CrawlKit Platform Model

CrawlKit is mission control for hard-to-get data: acquisition workbenches on top of a trustworthy data engineering backbone. It turns live web crawls, deterministic spiders, scrape jobs, ETL, social signals, synthetic generation, and domain applications into governed, queryable, lineage-backed data assets.

Canonical product frame: CrawlKit is a data engineering backbone plus capability workbenches plus downstream applications. The backbone creates trust; the workbenches create power; downstream apps prove the model in real domains.

The operating loop: Acquire → Extract → Refine → Serve → Prove

Acquire
  Live web crawls, spiders, scrape jobs, frontier queues, stealth/proxies,
  WARC/Common Crawl, file/API ETL, social monitors, OpenRate MRF/TiC ingestion.

Extract
  Declarative schemas, selectors, vertical templates, content classification,
  parser stages, field processors, and deterministic structured records.

Refine
  Enrichment, taxonomy, scoring, deduplication, decontamination, quality gates,
  validation, outlier detection, and synthetic verification.

Serve
  Datasets, query views, DataFusion SQL, DuckDB hybrid SQL, Arrow Flight,
  streams, exports, reports, training formats, and API applications.

Prove
  Durable workflow runs, lineage, policy, consent, audit, telemetry,
  evidence drawers, governance approvals, and measurement feedback loops.

The backbone

The backbone is the persistent trust layer that every capability writes into. It prevents CrawlKit from becoming a pile of scraping scripts by making every output inspectable, replayable, governable, and usable by other systems.

Backbone objectWhy it matters
Dataset / AssetThe durable product surface for rows, tables, views, artifacts, rates, mentions, pages, and generated data.
Schema / ContractStructural and quality expectations that make data safe to query, serve, export, or train on.
Run / WorkflowRun / JobThe durable execution record for crawls, scrapes, ETL, pipelines, streams, synthetic runs, and domain pipelines.
LineageSource → job → transform → dataset → query/view/export/application provenance.
Quality resultGate outputs before they reach production, reports, downstream apps, or AI training.
Policy / Consent / AuditGovernance for robots, source consent, sensitive fields, approvals, production changes, and accountability.
Query / Index / Flight endpointConsumption surfaces for SQL, semantic search, Arrow-native clients, exports, and application APIs.
Measurement / TelemetryThe learning loop: operational evidence, adoption analytics, attribution, calibration, and observability.

The workbenches

Ingest

Crawls, Spider Studio, scrape jobs, frontier, stealth/proxies, WARC/Common Crawl, ETL, social sources, and domain sources.

Extract

Selectors, processors, extraction schemas, vertical templates, page handlers, content type detection, and deterministic records.

Refine

Enrichment, taxonomy, scoring, quality lab, dedup, decontamination, indexes, and measurement-ready signals.

Build

Workflow templates, pipeline DAGs, operation registry, schedules, AI plans, durable runs, worker progress, and lineage emission.

Query

SQL workbench, views, DataFusion, DuckDB hybrid, schema describe, stats, exports, indexes, and semantic search.

Serve

Exports, Arrow Flight, streams, training formats, reports, dataset cards, and downstream application APIs.

Quality

Contracts, gates, profiles, drift, deduplication, quarantine, taxonomy checks, and benchmark decontamination.

Governance

Policy, consent, robots/politeness, approvals, audit, sensitive fields, role scoping, and production safety.

Every object shows the Backbone Strip

Every capability detail page should answer the same evidence questions: What source produced this? Which run changed it? What dataset or asset did it create? What quality/policy gates passed? What lineage proves it?

Source / Definition  →  Output Dataset / Asset  →  Latest Run
        →  Quality / Contract  →  Lineage / Impact  →  Policy / Audit

For example, a dataset detail view should show the spider or ETL run that created it, the latest run timestamp and row count, the quality gate pass/fail state, quarantined rows, upstream sources, downstream query views or applications, consent/policy status, and the audit trail for who changed what.

Applications and lenses

CrawlKit is not only a platform console. It also powers downstream applications and applied lenses.

LayerExamplesHow it connects back
Downstream applicationOpenRate healthcare pricing transparencyEvery displayed rate can link to source MRF/TiC file, parser run, enrichment run, quality checks, query view, and audit.
SEO/GEO lensSite audits, GSC, DNS, IndexNow, content briefsWeb crawl runs produce page datasets, issue datasets, content assets, and measurement feedback.
Social/revenue lensReddit/LinkedIn mentions, leads, campaigns, dealsMentions become rows, enrichment/scoring creates leads, campaigns/deals trace back to source signals.
ML training-data lensSynthetic data, quality filters, training formats, streamsGenerated data keeps seed lineage, verification, consent, quality, decontamination, and export records.
Operations lensTelemetry, alerts, healing, incidents, costRun/job failures and anomalies become evidence-backed incidents and safe remediation workflows.

OpenRate is the proof point

OpenRate is the first downstream application built on the CrawlKit backbone. It demonstrates how difficult public data becomes a user-facing product.

CMS / hospital MRF / payer TiC sources
  → discovery + change detection + resilient fetch
  → parse + normalize + enrich + validate
  → hospital/provider/payer/rate datasets
  → query views for search, compare, discrepancies
  → reports, exports, APIs, and evidence drill-down

Where to go next