Lead Enricher

Takes raw inbound lead data (often just a name and email) and enriches it into a complete lead record. Uses WebSearch for public data, checks tools.lead_enrichment config for Apollo or ZoomInfo API paths, and outputs a fully populated lead record ready for qualification and routing.

Prerequisites

• agency.config.json populated (ICP, tools config)
• WebSearch tool available
• Inbound lead data: at minimum, email address or company name
• Optional: Apollo.io API access (configured in tools.lead_enrichment)
• Optional: ZoomInfo access (configured in tools.lead_enrichment)

Capabilities Used

1. company-researcher -- deep company data extraction
2. person-researcher -- contact-level enrichment
3. linkedin-researcher -- LinkedIn profile data
4. email-validator -- verify email deliverability
5. crm-writer -- write enriched record back to CRM

Phase 0: Intake

Read agency.config.json:

• icp.segments[] -- for ICP fit tagging during enrichment
• tools.lead_enrichment -- check which enrichment tools are available:

  {
    "tools": {
      "lead_enrichment": {
        "primary": "apollo",
        "apollo_api_key": "env:APOLLO_API_KEY",
        "zerobounce_api_key": "env:ZEROBOUNCE_API_KEY",
        "fallback": "websearch"
      }
    }
  }
  

• crm.tabs -- where to write enriched records
• services[] -- for service-need matching during enrichment

Accept parameters:

• lead -- (required) lead object with available fields
• mode -- single | batch. Default: single
• leads -- (required if mode = batch) array of lead objects
• enrichment_depth -- basic | standard | deep. Default: standard
• validate_email -- boolean, run email validation. Default: true
• write_to_crm -- boolean, write enriched record to CRM. Default: true

Minimum lead input:

{
  "email": "priya@freshskin.co"
}

Or:

{
  "contact_name": "Priya Mehta",
  "company_name": "FreshSkin Co"
}

Phase 1: Email Domain Extraction

If only email provided:

• Extract domain: freshskin.co
• Derive company domain: check if freshskin.co resolves to a website
• Flag freemail domains (gmail.com, yahoo.com, outlook.com) -- lower quality lead signal

If company name provided but no domain:

• WebSearch: "{{company_name}}" website
• Extract the primary domain

Phase 2: Contact Enrichment

Apollo Path (if tools.lead_enrichment.primary = "apollo")

Use Apollo People Match or People Search:

• Input: email or name + company
• Extract: full name, title, phone, LinkedIn URL, company details
• Apollo returns: person match confidence, verified email, company firmographics

WebSearch Path (if Apollo unavailable or as supplement)

Run person-researcher with available data:

Name + Company search:

• WebSearch: "{{contact_name}}" "{{company_name}}"
• WebSearch: "{{contact_name}}" linkedin
• WebSearch: "{{contact_name}}" {{company_domain}}

Extract:

• Full name (verify spelling)
• Current title and role
• LinkedIn profile URL
• Twitter/X handle
• Other social profiles
• Professional background summary
• Recent activity (posts, interviews, conference appearances)

LinkedIn Enrichment

Run linkedin-researcher for:

• Profile headline and summary
• Current role details (start date, description)
• Previous roles (career trajectory)
• Education
• Skills and endorsements
• Connections count (proxy for network size)
• Recent posts (content they care about)
• Groups (interests and focus areas)

Compile:

CONTACT RECORD
---
Full name: [verified name]
Email: [email]
Email status: [valid/invalid/catch-all/unknown]
Title: [current title]
Phone: [if found]
LinkedIn: [URL]
Twitter: [handle]
Location: [city, country]
Seniority: [C-level/VP/Director/Manager/Individual]
Department: [Marketing/Ecommerce/Engineering/Operations/Executive]

Phase 3: Company Enrichment

Apollo Path (if available)

Use Apollo Organization Enrich:

• Input: domain
• Extract: company name, industry, employee count, revenue range, tech stack, social profiles

WebSearch Path

Run company-researcher with the company domain:

Firmographic data:

• Company legal name
• Industry and sub-industry (SIC/NAICS if available)
• Employee count (exact or range)
• Revenue estimate (range)
• Founded year
• Headquarters location
• Funding status (bootstrapped, seed, Series A/B/C, public)
• Total funding raised

Digital presence:

• Website URL and platform (Shopify, WooCommerce, custom, etc.)
• Technology stack (via BuiltWith signals in search results)
• Social profiles: LinkedIn company page, Instagram, Twitter, Facebook, YouTube
• App store presence (if applicable)
• Review sites (G2, Capterra, Trustpilot, Google Reviews)

Business signals:

• Recent funding rounds
• Job postings (roles, departments expanding)
• Press mentions (last 6 months)
• Partnership announcements
• Product launches
• Awards or recognition
• Competitor landscape (who else is in their space)

Compile:

COMPANY RECORD
---
Company name: [legal name]
Domain: [primary domain]
Industry: [vertical]
Sub-industry: [niche]
Employee count: [range]
Revenue estimate: [range]
Founded: [year]
HQ: [city, country]
Funding: [status + total raised]
Platform: [ecommerce platform]
Tech stack: [key technologies detected]

