--- title: "People Data Labs Integration" description: "Enrich people and companies with the People Data Labs dataset. Agents turn an email or domain into a full profile with job, employer, and firmographics, resolve ambiguous identities into ranked matches, and build lead lists with Elasticsearch or SQL search." url: https://flowrunner.ai/integrations/people-data-labs date_modified: 2026-08-04T10:04:16-07:00 --- # People Data Labs [Sales Intelligence](https://flowrunner.ai/integrations/category/crm-sales) Enrich people and companies with the People Data Labs dataset. Agents turn an email or domain into a full profile with job, employer, and firmographics, resolve ambiguous identities into ranked matches, and build lead lists with Elasticsearch or SQL search. [Verified](https://flowrunner.ai/integrations/verified "What does verified mean?") · 8 actions · API key · available [People Data Labs website](https://peopledatalabs.com/) · [Platform Documentation](https://docs.peopledatalabs.com/docs) · Capability data verified 2026-08-12 1. A new signup arrives with nothing but a work email and a company domain 2. Enrich Person matches the email against the dataset with Min Likelihood set to the team's confidence bar 3. Enrich Company resolves the domain into firmographics: industry, size, employee count, headquarters 4. The agent scores the account against the ICP using title, seniority, and company size 5. The enriched profile writes to the CRM and the firmographic summary posts to the sales channel 6. Signups whose identifiers were too thin for a confident match route through Identify Person, returning ranked candidates 7. The SDR reviews the candidate list and picks the right identity, or rejects them all, before anything writes to the CRM 8. ICP-fit accounts alert the account executive with the full profile and the reason for the fit score ## What This Integration Enables People Data Labs is a dataset with an API, and the honest way to think about it is as a per-record data market: every matched person, every returned company, every search result is a credit spent. That framing is exactly why it belongs inside orchestration rather than wired to raw triggers. FlowRunner agents turn emails into full person profiles, domains into firmographics, ambiguous names into ranked identity candidates, and Elasticsearch or SQL queries into lead lists, while the workflow keeps score of what each answer cost. - Enrich signups, leads, and customers from a single email into job, employer, location, and socials - Append firmographics to any company from its domain, name, or ticker - Build lead and target-account lists by title, industry, skills, size, or location - Resolve partial identities into ranked candidates for human selection - Batch-enrich imported lists up to 100 lookups per request - Keep queries clean with canonical values from Autocomplete Enrichment strictness is a dial, not a hope: Min Likelihood and Required Fields turn "best guess" into "confident match or nothing," and FlowRunner's [human-in-the-loop](https://flowrunner.ai/concepts/human-in-the-loop) steps handle the cases the dial cannot. ### Without FlowRunner **Leads are email addresses**: Routing and prioritization run on the only two fields the form captured **Research is a browser tab ritual**: Every promising signup costs someone ten minutes of manual lookup **Enrichment spend is invisible**: Credits drain across tools and nobody can say what a lead list actually cost ### With FlowRunner **Leads are profiles**: Title, employer, seniority, and firmographics arrive seconds after the signup does **Research is a workflow step**: Agents enrich, score, and route while the lead is still on the thank-you page **Spend is a decision**: Searches are costed before they scroll, and a person approves the purchase ## Use Case Scenarios ### Signup to routed lead in one motion A new customer fires from [Shopify](https://flowrunner.ai/integrations/shopify) or a trial form. Enrich Person resolves the email with the likelihood floor set high, Enrich Company resolves the domain, and the combined profile lands in [HubSpot](https://flowrunner.ai/integrations/hubspot) with ICP scoring already applied. Fits alert sales in [Slack](https://flowrunner.ai/integrations/slack) with the summary; non-fits route to the nurture track. The ten-minute research ritual becomes a four-second workflow step. ### The lead list as a costed purchase The team defines a segment: VP-level operations titles at logistics companies between 200 and 2,000 employees. Autocomplete canonicalizes the titles and industry values first so the query matches cleanly. Search People runs a first page, reports the total match count and projected credit cost, and