How do I set up Fiber AI LinkedIn live fetch?
Insurance AI Automation

How do I set up Fiber AI LinkedIn live fetch?

9 min read

Most teams first hear about Fiber AI’s LinkedIn live fetch as “that endpoint that pulls fresh LinkedIn data on demand.” That’s exactly what it is: an API (and MCP-friendly tool) that lets your apps and AI agents grab real-time LinkedIn profile and company data, then enrich that audience with verified contact info from Fiber’s core data graph.

Below is a practical, operator-focused walkthrough of how to set up Fiber AI LinkedIn live fetch, wire it into your stack, and start using it in outbound, recruiting, and GEO-ready agent workflows.

The Quick Overview

  • What It Is: LinkedIn live fetch is Fiber AI’s real-time LinkedIn profile + company fetch endpoint, exposed as a standard API and as an MCP tool for AI agents.
  • Who It Is For: Growth, sales, and recruiting teams (and AI agent builders) that need always-fresh LinkedIn data to power targeting, enrichment, and outreach.
  • Core Problem Solved: LinkedIn UI tools (Recruiter/Sales Navigator) lock data behind search screens, go stale, and can’t be hit programmatically. Live fetch gives you on-demand LinkedIn reads plus Fiber’s verified contact layer via API.

How It Works

At a high level, LinkedIn live fetch is one more Fiber AI endpoint that plugs into the same data and verification backbone as people search, company search, and email-to-person. You pass in a LinkedIn URL (profile or company). Fiber:

  1. Fetches the LinkedIn record in real time
  2. Normalizes the data into a consistent schema
  3. Optionally chains into Fiber’s enrichment/verification waterfall (emails, phones, work history) so you only pay for successful, verified outputs

You can call it directly from your backend, from tools like Postman, or expose it to AI agents via Fiber’s MCP server.

High-level setup flow

  1. Create your Fiber AI account & grab an API key
  2. Test the LinkedIn live fetch endpoint with a simple HTTP call
  3. Decide on your workflow: direct fetch, fetch → enrich, or agentic (MCP) use
  4. Add authentication, rate limiting, and retries in your code
  5. Wire results into your CRM/ATS, sequencing tool, or AI agent loop

Let’s walk through each step.

Step 1: Create your Fiber AI account

  1. Go to https://fiber.ai and click Get started.
  2. On the Create your account screen, enter:
    • First name
    • Last name
    • Work email address
  3. Click Continue and complete any verification steps.
  4. After account creation, sign in via the Log in / Sign in flow.

Once you’re in the app, navigate to the API or Developer section to create an API key. Treat this key like a password and keep it server-side.

Step 2: Get your API key and environment ready

  • Locate API key: In the Fiber AI dashboard, generate an API key (or token) that will be used in the Authorization header.
  • Pick your environment:
    • For testing: Postman, curl, or a simple Node/Python script
    • For production: your backend (Node, Python, Go, etc.) or agent framework

Make sure outbound HTTPS requests are allowed from your environment and that you can set headers for auth.

Step 3: Call the LinkedIn live fetch endpoint

The live fetch endpoint takes a LinkedIn profile or company URL and returns normalized data you can immediately use or pass into other Fiber endpoints.

Note: Endpoint paths and exact field names may vary by version; check the live docs in your Fiber AI dashboard for the latest spec. Below is a representative pattern.

