B2B Lead Generation & Sales Intelligence Platform — Extract Verified Decision Maker Emails at Scale

The most powerful B2B lead generation and contact enrichment tool on Apify. Upload a company list and get back verified decision maker emails, phone numbers, LinkedIn profiles, and company intelligence — all from a single 24-stage automated pipeline. No API keys required.

$2 per 1,000 results. First 20 free.

Keywords: B2B lead generation, email finder, email verifier, company scraper, contact extractor, lead scoring, sales intelligence, prospect enrichment, domain validator, website crawler, business data extraction, lead qualification, CRM export, email discovery, SMTP verification, B2B data enrichment, Apollo alternative, ZoomInfo alternative, Clearbit alternative, cold email tool, sales prospecting, LinkedIn scraper, decision maker finder

---

🔍 What Is This Tool?

This is an enterprise B2B sales intelligence platform that turns a simple list of company names into a complete, verified prospect database — ready for cold email, CRM import, or sales outreach.

Who it's for: Sales teams, SDRs, growth marketers, recruiters, agencies, and anyone who needs verified B2B contact data without paying $100-500/month for Apollo or ZoomInfo.

What it does:

• 🌐 Discovers company websites from just a company name
• 👤 Extracts decision makers (names, titles, emails, phones, LinkedIn) using 4 extraction methods
• 📧 Finds emails through 5 discovery layers (DNS, website crawl, search engines, PDF mining, social)
• ✅ Verifies every email with a 6-check pipeline and assigns B2B send tiers
• 🧠 Scores and ranks contacts by seniority, authority, and email confidence
• 📊 Exports CRM-ready data in CSV, Excel, JSON Lines, or via webhook

📋 Data You Get Back

Category	Fields
👤 Contacts	Full name, job title, email, phone, LinkedIn URL, seniority level, persona type
🏢 Companies	Website, domain, social profiles (8 platforms), tech stack, employee count, revenue signals
📧 Email Intel	Verification status, B2B tier (TIER_1_SEND / TIER_2 / TIER_3 / SKIP), confidence score, auth records
🧠 Lead Score	Combined priority (0-100), authority score, decision maker flag, persona classification
🔬 Company Intel	Tech stack fingerprint, SaaS detection, company maturity score, funding stage, SERP signals

---

⚡ Quick Start

1. Upload your company list

Provide companies as CSV/Excel upload, public URL, or inline JSON:

{
    "companies": [
        {"company_name": "Stripe", "website": "https://stripe.com"},
        {"company_name": "Notion", "website": "https://notion.so"},
        {"company_name": "Linear"},
        {"company_name": "Vercel"},
        {"company_name": "Figma"}
    ],
    "maxResults": 20
}

💡 Tip: You only need the company_name column. The pipeline discovers websites automatically if none is provided.

2. Click Start

Watch progress in real-time: Stage 4/24: Enriching 45/100 companies...

3. Download results

Get your data from the Dataset tab (JSON/CSV/Excel) or KeyValueStore (multi-sheet Excel, JSON Lines).

---

⬆️ Output

Sample Output (JSON)

{
    "company_name": "Acme Inc",
    "domain": "acme.com",
    "company_website": "https://acme.com",
    "contact_name": "John Doe",
    "contact_title": "VP Sales",
    "contact_email": "john.doe@acme.com",
    "contact_phone": "+1-555-0123",
    "contact_linkedin": "https://linkedin.com/in/johndoe",
    "extraction_method": "team_card",
    "is_decision_maker": true,
    "persona_type": "Champion",
    "seniority": 4,
    "lead_score": 85,
    "combined_priority": 78,
    "priority_band": "HIGH",
    "verification_status": "valid",
    "b2b_tier": "TIER_1_SEND",
    "confidence_score": 92,
    "correlation_confidence": 88,
    "composite_confidence": 0.91,
    "evidence_count": 4,
    "data_freshness": "verified",
    "auth_score": 85,
    "linkedin_company": "https://linkedin.com/company/acme",
    "twitter_url": "https://twitter.com/acme",
    "tech_stack": "React, Next.js, AWS, Stripe",
    "company_maturity_score": 72,
    "employee_count_estimate": "50-200",
    "has_mx": true,
    "has_spf": true,
    "has_dmarc": true,
    "domain_score": 88,
    "enrichment_status": "done"
}

