Proxycurl

Extracts and structures LinkedIn profile information (education, work history, skills, endorsements, recommendations) by scraping LinkedIn's public profile pages and parsing HTML/DOM into normalized JSON schemas. Uses headless browser automation or direct HTTP requests with LinkedIn session handling to bypass rate limiting, returning standardized profile objects with 50+ fields including employment timeline, skill endorsements, and recommendation counts.

Extracts structured company information from LinkedIn company pages including employee count, industry classification, funding status, company size, headquarters location, and employee list. Parses LinkedIn's company page DOM to extract metadata, cross-references with other data sources to infer company stage (Series A, B, C, etc.) and funding details, and returns normalized company objects with employment distribution across roles and seniority levels.

Manages API rate limits and quota allocation across requests, implementing per-minute and per-month rate limiting with quota tracking and enforcement. Provides quota usage reporting and alerts to prevent unexpected overage charges, with support for quota pooling across team members and automatic request queuing to respect rate limits without client-side retry logic.

Provides official SDKs and community-maintained libraries for popular programming languages (Python, JavaScript/Node.js, Ruby, PHP, Go) with language-idiomatic APIs, built-in error handling, retry logic, and type definitions. SDKs abstract HTTP request handling and provide convenient methods for common operations like profile lookup, company enrichment, and batch operations. Includes comprehensive documentation and example code for each language.

Supports webhook callbacks for asynchronous batch operations and long-running requests, delivering results to a specified endpoint when processing completes. Implements webhook retry logic with exponential backoff for failed deliveries and provides webhook signature verification for security. Enables real-time integration with downstream systems without requiring polling for results.

Extracts structured job posting information from LinkedIn job listings including job title, description, required skills, seniority level, employment type, salary range (where disclosed), and company details. Parses LinkedIn job page HTML to extract posting metadata, applies NLP-based skill extraction to identify required competencies from free-text descriptions, and normalizes job classifications (title, level, function) into standardized taxonomies for downstream analysis and matching.

Processes multiple LinkedIn profile and company URLs in a single batch request, returning structured data for all inputs with optimized throughput and reduced per-request overhead. Implements request queuing, deduplication, and parallel processing to handle 100-10,000 URLs per batch, with support for CSV/JSON input formats and webhook callbacks for asynchronous result delivery, enabling efficient data pipeline integration for large-scale enrichment workflows.

Extracts lists of employees from LinkedIn company pages, returning structured employee records with name, current title, profile URL, and seniority level. Implements pagination and filtering to handle companies with 1,000+ employees, and optionally enriches each employee record with full profile data (work history, skills, education) through linked profile extraction, enabling organizational mapping and workforce analysis use cases.

Infers likely email addresses for LinkedIn profiles based on name, company, and common email format patterns (firstname.lastname@company.com, first_last@company.com, etc.). Uses company domain lookup and pattern matching against known email formats, with confidence scoring to indicate reliability of inferred addresses. Integrates with profile enrichment to automatically generate contact lists without requiring email verification or third-party email databases.

Looks up LinkedIn profiles by email address, returning matching profile data if a public profile is associated with that email. Implements email-to-profile matching by querying LinkedIn's index and returning normalized profile objects, enabling reverse enrichment workflows where email is the starting point rather than LinkedIn URL.

Normalizes and standardizes skills extracted from LinkedIn profiles and job postings against a curated skill taxonomy, mapping free-text skills to canonical skill names and categories (e.g., 'Python programming' → 'Python' under 'Programming Languages'). Implements fuzzy matching and synonym resolution to handle variations in skill naming, and provides skill proficiency levels and endorsement counts from LinkedIn data, enabling skill-based matching and talent analytics.

Monitors LinkedIn profiles for changes (job changes, skill additions, endorsements, recommendations) and triggers webhooks or notifications when updates are detected. Implements periodic polling of profile data and diff-based change detection to identify new employment, skill endorsements, or profile completeness changes, enabling real-time lead scoring and recruitment alerts without manual profile checks.

Filters LinkedIn profiles and company data by geographic location, industry, company size, job title, and seniority level to enable targeted lead generation and prospecting. Implements server-side filtering on extracted profile data to reduce client-side processing, supporting complex filter combinations (e.g., 'VP-level decision-makers in tech companies with 100-500 employees in California') for efficient lead list generation.