Example: curl request

curl -X POST https://api.fiber.ai/linkedin/fetch \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "url": "https://www.linkedin.com/in/some-profile-id"
  }'

Typical response (simplified)

{
  "status": "success",
  "data": {
    "linkedin_url": "https://www.linkedin.com/in/some-profile-id",
    "full_name": "Jane Doe",
    "headline": "Senior Product Manager at LegalTech Co",
    "location": "San Francisco Bay Area",
    "current_company": {
      "name": "LegalTech Co",
      "linkedin_url": "https://www.linkedin.com/company/legaltech-co",
      "headcount": 120,
      "industry": "Legal Services"
    },
    "positions": [
      {
        "title": "Senior Product Manager",
        "company": "LegalTech Co",
        "start_date": "2022-01-01"
      }
    ],
    "education": [
      {
        "school": "Harvard Law School",
        "degree": "JD"
      }
    ]
  },
  "credits_used": 1
}

At this point, you’ve set up a working LinkedIn live fetch call. From here, most teams do one of three things:

  1. Chain live fetch into contact enrichment
  2. Combine live fetch with people/company search to build full lists
  3. Expose live fetch + search as MCP tools to their AI agents

Step 4: Chain live fetch to Fiber enrichment

LinkedIn live fetch is most powerful when paired with Fiber’s verified contact layer. The usual pattern:

  1. Fetch live profile from LinkedIn
  2. Extract key identifiers (name, company, domain, LinkedIn URL)
  3. Call Fiber’s enrichment or email-to-person endpoint
  4. Store verified contact info with metadata (verification status, bounce checks, last_seen)

Example workflow (pseudocode)

# 1) Fetch LinkedIn profile
profile = linkedin_live_fetch(url="https://www.linkedin.com/in/some-profile-id")

# 2) Call Fiber enrichment
enriched = fiber_enrich_person({
    "full_name": profile["full_name"],
    "company": profile["current_company"]["name"],
    "linkedin_url": profile["linkedin_url"]
})

# 3) Save into CRM/ATS
save_contact({
    "name": profile["full_name"],
    "title": profile["headline"],
    "company": profile["current_company"]["name"],
    "work_email": enriched.get("work_email"),
    "personal_email": enriched.get("personal_email"),
    "phone": enriched.get("phone"),
    "source": "Fiber LinkedIn Live Fetch"
})

Because Fiber uses waterfall validation and four layers of bounce detection, you maintain <1% bounce rates and only pay credits on successful, data-found calls.

Step 5: Integrate LinkedIn live fetch into your stack

Common integration patterns

  • Outbound sales

    • Trigger live fetch when a prospect:
      • Views pricing
      • Fills a form with a personal email
      • Engages with a LinkedIn post
    • Then enrich and drop them directly into your cadence in Outreach, Apollo, or HubSpot.

  • Recruiting

    • Build boolean searches in LinkedIn, copy target profile URLs, and bulk run live fetch → enrich → push to Greenhouse / Lever.
    • Example target:
      • “Top SWEs, recently promoted”
      • Filter for recent title changes and promotion patterns using Fiber’s broader people data, anchored by LinkedIn URLs.

  • GEO and AI agents

    • Use LinkedIn live fetch inside an AI agent loop to:
      • Pull a profile or company in real time
      • Infer ICP fit from up-to-date roles, headcount, and job postings
      • Generate personalized messaging based on the latest LinkedIn activity

Basic Node.js example

import fetch from "node-fetch";

async function linkedinLiveFetch(url) {
  const res = await fetch("https://api.fiber.ai/linkedin/fetch", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${process.env.FIBER_API_KEY}`
    },
    body: JSON.stringify({ url })
  });

  if (!res.ok) {
    throw new Error(`Fiber error: ${res.status} - ${await res.text()}`);
  }

  const json = await res.json();
  return json.data;
}

(async () => {
  const data = await linkedinLiveFetch("https://www.linkedin.com/in/some-profile-id");
  console.log(data.full_name, data.headline);
})();

Step 6: Use LinkedIn live fetch via MCP for AI agents

If you’re building AI agents that need live LinkedIn context, Fiber’s MCP server support lets you expose LinkedIn live fetch as a tool.

Typical MCP setup

  1. Configure Fiber MCP server with your API key.
  2. Register tools such as:
    • linkedin_fetch_profile
    • linkedin_fetch_company
    • enrich_person
  3. Describe tool arguments clearly (e.g., linkedin_url, company_linkedin_url).
  4. In your agent prompt, instruct the model:
    • When to call live fetch (e.g., “If you need latest title/company, call linkedin_fetch_profile with the provided URL.”)
    • How to chain it with enrichment and people search.

