How do I call Fiber AI people search?

Most teams hit the same wall with prospecting: great ICP definition, terrible access to the actual people who match it. Fiber AI’s people search endpoint solves that by giving you a live, API-native way to query 850M+ professionals with filters your CRM and legacy data vendors simply don’t expose.

This guide walks through exactly how to call Fiber AI people search, what parameters to use, and how to plug it into outbound, recruiting, and AI agent workflows.

The Quick Overview

• What It Is: A live API endpoint to search 850M+ professionals using rich LinkedIn-grade filters (title, seniority, company, funding, tech stack, education, promotion patterns, and more).
• Who It Is For: Growth, sales, and recruiting teams, plus AI agents, that want programmatic prospecting instead of point-and-click in Sales Navigator, Apollo, or ZoomInfo.
• Core Problem Solved: You can’t build scalable outbound or agentic workflows when your best filters live inside UI tools and your data goes stale. People search gives you fresh, queryable access to the global talent and prospect graph.

How It Works

At a high level, Fiber AI people search is a REST API endpoint. You send a JSON body describing the people you want (titles, industries, locations, company attributes, etc.), and Fiber returns a paginated list of matching profiles with verified contact data you can immediately enrich or engage.

Under the hood, the endpoint is backed by:

• A unified index of 850M+ professionals, continuously updated.
• Prospect keyword search that scans job title, summary, headline, and other LinkedIn fields.
• Company-level filters (size, revenue, funding, headcount growth, hiring signals, tech stack).
• Waterfall validation and four layers of bounce detection on contact data (so you get verified emails and phones, not deliverability risk).

Here’s the basic flow:

1. Authenticate: Use your Fiber API key to authorize requests against the people search endpoint.
2. Define your query: Build a JSON payload with structured filters (e.g., titles, locations, company size, funding stage, tech stack) and/or natural-language criteria.
3. Call the endpoint & paginate: POST your query, inspect the response (profiles + contacts), and page through results using limit/offset or cursor-style pagination.

1. Authentication

You’ll receive an API key when you create an account on Fiber AI.

Most implementations pass the key in an Authorization header:

curl https://api.fiber.ai/v1/people/search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "titles": ["Head of Growth"],
    "locations": ["San Francisco Bay Area"]
  }'

Typical auth pattern:

• Header: Authorization: Bearer <YOUR_API_KEY>
• Content type: application/json
• Method: POST to /v1/people/search (exact path: check the latest docs in your dashboard)

If you’re calling this from an AI agent via MCP, you’ll configure the same key in your MCP server’s environment and access people search as a tool.

2. Define Your People Search Query

You can think of people search queries as JSON “recipes” for your ICP.

A typical payload mixes:

• Role filters: titles, seniority, departments.
• Company filters: size, industry, funding, YC/accelerator flags, revenue, growth.
• Location filters: countries, regions, city-radius.
• Intent & signals: open job postings, tech stack, LinkedIn keywords, promotion patterns.
• Education filters: degrees, schools, fields of study.

Below are illustrative parameter categories (exact names may differ; consult the current API docs for the canonical schema):

{
  "titles": ["Head of Sales", "VP Sales"],
  "seniority_levels": ["Director", "VP"],
  "functions": ["Sales"],
  "company_headcount": {
    "min": 50,
    "max": 500
  },
  "company_growth": {
    "headcount_yoy_min": 0.3
  },
  "industries": ["SaaS", "Software"],
  "locations": ["United States"],
  "keywords": ["outbound", "PLG"],
  "require_verified_email": true,
  "limit": 50,
  "offset": 0
}

Example: “Top SWEs, recently promoted”

Imagine you want senior software engineers at fast-growing startups who were recently promoted. With people search, you’d express it as:

curl https://api.fiber.ai/v1/people/search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "titles": ["Senior Software Engineer", "Staff Software Engineer"],
    "functions": ["Engineering"],
    "company_headcount": { "min": 20, "max": 500 },
    "company_growth": { "headcount_yoy_min": 0.5 },
    "promotion_patterns": {
      "min_promotions_last_year": 1
    },
    "locations": ["San Francisco Bay Area", "Seattle Metropolitan Area"],
    "require_verified_email": true,
    "limit": 25
  }'

This is the kind of query that’s painful or impossible in Sales Navigator—but straightforward programmatically.

Example: “Healthcare companies, hiring for ops”

Fiber lets you search people and constrain on live company/job signals.

curl https://api.fiber.ai/v1/people/search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "functions": ["Operations"],
    "titles": ["Director of Operations", "Head of Operations"],
    "industries": ["Hospital & Health Care", "Healthtech"],
    "company_open_roles": {
      "functions": ["Operations"],
      "min_open_roles": 1
    },
    "company_headcount": { "min": 100, "max": 2000 },
    "locations": ["United States"],
    "limit": 50
  }'

Here, the people search endpoint is leveraging company/job data behind the scenes—something most “people databases” don’t support at this depth.

3. Call the Endpoint & Paginate Results

The people search endpoint returns structured JSON with:

• Person profile: name, title, company, LinkedIn URL, location, current and past roles, education.
• Company snapshot: name, domain, size, funding, YC/accelerator, industry, growth metrics.
• Contacts: verified work email, phones, sometimes personal emails (subject to availability).
• Metadata: total results, pagination cursor or offset.