📊 Dataset Views

The actor provides 6 pre-built dataset views in the Apify Console:

View	What It Shows
All Contacts	Every contact with full scoring, verification, and evidence fields
High Priority Decision Makers	Filtered to decision makers with correlation confidence and evidence
Companies	Company-level data: domain, social profiles, tech stack, maturity
Company Intelligence	Tech stack, analytics tools, SaaS signals, maturity score
Funding Intel	Revenue estimates, funding stage, employee counts, acquisition signals

📂 Export Formats

Format	Location	Best For
Apify Dataset	Dataset tab	API access, JSON/CSV download
CSV (output.csv)	KeyValueStore	CRM import (UTF-8 with BOM)
Excel (output.xlsx)	KeyValueStore	5-sheet workbook with Contacts, Companies, Locations, High_Priority, Audit
JSON Lines (output.jsonl)	KeyValueStore	BigQuery, Snowflake, streaming ingestion
Webhook	Your endpoint	Real-time delivery to CRM/Zapier

---

💰 Why Teams Switch from Apollo, ZoomInfo, and Lusha

Pain Point	How This Solves It
Apollo/ZoomInfo costs $100-500/mo for stale data	$2 per 1,000 leads — fresh data scraped in real time, no subscription
Purchased lead lists have 30-50% bounce rates	Built-in 6-check email verification with B2B tier classification (TIER_1 = <5% bounce)
Contact databases miss small/mid-size companies	Scrapes any company website directly — not limited to a pre-built database
LinkedIn Sales Navigator requires manual prospecting	Automated LinkedIn employee discovery via search engines (no login needed)
Generic web scrapers miss contacts in JavaScript	4 extraction methods catch contacts in JSON-LD, JS bundles, and hydration payloads
No way to tell who's a decision maker	AI lead scoring with seniority mapping, persona classification, and authority scoring
Exporting data requires manual cleanup	14-rule junk removal, dedup, and CRM-ready export in CSV, Excel, JSON Lines
Running the same list twice wastes time	Incremental delta mode skips recently-enriched companies (~70% time savings)

💵 Cost Comparison

Solution	1,000 Leads	10,000 Leads	100,000 Leads
This Actor	~$3	~$30	~$250
Apollo.io	$49/mo (limited)	$99-399/mo	Custom pricing
ZoomInfo	$250+/mo	$500+/mo	$1,000+/mo
Lusha	$49/mo (limited)	$199/mo	Custom pricing
Hunter.io	$49/mo (500 lookups)	$199/mo	Custom pricing

Includes per-event fees + estimated Apify platform charges. All stages, residential proxy.

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🎯 Use Cases

Cold Email Outreach & Email Marketing

Upload your target company list and get verified decision maker emails with B2B tier classification. Filter by TIER_1_SEND for safest emails (<5% bounce rate). Import directly into Lemlist, Instantly, Smartlead, Apollo, Woodpecker, or Mailchimp.

Sales Prospecting & Lead List Building

Build targeted B2B lead lists from scratch. Start with just company names — the pipeline discovers websites, extracts leadership teams, finds and verifies emails, and scores every contact. Export the High_Priority sheet for your SDR team.

Account-Based Marketing (ABM)

Enrich your target account list with verified contacts, social profiles, tech stack data, and company intelligence. Decision maker mapping identifies Economic Buyers and Champions at each company.

