Sales Prospect Finder & Enricher — PhantomBuster Alternative

Find and enrich public sales prospects across LinkedIn, Instagram, X, YouTube, and Google. Export people, companies, profiles, activity, and search records with source links and optional outreach drafts for human review. For sales operations, each accepted dataset row is a normalized prospecting or profile result with a record type and workflow identifier.

Workflow: put the results to work

Choose a discovery workflow to find the relevant people, companies, or public activity, then enrich selected profile URLs. Review normalized records with their source evidence before qualification or CRM import. Optional outreach remains a draft marked for human review; no messages or invitations are sent.

Supported workflows

Goal	Workflow	Input	Result
Find people	linkedinPeopleSearch	Query, optional company/title/location	Person prospects
Enrich people	linkedinProfile	LinkedIn profile URLs	Detailed person profiles
Find companies	linkedinCompanySearch	Query and optional location	Company prospects
Enrich companies	linkedinCompany	LinkedIn company URLs	Detailed company profiles
Find warm leads	linkedinPostEngagers	LinkedIn post URLs	Commenter and reactor records
Track person activity	linkedinProfilePosts	LinkedIn profile URLs	Recent public posts
Track company activity	linkedinCompanyPosts	LinkedIn company URLs	Recent public posts
Find hiring signals	linkedinJobSearch	Query and optional location	Public job records
Find intent content	linkedinPostSearch	Query and freshness	Public post records
Find service providers	linkedinServiceSearch	Query and optional location	Public service-profile records
Enrich creators	instagramProfile, xProfile, youtubeChannel	Public profile/channel URLs	Detailed social profiles
Research the open web	googleSearch	Query, region, and freshness	Public search results

From public search to a reviewed lead

A sales operations analyst starts with linkedinPeopleSearch and a specific market, role, and location. The Actor returns normalized people records with source URLs and collection times. Next, the analyst selects relevant profiles and runs linkedinProfile for richer public context. The accepted rows can then be passed to a CRM import, qualification model, or review queue. When drafts are enabled, every draft stays labeled for human review and can be edited before a separately authorized outreach step.

Quick start

Find LinkedIn people:

{
  "workflow": "linkedinPeopleSearch",
  "query": "B2B SaaS founders",
  "location": "United States",
  "maxResults": 25,
  "responseFormat": "concise"
}

Enrich known profiles and prepare drafts for review:

{
  "workflow": "linkedinProfile",
  "targetUrls": [
    { "url": "https://www.linkedin.com/in/example/" }
  ],
  "maxResults": 10,
  "includeOutreachDraft": true,
  "outreachGoal": "compare sales workflow priorities",
  "senderName": "Alex"
}

Output

Every accepted dataset row uses the same core contract:

• Identity: recordId, recordType, fullName, profileUrl
• Work context: jobTitle, companyName, companyUrl, location
• Public contact fields: website, publicEmails, publicPhones
• Sales signals: summary, engagementType, publishedAt, follower/connection/employee counts
• Provenance: workflow, sourcePlatform, sourceUrl, query, collectedAt
• Optional review queue: outreachDraft, reviewStatus, actionStatus

Use responseFormat: "detailed" when you also need a bounded public source-data object. Private routing, credentials, and service-billing metadata are removed from that object.

Example:

{
  "recordId": "015bfeaa54bd846b89d0d8ef",
  "recordType": "person",
  "workflow": "linkedinPeopleSearch",
  "sourcePlatform": "linkedin",
  "sourceUrl": "https://www.linkedin.com/in/example/",
  "fullName": "Example Person",
  "jobTitle": "Founder",
  "companyName": "Example Company",
  "location": "London, United Kingdom",
  "publicEmails": [],
  "publicPhones": [],
  "collectedAt": "ISO 8601 collection timestamp",
  "provenance": "public_web_data"
}

The example illustrates the schema only and is not a real lead.

Best results and workflow guidance

Provide a focused query or canonical public HTTPS target URL, start with a small result cap, and confirm that the returned source URLs match your intended market. Use concise output for routing and qualification; use detailed output for a bounded research record. For fresher intent signals, pair company or person activity with post-engager collection and keep collectedAt alongside any downstream score. A VALID_EMPTY outcome is a useful result: refine the query, verify the public target, and continue with the next specific segment.