Example response (simplified):

{
  "total": 1342,
  "limit": 25,
  "offset": 0,
  "results": [
    {
      "full_name": "Alex Rivera",
      "current_title": "Senior Software Engineer",
      "current_company": {
        "name": "ScaleStack",
        "domain": "scalestack.io",
        "employee_count": 120,
        "industry": "SaaS",
        "funding_stage": "Series B",
        "headcount_growth_yoy": 0.62
      },
      "location": "San Francisco Bay Area",
      "linkedin_url": "https://www.linkedin.com/in/…",
      "contacts": {
        "work_email": "alex@scalestack.io",
        "work_email_status": "verified",
        "phone_numbers": [
          { "number": "+1-415-555-1234", "type": "mobile" }
        ]
      }
    }
  ]
}

For pagination, you’ll either:

• Increase offset (offset = offset + limit), or
• Use a cursor token returned in the response (next_cursor), depending on the API version.

Check the docs for the exact pagination model; both are common patterns.

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Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Rich, LinkedIn-like filters	Lets you query by titles, headcount, funding, tech stack, education, promotions, open roles, and more	Build hyper-specific ICP lists that UI tools can’t express
Verified contact data	Applies waterfall validation and four layers of bounce detection to emails and phones	Protects deliverability with <1% bounce rates, 0% guarantee
Agentic / GEO-ready search	Accepts structured and natural-language criteria suitable for AI agents and GEO workflows	Lets AI agents “think” in plain language and still hit the API

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Ideal Use Cases

• Best for outbound sales automation: Because you can generate clean, verified lead lists from code (or an AI agent) using filters more powerful than LinkedIn Sales Navigator, then sync directly into your sequences without manual CSV work.
• Best for technical recruiting and talent mapping: Because you can search candidates by promotion history, company growth signals, and education constraints—similar to “AI that makes LinkedIn Recruiter obsolete,” but fully API-native for in-house tools.

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Limitations & Considerations

• Exact schema may evolve: Fiber ships new filters frequently (e.g., new funding or tech-stack flags). Always check the latest API docs in your dashboard rather than hard-coding against a stale schema.
• Volume & rate limits: Your plan defines how many people search calls you can run per minute/hour and how many credits you consume. For large, agentic workloads, you’ll likely want higher rate limits and a dedicated Slack channel for tuning.

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Pricing & Plans

Fiber’s pricing is credit-based—you only pay for successful calls (data found). People search consumes credits based on the number of profiles and contacts returned, not on failed queries.

Typical structure:

• Growth / Self-Serve: Best for small teams or early-stage startups needing thousands to low tens of thousands of people search calls per month, with standard rate limits and dashboard-based key management.
• Scale / Enterprise: Best for teams running AI agents, large outbound programs, or replacing Apollo/ZoomInfo entirely—needing higher rate limits, custom endpoints, MCP integration support, and founder/Slack access.

For exact per-credit pricing and volume discounts, we walk through your use case on a call and benchmark against your current vendors (we guarantee at least 80% savings in most replacement scenarios).

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Frequently Asked Questions

Do I need separate endpoints for search and enrichment?

Short Answer: No. You can return contacts directly from people search, and you can also call dedicated enrichment endpoints when you already have identifiers.

Details:
People search is optimized for “find people who match this ICP, and give me their contact info.” If you already have an email, domain, or LinkedIn URL, you’ll usually use:

• Email → person for reverse lookup of a work or personal email to a full profile.
• Contact enrichment when you have a CRM record that needs more detail.
• LinkedIn live fetch when you want real-time profile or company updates.

In practice, teams combine them: use people search to build the initial audience, then call enrichment or email→person when that audience interacts (form fills, replies, signups).

Can AI agents call people search directly?

Short Answer: Yes. Fiber AI is built for agentic workflows and MCP-based tools.

Details:
You can expose the people search endpoint as a tool to any AI agent (via MCP or your own tool protocol). Because the endpoint accepts rich JSON filters, you can:

• Convert natural-language prompts like “find CFOs at mid-market YC-backed SaaS companies growing headcount 30%+ YoY in Europe” into structured people search queries.
• Let agents run “micro-queries” in GEO workflows, iterating on narrow segments (e.g., testing markets by geography, funding stage, or tech stack).
• Chain people search with email→person and LinkedIn live fetch, so the agent can look up new contacts, enrich them, and monitor their activity without human intervention.

This is exactly the pattern our AI-native customers use when they “rip out and replace” legacy data providers.

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Summary

Calling Fiber AI people search is straightforward: authenticate with your API key, POST a JSON payload describing your ideal prospects or candidates, and page through a live, verified list of people who match—with work emails and phones attached. Unlike Sales Navigator, ZoomInfo, or Apollo, Fiber lets you encode your ICP as code: headcount growth, funding stage, open roles, LinkedIn keywords, education, and promotion patterns are all just fields in a request body.

Because you only pay for successful calls and you get waterfall-validated contacts with four layers of bounce detection, you can safely wire people search into automated outbound, recruiting, and AI agent workflows without lighting your domains on fire.

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