CRM Data Enrichment

Have a CRM full of companies but missing contact details? Upload your list and the pipeline fills in emails, phones, LinkedIn URLs, social profiles, tech stack, and decision maker details. Incremental mode ensures you only pay for new enrichment.

Competitive Intelligence

Scrape company websites at scale to collect leadership teams, tech stacks, funding signals, and social presence. Company Intelligence shows tech stack fingerprinting, SaaS detection, and company maturity scores.

Recruitment & Talent Sourcing

Find hiring managers and leadership contacts. The pipeline extracts LinkedIn profiles alongside email addresses for combined outreach. Persona classification identifies Technical Evaluators and Champions.

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🔑 Key Features

🌐 Multi-Source B2B Data Extraction

• Website email extractor with 4-method contact extraction (JSON-LD, team cards, heuristic proximity, LinkedIn URLs)
• 5-layer email discovery: DNS/OSINT, direct crawl, search engines, PDF mining, social platforms
• LinkedIn employee discovery via multi-query search (CEO, CTO, VP, Director, Manager variations)
• 8-platform social enrichment: LinkedIn, Twitter/X, Facebook, Instagram, YouTube, GitHub, Crunchbase, Glassdoor
• SERP intelligence: revenue estimates, funding signals, employee counts, acquisition signals
• File intelligence: PDF mining for contacts invisible to HTML scrapers
• Hidden contact extraction: __NEXT_DATA__, __NUXT__, __INITIAL_STATE__, JS hydration payloads

🧠 AI-Powered Lead Scoring

• Decision maker identification with 5-level seniority mapping (C-Suite → VP/Director → Manager → Staff → Unknown)
• Persona classification: Economic Buyer, Champion, Technical Evaluator, Influencer
• Combined priority score (0-100): 60% authority + 40% email confidence
• Company intelligence profile: tech stack fingerprinting (18+ frameworks), SaaS detection, maturity scoring
• Quality gate: configurable thresholds filter low-quality contacts before export

✅ Email Verification & Deliverability

• 6-check pipeline: syntax, MX records, catch-all, disposable, role detection, DKIM/SPF/DMARC
• B2B send tiers: TIER_1_SEND (safe) → TIER_2_LIKELY_GOOD → TIER_3_REVIEW → SKIP
• 8-pattern email prediction for contacts missing emails: first.last@, flast@, firstlast@, first_last@, and more
• Confidence scoring (0-100) with weighted components: SMTP +40, MX +20, auth +15, pattern +10

⚙️ Enterprise Infrastructure

• Adaptive concurrency: auto-scales 4-32 workers based on success rate and response times
• HTTP-first hybrid scraping: HTTP → Playwright → Playwright Stealth escalation (browser is last resort)
• Cross-run cache: eliminates redundant DNS, SERP, LinkedIn, and verification lookups across runs
• Incremental delta mode: skip companies enriched within freshness window (1-90 days)
• Executive correlation: cross-source dedup with fuzzy Levenshtein name matching
• Checkpoint/resume: large runs survive restarts and actor migrations

---

⬇️ Input

Data Input (choose one)

Parameter	Type	Description
inputFile	File upload	Upload a CSV or Excel file with company names and/or websites
inputUrl	String	Public URL to a CSV or Excel file
companies	JSON array	Inline company list as JSON objects

💡 Auto-detection: The actor recognizes 30+ column name aliases — company_name, organisation, business, exhibitor, firm, url, domain, web_address, and more. Any extra columns are preserved in output.

