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Overview

Vibe Prospecting combines conversational AI with Explorium’s comprehensive B2B database to deliver:
  • Natural language prospecting - Ask for what you need in plain English
  • Smart filtering - AI automatically selects the right filters from 20+ business and prospect attributes
  • Rich data enrichment - Add firmographics, technographics, contacts, funding data, and more
  • Event-based targeting - Find companies experiencing specific business events
  • Bulk operations - Process up to 1,000 entities per query with enterprise-grade performance

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Key Features

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Intelligent Business & Prospect Discovery

Find companies and decision-makers using any combination of filters:
  • Company attributes: Industry, size, revenue, location, age, tech stack, funding status
  • Prospect attributes: Job title, seniority, department, experience, location
  • Business events: Funding rounds, partnerships, office expansions, hiring trends, leadership changes
  • Intent signals: Companies showing buying intent for specific topics (premium feature)

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Comprehensive Data Enrichment

Enhance your results with detailed information: For Businesses:
  • Firmographics (company details, size, revenue)
  • Technographics (complete technology stack)
  • Funding & acquisitions history
  • Financial metrics & SEC insights
  • Workforce trends & department composition
  • LinkedIn posts & engagement
  • Website changes & keyword detection
  • Corporate hierarchies & subsidiaries
For Prospects:
  • Professional & personal email addresses
  • Phone numbers (mobile & direct)
  • Full work history & education
  • LinkedIn profiles & activity
  • Role details & tenure

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The Hub - Your Data Control Center

Manage all your prospecting data in one place:
  • View and manage exported datasets
  • Upload and enrich your own CSV files
  • Exclude specific entities from future searches
  • Track credit usage per dataset
  • Re-import datasets for follow-up searches

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Getting Started

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Basic Query Structure

Simply tell the AI what you’re looking for: 
Find VP of Sales at Series B SaaS companies in California with 100-500 employees
The AI will:
  1. Parse your requirements
  2. Apply appropriate filters
  3. Show you a sample preview with estimated cost
  4. Wait for your confirmation before exporting
Example 1: Finding Decision Makers 
Get me CTOs at fintech companies in New York that raised funding in the last 90 days
What happens:
  • AI identifies entity type: prospects
  • Filters: job_title (CTO), linkedin_category (fintech), location (New York), events (funding)
  • Returns sample with estimated export cost
  • Waits for your approval to export
Example 2: Finding Companies 
Show me B2B SaaS companies with 50-200 employees using Salesforce in their tech stack
What happens:
  • AI identifies entity type: businesses
  • Filters: industry (SaaS), size (50-200), technology (Salesforce)
  • Returns company details in sample preview
  • Shows cost estimate for full export

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Use Cases

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1. Sales Prospecting

Scenario: Find decision-makers at high-growth companies 
Find VPs of Engineering at companies that increased their engineering department by 20%+ in the last 60 days
Why it works: Combines role targeting with hiring signals to identify companies actively scaling their teams.

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2. Market Research

Scenario: Analyze market segments 
Get me statistics on healthcare software companies in the US with 100-1000 employees, 
broken down by revenue range
Why it works: Uses the statistics endpoint to provide market insights without consuming credits on individual records.

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3. Event-Driven Outreach

Scenario: Reach companies during key moments 
Find marketing directors at companies that just opened new offices in the last 30 days
Why it works: Targets prospects during organizational changes when they’re more likely to need new solutions.

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4. Account Expansion

Scenario: Find new contacts at existing customers 
I have a list of 50 companies. Find all C-level executives in the sales and marketing 
departments at these companies.
Why it works: Upload your customer list to the Hub, then use it as a reference to find additional decision-makers.

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5. Competitive Intelligence

Scenario: Monitor competitor customers 
Find companies using [Competitor Product] with 500+ employees in the enterprise software space
Why it works: Identifies prospects using competitor technology who might be open to switching.

