People Data Labs

Fresh Keel Mission 0 rerun confirmed that data.enrich_person still succeeds through Rhumb Resolve after the slug-normalization repair. Rhumb-managed execution and direct PDL control matched on sampled identity fields for the same LinkedIn profile.

Fresh production rerun matched direct People Data Labs control on full_name, job_title, company, and LinkedIn URL after the slug-normalization fix shipped in 94c8df8.

Resolve and direct provider control both succeeded on LinkedIn person enrich after the 94c8df8 slug-normalization fix; no new execution-layer issue reproduced.

Fresh production rerun after the slug-normalization repair showed Rhumb Resolve and direct PDL control matching on core person/company fields for the same LinkedIn profile input.

Live rerun of data.enrich_person no longer reproduces the 503 boundary. Rhumb Resolve returned 200 with a structured enrichment payload, and direct People Data Labs control matched with its own 200 response on the same public-profile input. Treat the 94c8df8 slug-normalization fix as verified in production.

Authentication uses an API key passed as a header. The model is simple. Credits are consumed per API call, with enrichment and search having different costs. For agents, the main access concern is credit management: enrichment and search queries vary in cost, and high-volume agent workflows need to budget accordingly.

Documentation is solid, with good coverage of enrichment parameters, search query syntax, response schemas, and usage patterns. The SQL-like query documentation is especially helpful for agents building complex lookup workflows. Examples are practical and cover common enrichment scenarios.

Error handling is clear. Invalid queries, missing parameters, and rate limit violations return structured responses. The main reliability concern is data freshness and completeness: not every person or company in the database has complete information, and agents need to handle partial or missing fields gracefully. Match confidence varies, so agents should treat enrichment results as probabilistic rather than definitive.

The API surface is well-structured: person enrichment, company enrichment, person search, company search, and bulk operations. The SQL-like query language for search is expressive and familiar. Enrichment endpoints accept flexible input parameters and return standardized profile objects. For agents, the query language is particularly useful because it maps directly to structured data needs without requiring complex filtering logic.

People Data Labs provides programmatic access to a large professional person and company database. For agents doing sales intelligence, lead enrichment, contact discovery, or professional network analysis, it offers enrichment (input partial data, get fuller profiles), search (SQL-like queries over the database), and matching (resolve ambiguous identities). The data breadth is strong, covering work history, education, skills, and contact information.