Settings & Pricing

Parameter	Type	Default	Description
pipelineVersion	String	v10	Engine version: v10 (default), v9 (Intelligence OS), v8 (legacy)
maxResults	Integer	20	Max companies to process. Free: 20/run. Beyond: $2/1,000 results
workers	Integer	16	Initial parallel workers (adaptive: auto-scales 4-32)
maxContactsPerCompany	Integer	20	Contact cap per company. Decision makers prioritized
maxCrawlPagesPerCompany	Integer	25	High-value pages crawled per company (5-60)

Incremental & Quality

Parameter	Type	Default	Description
incrementalMode	Boolean	false	Skip recently-enriched companies (~70% time savings)
incrementalFreshnessDays	Integer	7	Days before cached data is considered stale (1-90)
minLeadScore	Integer	0	Quality gate: min combined_priority to export
minConfidenceScore	Integer	0	Quality gate: min confidence score to export
targetConfidence	Number	0.80	Goal-seeking enrichment loop confidence target (0.0-1.0)
maxPasses	Integer	5	Max re-enrichment passes per company

Webhook & Export

Parameter	Type	Default	Description
webhookUrl	String	—	HTTP endpoint to receive results on completion
webhookSendFullResults	Boolean	false	Include full data in webhook payload
exportJsonLines	Boolean	false	Also export as .jsonl in KeyValueStore
pushWarehouseTables	Boolean	false	Push warehouse tables to dataset (increases PPE cost)

Pipeline Stage Controls

💡 Tip: Skip Google Boost + Social Enrichment for ~40% faster runs. The pipeline auto-adjusts downstream stages.

Parameter	Default	Skip Effect
skipGoogleBoost	false	Skip 8-step Google Discovery (~30% faster)
skipSocialEnrichment	false	Skip 8-platform social discovery (~15% faster)
skipLinkedInDiscovery	false	Skip LinkedIn employee discovery
skipSemanticPageDetect	false	Skip semantic page classification
skipSearchExpansion	false	Skip SERP intelligence (revenue/funding signals)
skipFileIntelligence	false	Skip PDF mining
skipDeepContactExtract	false	Skip 4-method deep re-extraction
skipHiddenContactExtract	false	Skip JS/JSON payload extraction
skipContactIntelligence	false	Skip decision maker mapping
skipCompanyIntel	false	Skip company intelligence profile
skipExecutiveCorrelation	false	Skip cross-source contact dedup
skipEmailDiscovery	false	Skip 5-layer email discovery
skipEmailPrediction	false	Skip 8-pattern email prediction
skipVerification	false	Skip 6-check email verification
skipQualityGate	false	Skip quality gate filtering

Parallel Processing

Parameter	Type	Default	Description
parallelMode	Boolean	true	Enable parallel company processing
companyConcurrency	Integer	5	Min companies processed in parallel (floor for adaptive scaling)
crawlStopContacts	Integer	8	Early-exit crawl after N titled contacts found
reuseBrowserContexts	Boolean	true	Reuse browser contexts (rotated every 25 requests)

Proxy

Parameter	Type	Description
proxyConfiguration	Proxy	Apify Proxy config. Residential strongly recommended for best results

⚠️ Warning: Running without proxy is not recommended for batches over 20 companies. Datacenter proxies work for most sites but corporate sites may block them.

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🏗️ How It Works — 24-Stage Intelligence Pipeline

