We extract attorney profiles, firm details, practice area data, peer reviews, and state law repositories from FindLaw. 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 Attorney Profiles objects from findlaw.com. All fields typed and schema-versioned.
"attorney_id": "FL-9823471", "full_name": "Jane Doe", "firm_name": "Doe & Associates", "location": "Chicago, IL", "phone_number": "+1-312-555-0198", "super_lawyers_status": true, "peer_rating": 4.8
| # | attorney_id | full_name | firm_name | practice_areas | location | phone_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Law Firm Details objects from findlaw.com. All fields typed and schema-versioned.
"firm_id": "F-44921", "firm_name": "Smith Legal Group", "city": "Austin", "state": "TX", "attorney_count": 14, "languages_spoken": "['English', 'Spanish']", "website": "https://smithlegalgrouptx.example.com"
| # | firm_id | firm_name | address_line_1 | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Practice Areas objects from findlaw.com. All fields typed and schema-versioned.
"category": "Personal Injury", "sub_category": "Medical Malpractice", "attorney_count_estimate": 8450, "top_listed_firms": "['Johnson Law', 'Apex Injury Attorneys']", "findlaw_url": "https://lawyers.findlaw.com/lawyer/practice/medical-malpractice", "scraped_at": "2026-05-12T10:15:00Z"
| # | category | sub_category | description | related_topics | attorney_count_estimate | top_listed_firms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from findlaw.com. All fields typed and schema-versioned.
"review_id": "REV-99213", "attorney_id": "FL-9823471", "reviewer_type": "Client", "star_rating": 5, "review_date": "2025-11-04", "verification_status": "Verified"
| # | review_id | attorney_id | reviewer_type | star_rating | review_body | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Legal Articles objects from findlaw.com. All fields typed and schema-versioned.
"article_id": "ART-55102", "title": "Understanding Restraining Orders in California", "topic": "Family Law", "state_applicability": "CA", "publish_date": "2023-04-12", "last_updated": "2025-01-20"
| # | article_id | title | topic | author | publish_date | last_updated |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our FindLaw scraper navigates complex directory structures, normalises inconsistent profile formats, and handles deep pagination to deliver a complete map of the US legal landscape.
Extract names, firm affiliations, practice areas, education, and bar admission histories for over a million listed attorneys.
Map law firm hierarchies, office locations, attorney rosters, and contact details across all 50 states.
Capture Super Lawyers and Rising Stars badge designations directly from the FindLaw profile metadata.
Preserve FindLaw's exact categorisation structure, linking specific attorneys to niche legal sub-disciplines.
Extract star ratings, written testimonials, and reviewer types to gauge reputation metrics for specific practitioners.
Scrape public state law repositories and legal guides hosted on FindLaw's informational subdomains.
Clean and format phone numbers, physical addresses, and website URLs into standardised, queryable fields.
Monitor directories for new bar admissions, firm changes, or updated contact details with diff-based delivery.
Traverse deeply nested search results without missing records due to hidden limits or UI truncation.
Brief in. Clean data out.
Provide target states, practice areas, or specific firm lists. We map the required data fields and extraction frequency.
We configure Scrapy spiders, manage proxy rotation to bypass rate limits, and write parsing logic for inconsistent profile layouts.
We run schema validation, check phone number formats, and ensure complete pagination coverage before full deployment.
Clean, normalised records pushed to your S3 bucket, BigQuery dataset, or via Webhook on your required schedule.
FindLaw protects its data with aggressive rate limiting and complex DOM structures. Here is how we ensure reliable extraction.
Directory sites use strict IP-based rate limiting. We route requests through a massive pool of US-based residential proxies to distribute the crawl load and mimic legitimate user traffic patterns.
Attorney profiles often have missing fields, varied address formats, or obfuscated phone numbers. Our pipeline includes a normalisation layer that standardises locations, names, and contact details before delivery.
FindLaw truncates large category results. We use programmatic search filters by city, zip code, and sub-practice area to force smaller result sets, ensuring 100% coverage of the underlying database.
Profile layouts differ between free listings and premium sponsored attorneys. We use multi-layered XPath and CSS selectors to accurately map fields regardless of the specific page template.
We monitor total extraction counts against baseline metrics. If a UI update hides contact details or breaks pagination, our alerting system flags the pipeline for immediate developer intervention.
Populate initial user bases and build comprehensive practitioner directories for new legal software platforms.
Identify law firms by size, location, and specialty to target with marketing, IT, or administrative services.
Analyse the density of specific practice areas across different states to identify underserved legal markets.
Locate highly rated attorneys with specific niche experience for consultation on complex litigation.
Study trends in legal education, bar admissions, and firm sizes using historical directory data.
Law firms monitor competitor growth, new hires, and practice area expansion within their geographic region.
"FindLaw hosts the most comprehensive directory of US legal professionals, but extracting clean, structured profiles requires traversing deeply nested, heavily protected pagination trees."
Directory scraping looks simple until you hit aggressive rate limits and inconsistent profile schemas. DataFlirt manages the residential proxies, JavaScript execution, and schema normalisation required to turn FindLaw pages into a reliable, queryable legal database. Your engineers get clean data, not maintenance tickets.
Everything supported by our findlaw.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 manages the crawl state and deduplication, while Playwright handles JavaScript execution for dynamic elements like click-to-reveal phone numbers.
A vast pool of US residential IPs ensures we can scrape millions of directory pages without triggering Cloudflare blocks or rate limits.
Airflow schedules regular diff scans, triggering AWS Lambda for parallel processing and storing state in PostgreSQL for change detection.
Data delivered to where your team already works — no new tooling required.
About findlaw.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public factual data like business addresses and attorney names is generally protected. We do not bypass authentication walls or scrape private communications. Clients must ensure their use of the data complies with local regulations like CAN-SPAM for marketing.
FindLaw often limits search results to a specific number of pages. We bypass this by programmatically iterating through smaller geographic areas (like zip codes) and specific sub-practice areas to ensure every profile is captured.
No. FindLaw routes communications through web forms to protect attorney emails. We extract physical addresses, phone numbers, and firm website URLs, which can often be cross-referenced to find emails.
We support one-off extractions, weekly updates, or monthly refreshes. Frequent updates use our change detection system to only deliver profiles that have been modified or added.
Yes. Our pipeline is designed to parse both the detailed premium profiles and the basic free listings, normalising the output into a single consistent schema.
Yes, we extract all visible historical reviews attached to an attorney's profile, including star ratings, dates, and the full review text.
We clean and normalise all phone numbers into standard E.164 format, removing inconsistent spaces, brackets, and dashes found on the raw HTML pages.
Yes. We provide a sample dataset covering a specific city or practice area so you can verify the schema, accuracy, and fill rates before committing to a full pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop dealing with rate limits and broken parsers. Tell us the states and practice areas you need, and we will deliver clean, structured attorney data directly to your systems.
