HubSpot Contact Enrichment

• Auto-research new leads
• Keep contact data current
• Provide sales context automatically
• Generate talking points for outreach
• Track enrichment history

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Overview

1. Receives webhook from HubSpot (contact created/updated)
2. Looks up full contact details
3. Searches web for company news and info
4. Uses AI to analyze and summarize intelligence
5. Updates contact with enrichment data
6. Creates timeline event to track enrichment

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What You’ll Need

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HubSpot Setup

• Trigger: Contact created OR Contact property changed
• Action: Send webhook to Agent.AI
• Payload includes: contact_id, contact_email, contact_company

• company_overview (Multi-line text) - AI-generated company summary
• recent_news (Multi-line text) - News summary
• talking_points (Multi-line text) - Sales talking points
• relevance_score (Number) - 1-10 priority rating
• outreach_approach (Multi-line text) - Recommended approach
• last_enriched (Date) - Track when enriched

• Read Contacts
• Write Contacts
• Read Timeline Events
• Write Timeline Events

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Agent.AI Setup

• Webhook Trigger
• Lookup HubSpot Object (V2)
• Get Search Results (web search)
• Invoke LLM
• Update HubSpot Object (V2)
• Create Timeline Event (V2)

• Web search API access (Google, Bing, or similar)
• LLM access (OpenAI, Anthropic, etc.)

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Step-by-Step Setup

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Step 1: Create the Agent.AI Workflow

• Copy the webhook URL (you’ll need this for HubSpot)
• Expected variables from HubSpot:
  • _hubspot_portal (automatically included)
  • contact_id (contact’s HubSpot ID)
  • contact_email (contact’s email)
  • contact_company (contact’s company name)

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Step 2: Setup HubSpot Workflow

1. Go to Automation → Workflows
2. Create workflow
3. Trigger: Contact created OR Contact property “Email” is known
4. Add action: Send webhook
5. Webhook URL: Paste Agent.AI webhook URL from Step 1
6. Method: POST
7. Payload:

{
  "_hubspot_portal": "[portal.id]",
  "contact_id": "[contact.hs_object_id]",
  "contact_email": "[contact.email]",
  "contact_company": "[contact.company]"
}

8. Save and activate

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Step 3: Lookup Full Contact Details

• Object Type: Contacts
• Lookup by: Lookup by Object ID
• Object ID: Click {} → select contact_id (from webhook)
• Retrieve Properties: Click ”+ Add Property” and select:
  • firstname
  • lastname
  • email
  • company
  • jobtitle
  • phone
  • city
  • state
  • country
  • industry
  • hs_object_id
• Retrieve Associations: Select “Companies” (get associated company records)
• Output Variable: contact_data

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Step 4: Web Search for Company Info

• Query: Type text and insert variables:
  • Click {} → contact_data → properties → company
  • Type ” news funding products recent”
• Number of Results: 5
• Output Variable: web_research

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Step 5: AI Enrichment Analysis

• Prompt:

Analyze this contact and create an enrichment summary:

Contact Information:
- Name: [contact first name] [contact last name]
- Title: [contact job title]
- Company: [contact company]
- Industry: [contact industry]
- Location: [contact city], [contact state]

Recent Company News & Information:
[web research results]

Please provide:
1. Company overview (2-3 sentences describing the company)
2. Recent news summary (key highlights from search results)
3. Talking points for sales (3-5 specific conversation starters)
4. Contact relevance score (1-10, where 10 is highest priority)
5. Recommended outreach approach (1-2 sentences)

Return as JSON with keys: company_overview, news_summary, talking_points, relevance_score, outreach_approach

• Model: gpt-4 (or your preferred LLM)
• Output Variable: enrichment_insights

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Step 6: Update Contact with Enrichment

• Object Type: Contacts
• Identify by: Lookup by Object ID
• Identifier: Click {} → select contact_id (from webhook)
• Update Properties: Click ”+ Add Property” and select custom properties:
  • company_overview: Click {} → enrichment_insights → company_overview
  • recent_news: Click {} → enrichment_insights → news_summary
  • talking_points: Click {} → enrichment_insights → talking_points
  • relevance_score: Click {} → enrichment_insights → relevance_score
  • outreach_approach: Click {} → enrichment_insights → outreach_approach
  • last_enriched: Type [now] or use current date variable
• Output Variable: updated_contact

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Step 7: Log Enrichment Activity

• Object Type: Contacts
• Target Object ID: Click {} → select contact_id
• Event Type: contact_enriched
• Event Title: “Contact Enriched with AI Insights”
• Event Description: Type “Enriched contact with company research and AI analysis. Relevance score: ” then click {} → enrichment_insights → relevance_score
• Event Properties: (optional)
  Copy

  Ask AI

  sources=[web_research]
  ai_model=gpt-4
  

• Event Timestamp: Leave blank (uses current time)
• Output Variable: enrichment_event