Outcome contract

Read OUTPUT for a compact result and RUN_SUMMARY for exact request, row, charge, warning, build, and anonymous route-attempt counts.

• COMPLETE: accepted records were returned with no unexpected request or validation failures.
• PARTIAL: useful records were returned, with at least one target or record requiring another pass.
• VALID_EMPTY: the public request completed correctly and found no matching records.
• INVALID_INPUT: the workflow/input combination is invalid.
• UPSTREAM_FAILED: no configured data route returned usable public data.

A green Apify run is not the only proof: downstream automation should reconcile itemsPushed, the dataset count, chargedEventCounts, and the terminal outcome.

AI agent and MCP prompt

Run linkedinPeopleSearch for B2B SaaS founders in Canada with a 25-result scope. Read back the dataset and OUTPUT, report the terminal outcome and charged event counts, preserve each source URL and collection time, and return only records with a public profile URL. Prepare drafts for human review and keep every action status as not_sent.

Builder's note

I designed the workflow catalog around the jobs that repeatedly matter in prospecting: find a market, inspect the person or company, capture activity and engagement signals, and hand a stable record to the next system. In my testing, the most useful design choice was keeping one normalized dataset contract across every source while preserving optional detailed public fields for deeper review.

How it compares with PhantomBuster

PhantomBuster is a broad sales-automation workspace with subscription plans, automation slots, execution-hour allowances, lead lists, enrichment credit pools, scheduling, integrations, and authenticated outreach. This Actor is a focused, API-first alternative for teams that want bounded public-data prospecting jobs and Apify-native outputs.

Decision	This Actor	PhantomBuster
Billing	Dynamic completed-request and accepted-result events	Subscription plans with slots, execution time, and separate credit pools
Same-job cost	Announces a calculable event-charge cap from requests and accepted rows	Allocates the job against shared monthly workspace capacity
Effective efficiency	One normalized dataset contract across every workflow	Lead lists and automation-specific outputs inside an integrated workspace
LinkedIn research	People, profiles, companies, engagers, activity, jobs, posts, and services	Larger catalog with authenticated and scheduled LinkedIn workflows
Other sources	Instagram, X, YouTube, and Google profile/search workflows	Broader automation catalog across 15+ platforms
Output	One normalized dataset contract plus machine-readable terminal records	Workspace lead lists and automation-specific exports
Outreach	Reviewable drafts only; nothing is sent	Authenticated invitations, messages, and follow-up sequences
Automation surface	One API-first Actor that can be scheduled or chained on Apify	Full dashboard workspace, extension-based account connections, and team features

Choose PhantomBuster when authenticated social actions, its workspace UI, or its complete automation catalog is the requirement. Choose this Actor when the job is public prospect discovery/enrichment with explicit cost caps and normalized Apify data.

PhantomBuster is a trademark of its owner. This independent Actor is not affiliated, associated, sponsored, or endorsed by PhantomBuster.

API

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('khadinakbar/phantombuster-alternative').call({
  workflow: 'linkedinPeopleSearch',
  query: 'AI agency founders',
  maxResults: 25
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Responsible use

Collect only public data you are authorized to process. Follow applicable platform terms, privacy and employment laws, data-minimization rules, opt-outs, and retention requirements. Public contact details are not proof of accuracy, consent, current employment, or permission to contact someone. Review every draft and establish a lawful basis before any outreach in another system.

Pricing and run costs

This Actor uses Pay per event plus Apify platform usage. The Pricing tab lists the current event rates and billing terms.

Event	Billing unit	When it applies
workflow-request	Completed workflow request	One target URL or search page completed through a usable public-data route, including valid requests with no matching rows.
prospect-found	Prospecting result	One validated discovery, activity, engager, job, service, content, or search-result record persisted to the dataset.
prospect-enriched	Enriched prospect profile	One validated detailed person, company, Instagram, X, or YouTube profile persisted to the dataset.

Run cost combines the charged events and Apify platform usage. Review the run charge limit and requested result count before starting.

Connect an AI agent

Use the Apify MCP configurator to choose an available client connection. Inspect this Actor’s current input schema and required credentials before running it.