lead-enrichment

name	lead-enrichment
description	Lead enrichment pipelines: web scraping to structured prospect profiles, company intelligence signals, CRM population — Firecrawl, Clearbit, Apollo patterns

Lead Enrichment Skill

When to activate

• Building a pipeline that turns a raw email/URL list into rich prospect profiles
• Scraping company websites and LinkedIn for firmographic data
• Populating HubSpot/Salesforce records from external research
• Generating ICP scores based on enriched company data
• Monitoring signals (funding rounds, hiring surges, exec changes) for account triggers

When NOT to use

• Single ad-hoc lookups — use a browser or LinkedIn directly
• Bulk B2C consumer data — different regulations and data sources
• When verified data is already in your CRM — don't re-enrich unnecessarily

Instructions

The enrichment pipeline

Raw Input (email / domain / LinkedIn URL)
    ↓
Step 1: IDENTIFY      — resolve email to person + company
Step 2: ENRICH        — fetch firmographic data (company, tech stack, headcount)
Step 3: SIGNAL CHECK  — recent news, funding, hiring, exec changes
Step 4: SCORE         — ICP fit score
Step 5: STORE         — upsert to CRM with enriched fields

Email → person resolution

// Using Hunter.io API
async function resolveEmailToPerson(email: string): Promise<PersonData | null> {
  const res = await fetch(
    `https://api.hunter.io/v2/email-verifier?email=${email}&api_key=${process.env.HUNTER_API_KEY}`
  )
  const data = await res.json()
  if (data.data.status !== 'valid') return null
  return {
    email,
    firstName: data.data.first_name,
    lastName: data.data.last_name,
    company: data.data.organization,
    domain: getDomain(email),
  }
}

// Using Apollo.io (richer data)
async function enrichFromApollo(email: string) {
  const res = await fetch('https://api.apollo.io/v1/people/match', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', 'x-api-key': process.env.APOLLO_API_KEY! },
    body: JSON.stringify({ email }),
  })
  return res.json()
}

Company enrichment via web scraping (Firecrawl)

import FirecrawlApp from '@mendable/firecrawl-js'

const firecrawl = new FirecrawlApp({ apiKey: process.env.FIRECRAWL_API_KEY })

async function enrichCompanyFromWebsite(domain: string): Promise<CompanyData> {
  // Scrape the company website with structured extraction
  const result = await firecrawl.scrapeUrl(`https://${domain}`, {
    formats: ['extract'],
    extract: {
      schema: {
        type: 'object',
        properties: {
          companyName:    { type: 'string' },
          description:    { type: 'string' },
          industry:       { type: 'string' },
          products:       { type: 'array', items: { type: 'string' } },
          techStack:      { type: 'array', items: { type: 'string' } },
          :       { :  },
          :        { :  },
          :   { :  },
        },
      },
    },
  })

   result.  
}

Signal detection (trigger-based outreach)

async function detectTriggerSignals(company: string): Promise<TriggerSignal[]> {
  const signals: TriggerSignal[] = []

  // Funding signals — check TechCrunch, Crunchbase
  const fundingNews = await searchRecentNews(`${company} funding round 2026`)
  if (fundingNews.length > 0) {
    signals.push({ type: 'funding', relevance: 0.9, context: fundingNews[0].title })
  }

  // Hiring signals — engineering hires suggest growth/new projects
  const hiringData = await checkLinkedInJobs(company)
  if (hiringData.engineeringJobCount > 10) {
    signals.push({ type: 'hiring_surge', relevance: 0.7, context: `${hiringData.engineeringJobCount} open engineering roles` })
  }

  // Tech stack changes — new tools = new needs
  const techChanges = await checkBuiltWithHistory(getDomain(company))
  if (techChanges.. > ) {
    signals.({ : , : , :  })
  }

   signals.( b. - a.)
}

Claude-powered profile synthesis

async function synthesiseProspectProfile(
  person: PersonData,
  company: CompanyData,
  signals: TriggerSignal[]
): Promise<ProspectProfile> {
  const { object } = await generateObject({
    model: anthropic('claude-opus-4-7'),
    schema: z.object({
      icpScore:         z.number().min(0).max(100),
      painPoints:       z.array(z.string()),
      outreachAngle:    z.string(),
      bestChannel:      z.enum(['email', 'linkedin', 'cold_call']),
      bestTiming:       z.string(),
      notAGoodFit:      z.boolean(),
      notAGoodFitReason: z.string().optional(),
    }),
    prompt: `Analyse this prospect for a ${process.env.OUR_PRODUCT_DESCRIPTION}.

Person: ${person.firstName} ${person.lastName}, ${person.jobTitle} at 
Company: .  employees. .
Tech stack: 
Recent signals: 

Score their ICP fit, identify pain points we can solve, and suggest the best outreach angle.`,
  })

   { ...person, ...company, signals, ... }
}

Full pipeline

async function enrichLeadList(emails: string[]): Promise<EnrichedLead[]> {
  const results: EnrichedLead[] = []

  for (const email of emails) {
    try {
      console.log(`Enriching ${email}...`)

      const [person, company] = await Promise.all([
        resolveEmailToPerson(email),
        enrichCompanyFromWebsite(getDomain(email)),
      ])

      if (!person || !company) {
        results.push({ email, status: 'not_found' })
        continue
      }

      const signals = await detectTriggerSignals(company.companyName)
      const profile = await synthesiseProspectProfile(person, company, signals)

      // Upsert to HubSpot
      await upsertHubSpotContact(email, profile)

      results.push({ email, status: 'enriched', profile })

      // Rate limiting
      await new Promise( (r, ))
    }  (err) {
      results.({ email, : , : err. })
    }
  }

   results
}

Example

User: Build a pipeline that takes 50 company domains from a CSV, scrapes each website for company data, detects funding and hiring signals, scores ICP fit, and pushes results to HubSpot.

Expected output:

• scripts/enrich-pipeline.ts — reads domains.csv, runs enrichment, writes results.json
• enrichCompanyFromWebsite(domain) — Firecrawl structured extraction
• detectTriggerSignals(company) — funding + hiring + tech signals
• scoreICP(company, criteria) — 0-100 score
• upsertHubSpotContact(email, enrichedData) — creates/updates CRM records
• Progress logging, error capture to failed-enrichments.csv

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