People Data Labs (PDL)
B2B data enrichment API providing person and company data at scale. PDL aggregates data from public sources to provide structured person profiles (employment history, education, skills, social profiles) and company data (headcount, funding, technologies used). Used by sales teams, recruiters, and AI agents for data enrichment workflows.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
HTTPS enforced. GDPR and CCPA compliant. Privacy implications require legal review for EU use. SOC2 Type II certified.
⚡ Reliability
Best When
You need scalable B2B data enrichment (person profiles, company data, tech stack) for agent sales, recruiting, or research workflows.
Avoid When
You need real-time verified data, consumer data, or strictly regulated GDPR workflows without legal review.
Use Cases
- • Enrich contact records with professional history and skills for agent-driven lead qualification pipelines
- • Identify company technographics (tech stack, headcount, funding stage) for agent-powered outbound sales research
- • Match and resolve person identity across partial data points (email, LinkedIn, name+company) in agent data pipelines
- • Build agent-driven recruiting tools that identify candidates by skills, experience, and location from PDL's person search
- • Enrich event attendee or customer lists with employment context for agent personalization workflows
Not For
- • Consumer data enrichment — PDL is B2B/professional data only
- • Real-time identity verification — PDL data may be months old
- • EU GDPR-heavy workflows without careful legal review
Interface
Authentication
API key passed as api_key query parameter or X-Api-Key header. Credit-based system — each API call consumes credits based on data returned.
Pricing
Credit-based pricing consumed per API call. Person enrichment costs more credits than company enrichment. Credits don't roll over monthly.
Agent Metadata
Known Gotchas
- ⚠ Credits are consumed even for no-match results — agents must budget credits for zero-result searches
- ⚠ Data accuracy varies — high-profile individuals have accurate data; less-prominent professionals may have outdated data
- ⚠ GDPR compliance requires legal basis for processing European professional data — review DPA before EU use
- ⚠ Person search returns probabilistic matches with a 'likelihood' score — filter by threshold appropriate for use case
- ⚠ Rate limits of 100 req/min can bottleneck batch enrichment — implement exponential backoff for large batches
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for People Data Labs (PDL).
Scores are editorial opinions as of 2026-03-06.