stops. The owner approves the buy, the agent scrolls through the result set, and each record seeds [Salesforce](https://flowrunner.ai/integrations/salesforce-pro) as a campaign lead with Search Companies filling account-level firmographics alongside in [Google Sheets](https://flowrunner.ai/integrations/google-sheets). ### The imported list, resolved honestly A conference badge scan export arrives: names, partial companies, some emails. Bulk Enrich People submits the list in batches of 100 with shared minimum-likelihood defaults, resolving the clean rows automatically. The ambiguous remainder goes through Identify Person, and the SDR picks from ranked candidates rather than trusting a coin flip. Retrieve Person refreshes previously matched profiles by ID at renewal time without re-running matches, keeping long-lived records current at the lowest credit cost. ## Human-in-Loop Highlight Search People bills one credit per returned record, and it returns up to 100 per call with a scroll token that will happily keep going: an agent that paginates a 40,000-match query has spent 40,000 credits by the time anyone looks up. So on this page, a search is a quote before it is a purchase. The agent runs the first page, posts "Query matches 38,412 people. Cap the pull at 2,500 best-fit records for 2,500 credits, or refine the query?", and the list owner sets the cap or tightens the criteria before the scroll begins. The other gate is identity itself: when Identify Person returns ranked candidates for "J. Chen, ops director, Bay Area," an agent that auto-picks the top match may attach a stranger's employment history to a customer record and address outreach to it. The SDR picks the person; the agent does everything after that. Agent processes routinely Detects exception requiring judgment Clear match Continues automatically Ambiguous Routes to human via preferred channel Human decides Agent resumes with decision ## Agent Capabilities 8 actions ### Person 5 - **Enrich Person** Matches identifiers, email, phone, social URL, name plus company or location, against the person dataset, returning a full profile with a 1 to 10 likelihood score. Min Likelihood and Required Fields enforce match strictness. One credit per successful match. - **Identify Person** Runs fuzzy identity resolution and returns up to 20 ranked candidates, each scored, for ambiguous inputs. The action behind this page's human identity pick. Credits per matched record returned. - **Retrieve Person** Re-fetches the current record for a known person ID without re-matching. The cheapest way to keep long-lived profiles fresh. One credit per record. - **Search People** Queries the full person dataset with an Elasticsearch or SQL query, up to 100 records per call with scroll pagination. One credit per returned record, which is why searches are costed and capped before they run. - **Bulk Enrich People** Enriches up to 100 lookups in one request, each with its own identifiers and result, with shared strictness defaults. The batch path for imported lists. One credit per matched record. ### Company 2 - **Enrich Company** Matches a company by name, website, social URL, or ticker into a firmographic record: industry, size, employee count, founding year, headquarters. A website yields the most reliable match. One credit per successful match. - **Search Companies** Queries the full company dataset with Elasticsearch or SQL for target-account lists, 100 records per call with scroll pagination. One credit per returned record. ### Utilities 1 - **Autocomplete** Returns canonical values with record counts for a search field from partial text: companies, titles, industries, skills, schools, locations. The hygiene step that keeps searches from paying for fuzzy misses. ## Frequently Asked Questions ### What can FlowRunner do with People Data Labs? FlowRunner agents can run Enrich Person, Identify Person, and Retrieve Person in People Data Labs, plus 5 more actions. ### Does connecting People Data Labs to FlowRunner require OAuth? No. People Data Labs connects to FlowRunner with an API key, no OAuth flow required. ### Can People Data Labs trigger a FlowRunner workflow automatically? People Data Labs doesn't currently expose triggers in FlowRunner. It connects as an action step inside workflows started by another trigger. **Work at People Data Labs?** This integration exposes People Data Labs to AI agents on every FlowRunner plan, including through MCP, at no cost to you. [See what FlowRunner offers integration partners](https://flowrunner.ai/integrations/partners), including how to keep this page current. --- Markdown version of https://flowrunner.ai/integrations/people-data-labs. Site index: https://flowrunner.ai/llms.txt