Web data infrastructure for AI agents + LLM training
Structured web data for AI agents, RAG pipelines, and LLM training. Provenance-tagged, refreshed on your schedule, piped straight into your data stack.
- Amazon
- GitHub
- Wikipedia
- HackerNews
- ProductHunt
- Crunchbase
- LinkedIn pages
- Booking.com
- Real estate portals
- Job boards
- Documentation sites
Where AI teams hit the data infrastructure wall
Three structural gaps every ML team or AI startup eventually hits.
- MonthsLag 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 The model hallucinates last quarter's prices, last year's specs, and outdated facts. Data refreshed on a schedule is the unlock.
- Raw HTMLGeneric 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.
- 0Source 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.
What Scrapewise actually does for AI teams
Three things, simply: structured output, scheduled 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.
Fresh data for live agents
Schedule runs per source or start one on demand through the API or MCP server. 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.
How an AI team gets up and running
Five steps from blank canvas to fresh structured data flowing into your training or agent stack.
- 01
Define your data sources
Any public URL — competitor catalogues, marketplaces, news sites, documentation, forums. No fixed source list.
- 02
Configure scrape templates
Define your output schema — fields, types, validation rules. Structured JSON or CSV per source.
- 03
Set the refresh schedule
Daily or on demand for live agents, weekly for training, monthly for embeddings refresh, per source.
- 04
Pipe into your stack
REST API and MCP server. Load into S3, BigQuery or Snowflake with your own pipeline, then LangChain, LlamaIndex, Pinecone, Weaviate or your custom RAG pipeline.
- 05
Layer provenance + audit logs
Source URL + scraped_at timestamp on every row. Your training data has receipts.
What you can build on top of Scrapewise AI data
Each AI team need maps to a specific Scrapewise use case.
- Industry needFeed live agents with fresh web dataUse caseData for AI and LLMsRead more →
- Industry needPull structured product data for trainingUse caseProduct data extractionRead more →
- Industry needBuild market intelligence datasetsUse caseE-commerce market researchRead more →
- Industry needTrack competitive pricing for pricing modelsUse caseCompetitor price trackingRead more →
- Industry needPipe travel + real estate data into agentsUse caseTravel + hospitality data extractionRead more →
AI data infrastructure that doesn't get in the way
Structured output, schema-validated
JSON or CSV per source, validated against your schema. Drop into vector DBs or training pipelines without preprocessing.
Scheduled + on-demand refresh
On-demand runs for agent queries, scheduled snapshots for training. Predictable cost, predictable freshness.
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.
- ✕ 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
- ✓ 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
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
Warehouse, via REST API
BigQuery
Warehouse, via REST API
S3
Storage, via your pipeline
MCP server + REST API
Custom integrations
Connect Claude Code or Claude Desktop to the Scrapewise MCP server with an API key from Settings > API Keys.
Adjacent capabilities for AI teams
Use cases, comparisons, and reading for ML engineers and AI startup teams.
Use cases
Compare alternatives
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, provenance-tagged data layer they actually need, refreshed on their schedule.
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.