INPUT: Company list (CSV / Excel / URL / JSON)
  │
  ├── Stage 1:  INGEST           → Smart input parsing (30+ column aliases)
  ├── Stage 2:  DISCOVER         → Multi-engine website discovery
  ├── Stage 3:  GOOGLE BOOST     → 8-step search enhancement
  ├── Stage 4:  ENRICH           → Adaptive hybrid crawling (HTTP → Browser → Stealth)
  ├── Stage 5:  GEO              → Location intelligence
  ├── Stage 6:  SOCIAL           → 8-platform social discovery
  ├── Stage 7:  LINKEDIN         → Employee discovery via search engines
  ├── Stage 8:  SEMANTIC PAGES   → Intelligent page classification
  ├── Stage 9:  SEARCH + SERP    → Revenue, funding, employee signals
  ├── Stage 10: PDF MINING       → File intelligence extraction
  ├── Stage 11: DEEP EXTRACT     → 4-method contact re-extraction
  ├── Stage 12: HIDDEN EXTRACT   → JS payload mining (Next.js, Nuxt, Vue)
  ├── Stage 13: CONTACT INTEL    → Decision maker mapping & persona classification
  ├── Stage 14: COMPANY INTEL    → Tech stack, maturity, SaaS detection
  ├── Stage 15: EXEC CORRELATION → Cross-source fuzzy dedup
  ├── Stage 16: EMAIL DISCOVER   → 5-layer email discovery
  ├── Stage 17: EMAIL PREDICT    → 8-pattern email prediction
  ├── Stage 18: VERIFY           → 6-check email verification
  ├── Stage 19: SCORE            → Lead scoring engine
  ├── Stage 20: CLEANUP          → 14-rule junk removal
  ├── Stage 21: QUALITY GATE     → Configurable threshold filtering
  ├── Stage 22: METRICS          → Pipeline analytics
  ├── Stage 23: EXPORT           → Multi-format output
  └── Stage 24: WEBHOOK          → Real-time delivery
  │
OUTPUT: Verified leads → Dataset + CSV + Excel + JSON Lines + Webhook

Stage Details

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🛡️ HTTP-First Hybrid Scraping Architecture

Every page is fetched with the cheapest method that works — a browser render is the last resort, not the default:

HTTP (pooled httpx, ~0.3s) → Playwright (6s cap) → Playwright Stealth (15s cap)

Layer	Proxy Tier	When Used
HTTP (pooled keep-alive)	Datacenter	Always first; JS-shell detection decides escalation
Playwright	Datacenter	Only when HTTP returns a JS shell
Playwright Stealth	Residential	Only when plain render is blocked; budget-capped per run

Efficiency

Feature	How It Works
Compressed page store	Crawled HTML zlib-compressed and freed after last stage reads it
Pooled browser contexts	One per (browser, proxy tier), rotated every 25 requests
Resource blocking	Images, media, fonts, CSS, and 40+ tracking domains blocked
Early-exit crawl gate	Stops low-priority pages once enough contacts found
Cross-run SERP cache	Search queries hit network once per 7 days across all runs
LinkedIn + verification cache	Skip re-discovered profiles and re-verified emails
Crawl reuse	Email discovery reuses stage-4 crawl instead of re-fetching

Anti-Detection

Feature	How It Works
Fingerprint rotation	UA, viewport, locale, timezone, color scheme per context
Stealth hardening	navigator/webdriver masking on stealth renders
Proxy tiering	Datacenter for HTTP; residential reserved for stealth + search
Block detection	HTTP status + soft-block text markers trigger escalation
Adaptive concurrency	Auto-scales 4-32 workers based on success rate
Domain rate limiting	Per-domain circuit breaker with recovery timeout

---

💻 API Examples

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("leadslogix/leadslogix-pipeline").call(run_input={
    "inputUrl": "https://example.com/target-companies.csv",
    "maxResults": 500,
    "workers": 16,
    "maxContactsPerCompany": 15,
    "minLeadScore": 50,
    "webhookUrl": "https://your-crm.com/webhook",
    "proxyConfiguration": {"useApifyProxy": True},
})

# Get TIER_1 decision makers
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("is_decision_maker") and item.get("b2b_tier") == "TIER_1_SEND":
        print(f"{item['company_name']} | {item['contact_name']} | "
              f"{item['contact_email']} | Score: {item['combined_priority']}")

# Download Excel from KeyValueStore
kv = client.key_value_store(run["defaultKeyValueStoreId"])
xlsx = kv.get_record("output.xlsx")
with open("leads.xlsx", "wb") as f:
    f.write(xlsx["value"])

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("leadslogix/leadslogix-pipeline").call({
    companies: [
        { company_name: "Datadog", website: "https://datadoghq.com" },
        { company_name: "Cloudflare", website: "https://cloudflare.com" },
        { company_name: "Twilio", website: "https://twilio.com" },
    ],
    maxResults: 50,
    workers: 16,
    minLeadScore: 50,
    proxyConfiguration: { useApifyProxy: true },
});