Social profiles:
  LinkedIn: [URL]
  Instagram: [handle]
  Twitter: [handle]
  Facebook: [URL]
  YouTube: [URL]

Recent signals:
  Hiring: [roles if any]
  Funding: [recent round if any]
  Press: [notable mentions]
  Product: [recent launches]

Phase 4: Email Validation

If validate_email = true:

ZeroBounce Path (if configured)

• Submit email to ZeroBounce API
• Get: status (valid/invalid/catch-all/spamtrap/abuse/unknown), sub-status, domain info

Heuristic Path (if no API)

• Check MX records for domain
• Check if domain is active
• Flag disposable email domains
• Flag role-based emails (info@, hello@, support@)

Validation result:

Email validation:
  Address: [email]
  Status: [valid/invalid/risky/unknown]
  Type: [personal/role-based/freemail]
  Deliverability: [HIGH/MEDIUM/LOW]
  Risk flags: [catch-all, new domain, etc.]

Phase 5: ICP Tagging

Match enriched data against icp.segments[]:

For each segment, check:

• Industry match
• Company size match (employee range)
• Revenue range match
• Geography match
• Platform match
• Stage match (post-PMF, mid-market, enterprise)

Tag the lead:

ICP Assessment:
  Best segment match: [segment name]
  Fit score: [1-5]
  Matching criteria: [list of matches]
  Non-matching criteria: [list of misses]
  Fit verdict: STRONG_FIT / MODERATE_FIT / WEAK_FIT / NO_FIT

Phase 6: Service Need Detection

Based on enriched data, flag potential service needs:

• Store development: Outdated store, non-Shopify platform, poor mobile experience
• CRO: High traffic + low conversion signals, basic product pages
• Catalog management: Large SKU count, poor product imagery
• Performance marketing: Low traffic, no paid ads visible, competitor ads running
• SEO: Low organic visibility, thin content, no blog
• Email marketing: No email capture visible, no post-purchase flow

Detected needs:
  1. [Service] -- [signal that indicates this need] -- Confidence: [HIGH/MEDIUM/LOW]
  2. [Service] -- [signal] -- Confidence: [level]

Phase 7: Output

Return structured JSON:

{
  "enrichment_date": "2026-03-07",
  "enrichment_depth": "standard",
  "contact": {
    "full_name": "Priya Mehta",
    "email": "priya@freshskin.co",
    "email_valid": true,
    "email_type": "personal",
    "title": "Founder & CEO",
    "phone": "+91-98XXXXXXXX",
    "linkedin": "https://linkedin.com/in/priyamehta",
    "twitter": "@priyamehta",
    "location": "Mumbai, India",
    "seniority": "C-level",
    "department": "Executive"
  },
  "company": {
    "name": "FreshSkin Co",
    "domain": "freshskin.co",
    "industry": "Beauty & Skincare",
    "sub_industry": "D2C Skincare",
    "employee_count": "25-50",
    "revenue_estimate": "INR 5-10 Cr/year",
    "founded": 2022,
    "hq": "Mumbai, India",
    "funding": {"status": "Series A", "total_raised": "INR 8 Cr"},
    "platform": "Shopify",
    "tech_stack": ["Shopify", "Klaviyo", "Google Analytics"],
    "social": {
      "linkedin": "https://linkedin.com/company/freshskinco",
      "instagram": "@freshskinco",
      "twitter": "@freshskinco"
    },
    "recent_signals": {
      "hiring": ["Marketing Manager", "Content Creator"],
      "funding": "Series A closed Dec 2025",
      "product_launches": ["New serum line launched Jan 2026"]
    }
  },
  "icp_assessment": {
    "best_segment": "Post-PMF D2C India",
    "fit_score": 4,
    "fit_verdict": "STRONG_FIT",
    "matching": ["industry", "size", "geo", "platform", "stage"],
    "non_matching": []
  },
  "detected_needs": [
    {"service": "CRO", "signal": "Basic product pages, no trust elements", "confidence": "HIGH"},
    {"service": "Catalog Management", "signal": "50+ SKUs with inconsistent photography", "confidence": "MEDIUM"}
  ],
  "enrichment_sources": ["websearch", "linkedin", "zerobounce"],
  "data_completeness": "85%",
  "missing_fields": ["phone", "revenue_exact"],
  "generated_at": "2026-03-07T10:00:00Z"
}

Phase 8: CRM Write and Handoff

If write_to_crm = true:

• Write enriched record to CRM via crm-writer
• Set enrichment_status = "complete"
• Set enrichment_date = today
• Set icp_fit = fit verdict

Handoff options:

• If auto_qualify flag set, trigger lead-qualifier with enriched data
• If auto_route flag set, trigger lead-router after qualification
• Otherwise, present enriched record for manual review

APPROVAL GATE: "Lead enriched. [Data completeness]% complete. Route to qualification?"

Example Usage

Trigger phrases:

• "Enrich this lead: priya@freshskin.co"
• "I got a new inbound lead, enrich them: [name] at [company]"
• "Batch enrich this week's inbound leads"
• "Look up everything about [person] at [company]"
• "Fill in the missing data for [lead]"
• "Enrich and qualify [lead name]"