Crunchbase News Scraper: Data Extraction and Integration Guide

The Crunchbase News Scraper extracts structured editorial articles directly from news.crunchbase.com, delivering fields such as headlines, stripped content bodies, publication timestamps, author metadata, category names, tags, and featured images. The scraper interacts with public WordPress REST API endpoints without requiring proxies, cookies, or account credentials. This guide outlines how to configure query parameters, organize repeatable extraction workflows, and deploy startup funding reports and market intelligence into operational pipelines.

Define your operational scope before querying

Before running extractions, team pipelines must distinguish between Crunchbase News editorial reporting and the gated Crunchbase corporate database. The primary domain at crunchbase.com houses structured organization profiles, comprehensive financing histories, investor portfolios, and executive directories. That environment is gated behind authentication mechanisms and Cloudflare protections, meaning it cannot be scraped without authenticated session cookies.

In contrast, news.crunchbase.com operates on WordPress and provides editorial reporting through a public REST API at /wp-json/wp/v2/posts. The news portal maintains more than 8,500 indexed articles covering venture capital developments, initial public offerings, mergers and acquisitions, and funding round summaries. Clarifying this architectural difference ensures teams expect editorial article text, summaries, and categorized metadata rather than raw entity tables.

Practical use cases

Automated extraction from the news site supports targeted venture tracking, macroeconomic industry synthesis, and automated editorial feeds.

Use case 1: Funding round monitoring

• Outcome: Track venture capital transactions, early-stage seed rounds, and growth-stage investments announced across targeted technology sectors.
• Question to answer: Which early-stage and growth companies secured capital in our target sector within the specified calendar window?
• Configure: Enter targeted round stages in search such as "Series A" or "Seed". Set category to an industry slug such as "ai" or "fintech-ecommerce", populate after with an ISO date (such as "2026-01-01"), and configure maxItems to your needed sample size up to 1000.
• Working method: Review matching records by evaluating title and excerpt strings for announced investment sizes, stage tiers, and participating lead investors mentioned within the journalistic text. Map tagNames and categoryNames arrays directly into internal deal-tracking registries to document emerging startups.
• Deliverable: A structured deal alert sheet containing publishedDate, title, url, authorName, and investment details extracted from content.
• Stop condition: Extraction concludes when the scraper finishes paginating through matching posts or reaches the configured maxItems ceiling.

Use case 2: Market analysis

• Outcome: Aggregate sector-wide reporting to evaluate venture capital deployment patterns, quarterly liquidity events, and shifts in tech sentiment.
• Question to answer: How has editorial reporting on initial public offerings, liquidity, and venture valuations shifted across specific quarters?
• Configure: Leave search empty to ensure full thematic capture. Set category to macro taxonomy slugs such as "venture", "ipo", or "liquidity". Provide an ISO 8601 date string in after to target designated quarterly windows, and set maxItems to a large volume such as 500.
• Working method: Parse the 5,000-character truncated content body across extracted records. Aggregate recurring keywords across tagNames to evaluate topic momentum over time. Track reporting frequency by grouping records by publishedDate to assess shifts in sector coverage volume.
• Deliverable: A quarterly market narrative brief featuring aggregated tag frequencies, editorial analysis summaries from excerpt, and direct reference links from url.
• Stop condition: Processing terminates when the pagination hits the earliest publication date within the quarter or exhausts the maxItems limit.

Use case 3: Content syndication

• Outcome: Deliver verified tech journalism and venture news summaries directly into internal team dashboards, research hubs, or external industry newsletters.
• Question to answer: What are the top published stories across tech verticals that should be redistributed to our audience today?
• Configure: Set category to a broad vertical slug such as "startups" or "saas". Leave search empty to avoid keyword bias. Set after to the preceding calendar day (for example, "2026-03-29"), and set maxItems to 50.
• Working method: Ingest the flat JSON records directly into publishing queues. Map title to headlines, excerpt (~500 characters) to summary cards, featuredImageUrl to visual embeds, and url to canonical attribution links. Ensure author attribution is maintained using authorName.
• Deliverable: An automated feed payload ready for publication, complete with article metadata, image links, author attribution, and clean HTML-stripped text.
• Stop condition: Runs on a recurring schedule and stops after retrieving all articles published after the previous execution timestamp.

Execute a controlled workflow step by step

Following a systematic deployment process prevents unnecessary duplicate requests and guarantees downstream data cleanliness.

1. Define target taxonomy and keywords: Browse news.crunchbase.com to identify category URLs. Note canonical slugs such as clean-tech-and-energy, cybersecurity, crypto, or robotics. Identify whether aliases (such as artificial-intelligence or fintech) map cleanly to target topics.
2. Initialize run parameters: Open the Crunchbase News Scraper console. Enter your target keyword in search, select an appropriate category slug, provide a cutoff date in after, and set an initial exploratory maxItems value (such as 5 to 10 items).
3. Execute test extraction: Run the scraper. Because news.crunchbase.com exposes public endpoints at /wp-json/wp/v2/posts, the scraper communicates directly from standard datacenter IPs without requiring proxies, browser emulation, or session tokens.
4. Verify schema alignment: Inspect the run dataset. Ensure all 16 standardized fields are present, checking that HTML stripping functioned correctly on title, excerpt, and content.
5. Scale extraction volume: Once output fidelity is verified, increase maxItems up to the maximum permitted value of 1000 for historical sweeps or production ingest schedules.
6. Integrate into downstream storage: Route the resulting JSON dataset via webhook, API export, or cloud synchronization to your data warehouse or news feed.