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

const tier1 = items.filter(
    (i) => i.is_decision_maker && i.b2b_tier === "TIER_1_SEND"
);
console.log(`Found ${tier1.length} verified decision makers`);

for (const lead of tier1) {
    console.log(`${lead.company_name} | ${lead.contact_name} | ${lead.contact_email}`);
}

cURL

curl -X POST "https://api.apify.com/v2/acts/leadslogix~leadslogix-pipeline/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "companies": [
      {"company_name": "Figma", "website": "https://figma.com"},
      {"company_name": "Canva", "website": "https://canva.com"}
    ],
    "maxResults": 20,
    "workers": 16,
    "webhookUrl": "https://your-endpoint.com/webhook"
  }'

Usage Examples

With Quality Gate & Webhook:

{
    "inputUrl": "https://example.com/target-companies.csv",
    "maxResults": 500,
    "workers": 16,
    "minLeadScore": 50,
    "minConfidenceScore": 40,
    "webhookUrl": "https://hooks.zapier.com/hooks/catch/123456/abcdef/",
    "webhookSendFullResults": true,
    "exportJsonLines": true,
    "proxyConfiguration": {"useApifyProxy": true}
}

Incremental Mode (Repeat Runs):

{
    "inputUrl": "https://example.com/same-companies.csv",
    "maxResults": 1000,
    "incrementalMode": true,
    "incrementalFreshnessDays": 14,
    "proxyConfiguration": {"useApifyProxy": true}
}

Fast Run (Skip Optional Stages):

{
    "companies": [{"company_name": "Acme Corp"}],
    "maxResults": 20,
    "skipGoogleBoost": true,
    "skipSocialEnrichment": true,
    "skipLinkedInDiscovery": true,
    "skipSearchExpansion": true,
    "skipFileIntelligence": true
}

---

📊 Output Schema

Contact Fields

Field	Type	Description
contact_name	String	Full name
contact_title	String	Job title
contact_email	String	Email address
contact_phone	String	Direct phone number
contact_linkedin	String	LinkedIn profile URL
extraction_method	String	How found: jsonld, team_card, heuristic, linkedin, deep_extract, hidden_extract, file_intel, search
is_decision_maker	Boolean	Holds a leadership position
persona_type	String	Economic Buyer, Champion, Technical Evaluator, Influencer
seniority	Integer (0-5)	Title seniority level
lead_score	Integer (0-100)	Authority score
combined_priority	Integer (0-100)	60% authority + 40% verification
priority_band	String	HIGH, MEDIUM, LOW, SKIP
verification_status	String	valid, risky, invalid, unknown
b2b_tier	String	TIER_1_SEND, TIER_2_LIKELY_GOOD, TIER_3_REVIEW, SKIP
confidence_score	Integer (0-100)	Email deliverability confidence
composite_confidence	Float (0-1)	Multi-signal composite confidence
correlation_confidence	Integer (0-100)	Cross-source correlation
evidence_count	Integer	Number of independent evidence sources
data_freshness	String	verified, crawled, linkedin_only, predicted_only, search_derived
auth_score	Integer (0-100)	Domain authentication score

Company Fields

Field	Type	Description
company_name	String	Company name
company_website	String	Full URL
domain	String	Normalized domain
company_emails	String	Semicolon-separated company emails
company_phones	String	Semicolon-separated phones
linkedin_company	String	LinkedIn company page
twitter_url, facebook_url, instagram_url	String	Social profiles
youtube_url, github_url, crunchbase_url, glassdoor_url	String	Business profiles
company_city, company_country	String	Location
tech_stack	String	Detected technologies
analytics_tools	String	Detected analytics platforms
company_maturity_score	Integer (0-100)	Business maturity index
is_saas	Boolean	SaaS company detection
employee_count_estimate	String	Estimated employee count
estimated_revenue_m	Number	Revenue estimate (millions USD)
funding_amount_m	Number	Funding amount (millions USD)
funding_stage	String	Seed, Series A-F
has_mx, has_spf, has_dkim, has_dmarc	Boolean	DNS validation
domain_score	Integer (0-100)	Domain trust score
website_quality_score	Integer (0-100)	Website quality index
pages_crawled	Integer	Pages successfully scraped
enrichment_status	String	done, cached, failed, error

