We extract residential addresses, phone numbers, background check metadata, and business directory listings from Whitepages. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for People Search objects from whitepages.com. All fields typed and schema-versioned.
"profile_id": "WP-94827163", "full_name": "John D Smith", "age_range": "40-49", "current_address": "123 Main St, Seattle, WA 98101", "past_addresses": "['456 Oak Ln, Portland, OR 97204']", "phone_numbers": "['206-555-0192']", "background_check_available": true
| # | profile_id | full_name | age_range | current_address | past_addresses | phone_numbers |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reverse Phone objects from whitepages.com. All fields typed and schema-versioned.
"phone_number": "206-555-0192", "line_type": "Mobile", "carrier": "T-Mobile USA", "owner_name": "John D Smith", "spam_score": "Low", "risk_level": "Safe", "search_timestamp": "2026-05-12T09:14:00Z"
| # | phone_number | line_type | carrier | owner_name | owner_address | spam_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reverse Address objects from whitepages.com. All fields typed and schema-versioned.
"address": "123 Main St", "city": "Seattle", "state": "WA", "zip_code": "98101", "property_type": "Single Family", "year_built": 1998, "current_residents": "['John D Smith', 'Jane Smith']"
| # | address | city | state | zip_code | current_residents | past_residents |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Business Listings objects from whitepages.com. All fields typed and schema-versioned.
"business_name": "Seattle Plumbing Co", "category": "Plumbers", "phone": "206-555-0987", "address": "789 Pine St, Seattle, WA 98101", "claim_status": "Claimed", "rating": 4.5, "review_count": 42
| # | business_name | category | phone | address | website | operating_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Public Records Metadata objects from whitepages.com. All fields typed and schema-versioned.
"profile_id": "WP-94827163", "criminal_records_flag": false, "bankruptcies_flag": false, "liens_flag": false, "judgments_flag": false, "traffic_records_flag": true, "last_updated": "2026-05-10T14:22:00Z"
| # | profile_id | criminal_records_flag | bankruptcies_flag | liens_flag | judgments_flag | traffic_records_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Whitepages scraper bypasses anti-bot protections to extract structured profiles, reverse lookups, and business directory data. Built for high-volume data pipelines.
Capture names, age ranges, current addresses, historical addresses, and known phone numbers from public people search results.
Input phone numbers to extract owner names, line types (mobile vs landline), carrier data, and spam risk scores.
Input addresses to extract current residents, historical residents, property types, and ownership information.
Extract business names, categories, contact details, operating hours, and claim status from the Whitepages commercial directory.
Capture listed family members and known associates to build identity graphs and connection networks.
Extract boolean flags indicating the presence of criminal records, bankruptcies, liens, or traffic violations on a profile.
Compile full address histories including cities, states, and zip codes to track relocation patterns over time.
Automated CAPTCHA solving and residential proxy rotation to maintain high extraction success rates against Whitepages protections.
Run bulk batch exports or continuous pipelines to monitor directory updates and new profile additions.
Brief in. Clean data out.
Provide lists of names, phone numbers, addresses, or geographic regions. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and CAPTCHA handling specifically for whitepages.com.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Whitepages employs strict rate limiting and CAPTCHA walls. Here is how we maintain reliable extraction pipelines.
Whitepages heavily blocks datacentre IPs. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and randomised request timing to mimic human search behaviour.
Frequent searches trigger CAPTCHA challenges. We integrate 2Captcha and CapSolver directly into the Playwright session to solve challenges automatically and maintain pipeline throughput.
Common names return thousands of results, but pagination is often capped. We use geographic and demographic sub-filtering to extract the full catalogue of matching profiles without hitting hard limits.
Whitepages frequently updates its DOM structure to break scrapers. Our selector strategy uses multiple fallback chains per field, ensuring layout changes do not break your data feed.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and CAPTCHA block rates, adjusting proxy pools automatically.
Fintech and compliance teams cross-reference user-provided details against historical public records to verify identities.
Sales teams enrich incomplete CRM records with accurate phone numbers and current residential addresses.
Risk models incorporate reverse phone lookup data to flag high-risk or VOIP numbers during account creation.
Investors use reverse address lookups to identify property owners and track historical residency patterns.
Agencies locate current contact information and known associates for skip tracing operations.
Marketing teams append demographic and location data to existing customer profiles to optimise targeting.
"Whitepages holds decades of historical contact and address data, but accessing it programmatically requires bypassing aggressive anti-scraping layers."
Extracting contact data at scale requires residential proxies, CAPTCHA solvers, and persistent session management. Whitepages aggressively blocks standard HTTP clients. DataFlirt handles the infrastructure complexity, delivering structured JSON directly to your data warehouse so your engineering team can focus on core product development.
Everything supported by our whitepages.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering and CAPTCHA interactions. Combined via scrapy-playwright middleware.
We maintain pools of US-based residential ISP proxies. Rotation happens per-request to avoid Whitepages rate limiting and IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About whitepages.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Whitepages is generally permissible under applicable law. DataFlirt targets only public, non-authenticated directory data. We do not extract gated background check reports, SSNs, or circumvent payment walls. Clients should review Whitepages ToS and consult legal counsel for specific use cases.
We use US residential ISP proxies and full Playwright browser sessions with realistic fingerprints. When CAPTCHAs appear, our automated solvers (CapSolver and 2Captcha) clear the challenge within the active session to continue extraction.
Yes. You provide a list of inputs via CSV or API, and our pipeline processes the reverse lookups in batch, returning the corresponding owner details, line types, and historical records.
No. Full criminal, financial, and civil background reports on Whitepages are premium gated content that requires a paid subscription and compliance checks. We extract the public metadata flags indicating if such records exist.
Our packages start at defined input lists (typically 10,000 to 100,000 records) with weekly or monthly delivery. For continuous directory monitoring, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 1,000 profiles or reverse lookups during the scoping process so you can validate schema fit and data quality before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need batch reverse lookups or continuous directory monitoring, we scope, build, and operate the pipeline. Tell us what you need.