Configure available parameters

The scraper accepts four configuration parameters that control query filtering and pagination:

• search (String): Free-text keyword that matches across article titles and full article body text. Examples include "AI", "Series A", or "IPO".
• category (String): Category slug used to filter posts. Verified working slugs include venture, startups, business, ai, public, fintech-ecommerce, health-wellness-biotech, cybersecurity, transportation, clean-tech-and-energy, crypto, ma, data, liquidity, ipo, media-entertainment, seed, enterprise, agtech-foodtech, web3, real-estate-property-tech, semiconductors-and-5g, robotics, retail, and saas. Friendly aliases like artificial-intelligence, fintech, health, and m&a are automatically mapped to canonical slugs.
• after (String): Publication cutoff date formatted as YYYY-MM-DD or full ISO 8601 string. The scraper only returns articles published after this timestamp.
• maxItems (Integer): Maximum number of articles to return. The field defaults to 50, has an input prefill of 5, and accepts values between 1 and 1000.

Transform payload records into deliverables

Each article record returned by the scraper contains exactly 16 output fields structured in a flat schema with typed defaults. The scraper replaces missing values with empty strings, zeros, or empty arrays rather than null values.

• Identity fields: id (Integer), url (String), slug (String), and title (String with HTML stripped).
• Content fields: excerpt (Short summary stripped of HTML, approximately 500 characters) and content (Full article text stripped of HTML and truncated to 5,000 characters).
• Dates: publishedDate (ISO 8601 publication timestamp) and modifiedDate (ISO 8601 last modified timestamp).
• Author and taxonomy: authorId (Integer), authorName (String), categoryNames (Array of Strings), categoryIds (Array of Integers), and tagNames (Array of Strings with pre-resolved taxonomy labels).
• Media: featuredImageUrl (String containing the image asset link) and featuredImageId (Integer).
• Metadata: scrapedAt (ISO 8601 timestamp generated at extraction time).

When loading these records into analytical databases or downstream dashboards, mapping can be performed directly without pre-parsing HTML tags or decoding raw category ID numbers.

Quality controls before anyone uses the result

To prevent data degradation in downstream feeds or analytical models, implement standard validation checks before consumption:

• Verify truncation handling: The content field is capped at 5,000 characters. If downstream natural language tasks require unabridged text for long-form investigative pieces, confirm whether key data points fall within the 5,000-character boundary or can be supplemented via the excerpt.
• Audit typed defaults: Because the scraper uses typed defaults (zero for missing integer IDs, empty strings for missing URLs or text, empty arrays for missing taxonomies), ensure your ingestion scripts treat empty strings as absent values rather than genuine content.
• Validate category mapping: When relying on category filters, verify whether multi-category stories caused unexpected cross-sector records to appear in results. Inspect categoryNames on each returned item to confirm secondary categorizations.
• Check date boundaries: Ensure the after filter matches your target ingestion window and that timezone shifts in ISO 8601 publication dates do not exclude stories published near midnight UTC.

Operational limits

• Pagination increments: The scraper requests records in batches of 100 posts per page, which is the maximum page size supported by the WordPress REST API. It traverses pages sequentially until maxItems is satisfied.
• Dataset ceiling: The maximum number of records requested in a single execution run is 1000 items.
• No proxy requirements: Requests target the public REST API endpoint directly. Datacenter IP addresses execute queries without requiring proxy rotation, CAPTCHA solvers, or browser rendering engines.
• News domain exclusivity: The scraper does not interact with the gated directory on crunchbase.com. Company cap tables, private executive contact records, and structured database search tables cannot be retrieved through this actor.

Frequently asked questions

How should I validate my initial data extraction?

Run a test execution using the default maxItems prefill of 5 records. Inspect the returned JSON payload to confirm that the title, excerpt, and content fields contain clean, HTML-stripped text, and verify that the tagNames and categoryNames arrays return human-readable taxonomy names rather than empty lists.

How do I handle missing optional fields?

The scraper does not output null values. Missing strings default to "", missing integer IDs default to 0, and missing taxonomies default to []. Ingest logic should check for empty strings or zero values when identifying unassigned authors or missing featured images.

When is it appropriate to scale up batch sizes?

Scale maxItems from small exploratory runs (5 to 50 items) up to the maximum limit of 1000 when performing broad historical backfills, quarterly sector reviews, or multi-year tag trend analysis.

What conditions should trigger a workflow review?

Initiate a review if scheduled runs return zero items despite recent news activity, if target category slugs fail to match incoming stories due to upstream taxonomy adjustments on news.crunchbase.com, or if content requirements exceed the 5,000-character truncated limit.

Can this scraper extract private funding tables from the main Crunchbase database?

No. The scraper only extracts editorial articles published on news.crunchbase.com. The primary crunchbase.com company database is gated behind authentication and Cloudflare protection, which this scraper does not access.

Resources

• Crunchbase News Scraper on Apify - Execution console, input parameters, and data schema
• Crunchbase News Portal - Source website for editorial articles and category directories
• WordPress REST API Reference - Documentation for the underlying /wp-json/wp/v2/posts endpoint