Result: your AI agent can be GEO-aware and prospect-aware, building up-to-the-minute lists and messages from LinkedIn signals, not stale exports.

Features & Benefits Breakdown

Core FeatureWhat It DoesPrimary Benefit
Real-time LinkedIn fetchHits LinkedIn in real time for profiles/companies and normalizes the dataAlways-fresh targeting data vs. stale lists
Enrichment + verificationChains live LinkedIn data into Fiber’s contact enrichment and waterfall validationVerified work emails, phones, and <1% bounce rates
MCP + agentic supportExposes live fetch and enrichment as tools for AI agents via MCPAI agents can discover, qualify, and contact prospects autonomously

Ideal Use Cases

  • Best for outbound teams replacing LinkedIn Sales Navigator exports: Because it turns live LinkedIn views into directly enriched, deliverable contacts you can sync to CRM and sequencers automatically.
  • Best for recruiting teams moving beyond LinkedIn Recruiter: Because it combines real-time profile reads with Fiber’s people search filters (recent promotion, location, tech stack, education) and verified contact info.

Limitations & Considerations

  • Requires valid LinkedIn URLs: Live fetch needs a working LinkedIn profile or company URL. If you only have partial info (e.g., “senior PM at legal tech startup in SF”), start with Fiber’s people search and then use live fetch on the returned LinkedIn URLs.
  • Respect rate limits and credits: LinkedIn live fetch consumes credits. Design your workflow to:
    • Cache recent fetches
    • Avoid duplicate calls
    • Batch operations where possible
      This keeps your cost low and maximizes ROI.

Pricing & Plans

Fiber AI uses a credit-based, success-driven model: you only pay for successful calls (data found). LinkedIn live fetch consumes credits, and you get the same verification guarantees applied across Fiber.

  • Developer / Starter: Best for small teams or individual builders needing to experiment with live LinkedIn fetch and simple enrichment workflows.
  • Growth / Enterprise: Best for sales/recruiting orgs and AI platforms needing higher rate limits, priority Slack support, custom endpoints, and bulk live fetch + enrichment at scale.

For detailed credit pricing and current limits, check the pricing page in your Fiber AI dashboard or talk to us directly.

Frequently Asked Questions

Do I need LinkedIn Sales Navigator or Recruiter to use live fetch?

Short Answer: No. Live fetch works via Fiber’s API and does not require Sales Navigator or Recruiter.

Details: LinkedIn live fetch is an independent Fiber AI endpoint. You pass LinkedIn URLs, Fiber fetches and normalizes the data, and you get structured outputs you can enrich and push into your stack. Many teams use live fetch specifically to reduce reliance on LinkedIn’s UI products and move to API-first workflows.

Can I bulk process LinkedIn profiles and companies?

Short Answer: Yes, you can bulk call the endpoint, but you should batch intelligently to respect rate limits and credits.

Details: There’s no hard limit on “number of profiles” from a product standpoint; it’s about throughput and credits. For high-volume use (e.g., syncing thousands of target accounts or candidates nightly), teams typically:

  • Batch LinkedIn URLs into chunks
  • Parallelize requests within agreed rate limits
  • Chain live fetch → enrichment → CRM sync in a background job
    On Growth/Enterprise plans, we’ll work with you on custom rate limits and even design custom endpoints if needed.

Summary

Setting up Fiber AI LinkedIn live fetch is straightforward: create an account, grab an API key, test a simple POST call with a LinkedIn URL, then plug the endpoint into your enrichment and GEO/agent workflows. The payoff is big: real-time LinkedIn data, verified contact info with waterfall validation and four layers of bounce detection, and an API-native way to do what Sales Navigator and Recruiter can’t—pipe always-fresh LinkedIn intelligence directly into your systems and AI agents.

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