise actually does for AI teams

Three things, simply: structured output, real-time refresh, and provenance per row.

  • Structured output for RAG / training

    Schema-validated JSON or CSV — name, value, type, source URL, scraped_at. Drop directly into your vector DB or training pipeline.

  • Real-time refresh for live agents

    Configure scrape cadence per source. Live agents query fresh data; training pipelines pull weekly or monthly snapshots.

  • Provenance + audit trail per row

    Source URL and timestamp on every record. When models cite wrong data, you can trace and fix at the source.

Step-by-step

How an AI team gets up and running

Five steps from blank canvas to fresh structured data flowing into your training or agent stack.

  1. 01

    Define your data sources

    Any public URL — competitor catalogues, marketplaces, news sites, documentation, forums. No fixed source list.

  2. 02

    Configure scrape templates

    Define your output schema — fields, types, validation rules. Structured JSON or CSV per source.

  3. 03

    Set the refresh cadence

    Real-time for live agents, daily for training, weekly for embeddings refresh — per source.

  4. 04

    Pipe into your stack

    REST API, webhooks, S3, BigQuery, Snowflake. Connect to LangChain, LlamaIndex, Pinecone, Weaviate, or your custom RAG pipeline.

  5. 05

    Layer provenance + audit logs

    Source URL + scraped_at timestamp on every row. Your training data has receipts.

Use case mapping

What you can build on top of Scrapewise AI data

Each AI team need maps to a specific Scrapewise use case.

BENEFITS

AI data infrastructure that doesn't get in the way

Structured output, schema-validated

Structured output, schema-validated

JSON or CSV per source, validated against your schema. Drop into vector DBs or training pipelines without preprocessing.

Real-time + scheduled refresh

Real-time + scheduled refresh

Live data for agent queries, scheduled snapshots for training. Predictable cost, predictable freshness.

Provenance + anti-bot built in

Provenance + anti-bot built in

Source URL + timestamp per row. Cloudflare + DataDome bypass handled. Your team builds agents, not infrastructure.

From Building Scrapers to Building Agents

AI teams using Scrapewise stop spending engineering time on scraping infrastructure. The data flows in clean, the agents query fresh data, and provenance is built in.

✕ Generic Scraping API + Custom Cleanup
  • ✕ Engineering time consumed by scraper + cleanup pipelines
  • ✕ Raw HTML output requires custom parsing per source
  • ✕ No provenance — model hallucinations are unfixable
  • ✕ Anti-bot bypass built and maintained in-house
  • ✕ Per-request pricing makes large-scale training expensive
VS
✓ With Scrapewise
  • ✓ Engineering time goes back to agent + model work
  • ✓ Structured JSON / CSV per source, validated
  • ✓ Source URL + timestamp on every row
  • ✓ Cloudflare + DataDome + JS rendering handled
  • ✓ Predictable pricing for large-scale training pipelines
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Integrations

Plugs into the AI + data stack you already run

Pipe Scrapewise data into your training pipelines, vector DBs, and agent frameworks.

  • LangChain

    Agent framework

  • LlamaIndex

    RAG framework

  • Pinecone

    Vector DB

  • Weaviate

    Vector DB

  • Snowflake

    Data warehouse

  • BigQuery

    Data warehouse

  • S3 + Parquet

    Object storage

  • Webhooks + REST API

    Custom integrations

Native MCP support for connecting Scrapewise data directly to Claude, GPT, and custom agents.

Go deeper

Adjacent capabilities for AI teams

Use cases, comparisons, and reading for ML engineers and AI startup teams.

Use cases

Compare alternatives

AI + scraping reading

Your Agent Just Cited Stale Data. Did Your Team Notice?

Stop building scraper infrastructure on top of building the agent. Scrapewise gives AI teams the structured, real-time, provenance-tagged data layer they actually need.

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FAQ

Frequently Asked Questions

Common questions from ML engineers, AI startup founders, and data teams.

Yes. Output is structured JSON or CSV with field-level validation, source URL, and timestamp — drop directly into Pinecone, Weaviate, or your custom vector DB.

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