---

💵 Pricing

Tier	Actor Fee	Results Per Run	Best For
Free	$0	Up to 20	Testing the pipeline
Pay-Per-Event	$2 per 1,000 results	Unlimited	Production lead generation

Apify platform compute charges (CPU, memory, proxy) are billed separately per your Apify subscription.

Cost Estimation

Scenario	Companies	Actor Fee	Est. Platform	Total
Quick test	20	$0 (free)	~$0.05	~$0.05
Small batch	100	$0.16	~$0.15	~$0.31
Medium batch	500	$0.96	~$0.50	~$1.46
Large batch	1,000	$1.96	~$1.00	~$2.96
Enterprise	10,000	$19.96	~$10	~$30

---

📈 Performance Benchmarks

Metric	Typical Result
Companies per hour	100-200 (all stages, residential proxy)
Contacts per company	3-15 (varies by company size)
Email discovery rate	60-80% of companies
Decision maker rate	30-50% of contacts
TIER_1 email rate	40-60% of verified emails
Cache hit rate	30-70% on repeat runs

Estimated Run Times

Companies	All Stages	Skip Google+Social	Discovery Only
20	3-5 min	2-3 min	1-2 min
100	15-25 min	10-15 min	5-8 min
500	1-2 hours	40-70 min	20-30 min
1,000	3-5 hours	2-3 hours	45-60 min
10,000	24-48 hours	16-30 hours	6-10 hours

---

🔗 Integrations

Platform	How to Connect
Google Sheets	Auto-sync via Apify Google Sheets integration
HubSpot	Import CRM-ready CSV, or webhook for real-time sync
Salesforce	CSV import or connect via Zapier
Pipedrive	CSV import or webhook
Lemlist / Instantly / Smartlead	Export TIER_1 emails as CSV
Apollo / Outreach / SalesLoft	Import as prospect sequence
Zapier / Make	Connect to 5,000+ apps via Apify Zapier integration
BigQuery / Snowflake	Ingest JSON Lines output
Custom API	Full REST API for scheduling and automation

---

🔄 Webhook Payload

When the pipeline completes, your webhook receives:

{
    "event": "pipeline_complete",
    "pipeline_version": "v10.0",
    "timestamp": "2026-07-10T12:30:00.000Z",
    "summary": {
        "total_companies": 100,
        "total_contacts": 450,
        "high_priority": 85,
        "decision_makers": 120,
        "emails_found": 380,
        "verified_emails": 310
    },
    "audit": {
        "total_companies": 100,
        "elapsed_seconds": 1200,
        "pipeline_version": "v10.0 (24-stage Intelligence Platform)"
    }
}

---

⏰ Scheduled Lead Generation

Automate recurring prospecting:

1. Go to the actor page and click Schedules
2. Create a schedule (e.g., 0 8 * * 1 for every Monday at 8 AM)
3. Point the input to a URL that updates with new target companies
4. Enable incrementalMode to skip previously-enriched companies
5. Set a webhookUrl to receive results in your CRM automatically

---

🔧 Troubleshooting

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❓ FAQ

How is this different from Apollo, ZoomInfo, or Lusha? Those tools maintain a pre-built database of contacts. This tool scrapes company websites and search engines in real time, finding contacts that static databases miss — especially at small/mid-size companies, international firms, and recently-hired executives. It's also 10-50x cheaper per lead.

Do I need API keys? No. This tool uses public web data, DNS records, and search engines. No paid API subscriptions required.