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Best Practices

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Writing Effective Queries

Be Specific About What You Need ✅ Good: “Find CTOs at Series B fintech companies in San Francisco with 50-200 employees” ❌ Avoid: “Find some tech people” Combine Multiple Criteria ✅ Good: “Sales directors at SaaS companies that raised funding in the last 90 days and use Salesforce” ❌ Avoid: Making separate searches that could be combined Use Natural Language ✅ Good: “Companies that recently expanded their engineering teams” ❌ Avoid: Trying to write filter syntax manually

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Understanding Entity Types

Vibe Prospecting works with two entity types: Choose “Prospects” when you need:
  • Individual people/contacts
  • Email addresses or phone numbers
  • Specific job titles or roles
  • Decision-makers at companies
Choose “Businesses” when you need:
  • Company information only
  • Firmographics or technographics
  • Market research data
  • Lists of organizations
The AI automatically determines entity type, but mentioning “people,” “contacts,” or “executives” will ensure prospect results.

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Optimizing Credit Usage

1. Start with statistics Before running expensive prospect searches: 
What's the market size of cybersecurity companies with 100-500 employees in the US?
This helps validate your target market without using credits. 2. Review samples before exporting Always check the sample preview to ensure:
  • Results match your expectations
  • Data quality meets your needs
  • Cost is acceptable for your budget
3. Use exclusion lists Prevent duplicate spending: 
Find new prospects, but exclude entities I've already exported
4. Be precise with enrichments Only request enrichments you’ll actually use: 
Enrich with contacts only
vs. 
Enrich with everything available

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Handling Large Datasets

For queries returning 1,000+ results: The system caps at 1,000 results per query. To get more: 
First query: "Find SaaS companies in California with 100-500 employees"
Second query: "Find SaaS companies in New York with 100-500 employees"
Split by geography, industry subcategory, or company size.

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Working with Enrichments

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When to Enrich

Enrichments add detailed information but consume additional credits. Request them when:
  1. You need contact information: Always use enrich-prospects-contacts for emails/phones
  2. You need company details: Use enrich-business-firmographics for basic company info
  3. You need technology insights: Use enrich-business-technographics for full tech stack
  4. You need funding data: Use enrich-business-funding-and-acquisitions for investment history

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Enrichment Examples

Getting Email Addresses 
Find marketing managers at fintech companies, and get their email addresses
AI automatically applies: enrich-prospects-contacts Getting Company Technology Stack 
Show me which technologies are used by top e-commerce companies
AI automatically applies: enrich-business-technographics Multiple Enrichments 
Find CTOs at recently funded companies and get their emails, plus company funding details
AI applies both: enrich-prospects-contacts and enrich-business-funding-and-acquisitions

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Understanding Costs

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Credit System

Vibe Prospecting uses Explorium credits:
  • Base fetch: ~1 credit per entity
  • Enrichments: Additional credits per enrichment type per entity
  • Events: Additional credits when fetching detailed event information

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Cost Estimation

Before any export, you’ll see: 
Export Cost: 450 credits

Sample Preview (5 of 150):
[Table with sample data and cost_in_credits column]

Ready to Export? Get all 150 prospects with full details
👉 Say "export" to download the complete dataset as CSV 👈
Never auto-exports - you always approve costs first.

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Using The Hub

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Accessing The Hub

[IMAGE PLACEHOLDER: Hub dashboard showing datasets, credit usage, and upload button] The Hub is your central workspace for managing all prospecting data.

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Managing Exported Datasets

View Your Datasets All exported data appears in the Hub with:
  • Dataset name (auto-generated based on search)
  • Entity count
  • Export date
  • Credit cost
  • Status (ready, processing, error)
Re-Import Previous Searches 
Load my dataset from last week about SaaS CTOs
The AI will:
  1. Search your Hub for matching datasets
  2. Show available options if multiple matches
  3. Load the data into your current session
  4. Allow you to enrich, filter, or export again

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Excluding Entities

Automatic Exclusion Every export automatically adds entities to your exclusion list, preventing duplicates in future searches. Manual Exclusion 
Exclude all prospects from my "Q4 2024 Outreach" dataset
Using Exclusion Lists 
Find new VPs of Sales at fintech companies, but exclude anyone I've already contacted
AI automatically applies your default exclusion list.