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How It Works

1. Contact created in HubSpot (e.g., from form submission)
2. HubSpot workflow fires webhook to Agent.AI with contact ID
3. Lookup gets full contact profile from HubSpot → contact_data
4. Web Search finds recent company news → web_research
5. AI Analysis combines contact + news → generates insights → enrichment_insights
6. Update writes enrichment data to contact record in HubSpot
7. Timeline Event logs that enrichment occurred

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Example Output

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What the AI Generates

{
  "company_overview": "Acme Corp is a fast-growing enterprise AI platform company based in San Francisco. Recently raised $50M Series B led by Sequoia Capital, indicating strong investor confidence and expansion plans. Focus on enterprise machine learning solutions.",

  "news_summary": "Recent $50M Series B funding announced. New enterprise features launched including advanced analytics and custom model training. Expanding engineering team by 50% in next 6 months. Featured in TechCrunch for innovative AI approach.",

  "talking_points": "1. Congratulate on recent $50M Series B funding\n2. Discuss new enterprise features and how they align with engineering priorities\n3. Reference expansion plans and potential partnership opportunities\n4. Mention TechCrunch feature and industry recognition\n5. Connect on engineering team growth and talent needs",

  "relevance_score": 9,

  "outreach_approach": "High-priority contact. VP of Engineering at well-funded, rapidly growing company. Lead with technical value proposition and enterprise case studies. Timing is ideal given their product launch and team expansion."
}

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What Appears in HubSpot

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Customization Ideas

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Different Trigger Conditions

• Trigger on specific forms (only enrich demo requests)
• Trigger on lifecycle stage change (enrich when MQL)
• Trigger on company property change (re-enrich when company changes)

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Industry-Specific Research

• Tech companies: “[company] funding product launch tech news”
• Manufacturing: “[company] acquisitions capacity expansion news”
• Healthcare: “[company] FDA approvals clinical trials news”

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Different Enrichment Focus

Focus on: buying signals, budget indicators, decision maker access, competitive landscape

Focus on: company culture, growth trajectory, engineering team size, tech stack

Focus on: strategic initiatives, market positioning, partner ecosystem, expansion plans

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Conditional Enrichment

• Only enrich if jobtitle contains “VP”, “Director”, “C-level”
• Only enrich if relevance_score > 7
• Only enrich if company size > 100 employees

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Multi-Language Support

• Detect country from contact data
• Adjust web search language
• Request AI response in appropriate language

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Troubleshooting

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Webhook Not Firing

1. HubSpot workflow not active
2. Webhook URL incorrect
3. Contact doesn’t meet trigger criteria

1. Check HubSpot workflow status (Automation → Workflows)
2. Verify webhook URL matches Agent.AI URL exactly
3. Test with a contact that definitely meets trigger conditions
4. Check HubSpot workflow execution history

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No Web Results

1. Company name is generic or missing
2. Search API limit reached
3. Company is very small/new

1. Check contact_data.properties.company has a value
2. Add fallback: If no company, skip web search
3. Broaden search query (remove specific terms)
4. Add If Condition to skip search if company is empty

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AI Response Not Formatted

1. LLM didn’t follow format instructions
2. Web results overwhelming token limit

1. Make prompt more explicit: “Return ONLY valid JSON”
2. Reduce web search results from 5 to 3
3. Try GPT-4 instead of GPT-3.5 (better at structured output)
4. Add error handling with Set Variable action

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Properties Not Updating

1. Custom properties not created in HubSpot
2. AI returned unexpected format
3. Variable path wrong

1. Create all custom properties in HubSpot first
2. Check execution log - what did AI return?
3. Verify variable paths: enrichment_insights.company_overview not company_overview

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Enrichment Too Slow

1. Web search slow
2. LLM processing large context
3. Multiple API calls stacking

1. Reduce web search results (5 → 3)
2. Simplify AI prompt
3. Use faster LLM model (GPT-3.5 instead of GPT-4)
4. Consider async processing

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Tips & Best Practices

• Test with a few contacts before activating for all
• Create all custom properties in HubSpot first
• Use specific, relevant web search queries
• Monitor LLM costs (each contact = 1 LLM call)
• Review AI-generated insights for accuracy
• Add timestamp to track when enriched
• Log enrichment to timeline for audit trail

• Enrich every contact (filter for quality leads only)
• Use vague search queries (be specific)
• Forget to handle missing company names
• Skip error handling for web search failures
• Enrich contacts without email or company data
• Re-enrich same contact multiple times per day

• Only enrich contacts above certain score threshold
• Use cheaper LLM for simple enrichment
• Limit web search to 3 results
• Add cooldown period (don’t re-enrich within 30 days)

• Web search is public data only
• Don’t store sensitive info in custom properties
• Follow GDPR/privacy regulations
• Allow contacts to opt out of enrichment

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Related Resources

• Lookup HubSpot Object (V2)
• Update HubSpot Object (V2)
• Create Timeline Event (V2)

• HubSpot Deal Analysis - Similar AI analysis pattern
• HubSpot Customer Onboarding - Multi-step automation example