What input formats are supported? CSV, Excel (.xlsx, .xls), and inline JSON. Upload directly, provide a URL, or pass data via the API.

How does incremental mode work? When enabled, the pipeline checks its cross-run cache for each company. If enriched within the freshness window (default 7 days), it's skipped. Saves ~70% on repeat runs.

How does the quality gate work? Set minLeadScore and/or minConfidenceScore to filter contacts. Contacts below thresholds are excluded from export but tracked in metrics. Set both to 0 to export everything.

How accurate is the email verification? TIER_1_SEND emails typically have <5% bounce rate. The pipeline checks MX, SPF, DKIM, DMARC, catch-all, disposable, and role addresses. It does not perform SMTP-level mailbox verification.

Can I use this for a single company? Yes. Use inline JSON with one company and maxResults: 1. The API supports synchronous runs.

Does this work for non-English companies? Yes, but extraction rates are typically 30-50% lower for CJK and Arabic websites due to different page structures and email conventions.

What proxy should I use? Residential proxies give the best results. Datacenter proxies work for most sites but corporate sites may block them. No proxy is not recommended for 20+ companies.

Can I skip stages to save time? Yes. Toggle any of the 15 skip parameters. Skipping Google Boost + Social Enrichment saves ~40% runtime.

What's the maximum batch size? Up to 100,000 with maxResults. For 5,000+ companies, use 8-16 workers with residential proxy and incremental mode.

What pipeline version should I use? Use v10 (default) — it's the fastest and most efficient. v9 has a goal-seeking intelligence graph (more thorough but slower). v8 is the legacy fallback.

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⚠️ Limitations

• Email verification is DNS-based, not SMTP-based. Confirms the domain accepts mail but does not verify individual mailbox existence. For maximum accuracy, run TIER_2 emails through an additional SMTP service.
• Websites behind login walls or with aggressive anti-bot measures may return limited contacts.
• Non-English websites (Korean, Chinese, Japanese, Arabic) have lower extraction rates due to different page structures.
• LinkedIn discovery uses search engines, not direct LinkedIn scraping. Results depend on profile visibility in search indexes.
• SERP intelligence (revenue, funding) is regex-extracted from search snippets and may not be available for all companies.
• Social enrichment depends on DuckDuckGo availability. Heavy usage may reduce discovery rates.

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📜 Changelog

v10.1 (2026-07-04)

• CU optimization: compressed HTML, pooled clients, cross-run caches, datacenter-first renders with stealth budget, early-exit crawl gate, per-stage error isolation, cooperative shutdown
• Enrichment quality: careers/press/privacy pages crawled, LinkedIn company-match verification, cross-stage entity resolution, contacts ranked by composite confidence
• Output change: warehouse _table rows no longer in dataset by default (opt in with pushWarehouseTables)
• New inputs: crawlStopContacts, reuseBrowserContexts, pushWarehouseTables

v9.0 (2026-06-05)

• Intelligence OS: graph-centric, goal-seeking engine with persistent intelligence graph
• Evidence Engine: multi-source evidence with provenance tracking and contradiction detection
• Signal Fusion: composite confidence from 5 dimensions (identity, deliverability, authority, relationship, evidence)
• Crawl Budgeter: per-company budget allocation with ROI-based decisions

v8.1 (2026-06-01)

• Higher-yield crawl (25 pages default), 5-layer email discovery, better contact retention, safer per-company dedup

v8.0 (2026-05-20)

• Hybrid 7-engine scraping, smart fallback cascade, site auto-classification, enhanced anti-detection

v7.0 (2026-05-19)

• Quality gate, webhook dispatcher, incremental delta mode, fuzzy dedup, JSON Lines export

v6.0 (2026-05-19)

• SERP intelligence, PDF mining, executive correlation, adaptive concurrency, shared cache

v5.0 — v1.0

• See full changelog in release notes