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Uploading CSV Files

Use Cases for CSV Upload:
  1. Enrich your existing data: Upload company names/domains to get firmographics, tech stack, contacts
  2. Find contacts at your accounts: Upload customer list to find decision-makers
  3. Clean and standardize data: Upload messy data to get standardized Explorium IDs and enriched fields
How to Upload [IMAGE PLACEHOLDER: CSV upload interface with drag-and-drop area] Step 1: Prepare Your CSV Your CSV should contain: For businesses: 
company_name,domain
Explorium,explorium.ai
Anthropic,anthropic.com
For prospects: 
full_name,company_name,email
John Smith,Acme Corp,[email protected]
Jane Doe,Acme Corp,[email protected]
Step 2: Upload to Hub
  1. Click “Upload CSV” in the Hub
  2. Drag and drop your file or click to browse
  3. Map your CSV columns to Explorium fields
  4. Review mapping and confirm upload
Step 3: Process Your Data Once uploaded, tell the AI what you want: 
Find all C-level executives at the companies in my uploaded list
Or: 
Enrich my uploaded companies with technographics and funding data
Step 4: Export Results Review the sample, approve costs, and export as usual.

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Working with Referenced Data

Refining Previous Searches 
From my last search of SaaS companies, find only those in California
AI uses businesses_reference_table to filter previous results without re-fetching. Cross-Referencing Datasets 
Find prospects at companies from my "Series B SaaS" dataset who joined in the last 6 months
Combines company data from one search with prospect filtering in another.

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Advanced Features

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Business Events

Target companies experiencing specific moments: Available Event Types: Growth & Expansion:
  • new_funding_round
  • ipo_announcement
  • new_office
  • new_partnership
  • new_product
Organizational Changes:
  • increase_in_engineering_department
  • increase_in_sales_department
  • increase_in_marketing_department
  • hiring_in_[department]_department
  • employee_joined_company
Challenges & Opportunities:
  • cost_cutting
  • closing_office
  • merger_and_acquisitions
  • lawsuits_and_legal_issues
  • outages_and_security_breaches
Example: 
Find companies that raised Series B funding in the last 60 days and are hiring 
aggressively in engineering

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Intent Signals (Premium)

Target companies showing buying intent: 
Find companies with high intent for "cybersecurity solutions" in the finance industry
Intent levels:
  • emerging_intent: Early research stage
  • high_intent: Active evaluation
  • very_high_intent: Near purchase decision

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Prospect Events

Track individual career changes: Available Prospect Event Types:
  • prospect_changed_role: Promoted or changed positions
  • prospect_changed_company: Moved to a new company
  • prospect_job_start_anniversary: Tenure milestones
Example: 
Find VPs who were recently promoted in the last 30 days at enterprise software companies

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Statistics & Market Insights

Get aggregated data without consuming credits on individual records: 
Show me the distribution of SaaS companies by revenue range in the US
Perfect for:
  • Market sizing
  • TAM analysis
  • Industry research
  • Investment thesis validation

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Query Examples by Industry

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SaaS & Technology

Find product managers at B2B SaaS companies with 100-500 employees that use React 
and are hiring in engineering

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Financial Services

Show me CFOs at fintech companies that raised Series A or B in the last year with 
$10M-$50M in revenue

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Healthcare

Find CTOs at healthcare technology companies with 200+ employees using AWS and 
HIPAA compliance tools

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E-commerce & Retail

Get marketing directors at D2C e-commerce brands with $5M-$25M revenue that recently 
expanded to new offices

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Manufacturing & Industrial

Find operations directors at manufacturing companies with 500-2000 employees that 
recently invested in automation technology

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Troubleshooting

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No Results Found

If your search returns zero results:
  1. Broaden your criteria: Try removing one or two filters
  2. Check geography: Ensure location codes are correct (use “US” not “USA”)
  3. Verify timing: Events older than 90 days may not be captured
  4. Try statistics first: Validate your target market exists
Example: Instead of: 
Find CTOs at Series B fintech companies in Austin using React, Python, and Kubernetes
Try: 
Find CTOs at fintech companies in Texas using React

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Understanding Sample vs. Export

Sample Preview:
  • Shows 5-10 example results
  • For events: shows up to 3 events per entity
  • Includes cost estimate
  • Does NOT consume credits
Full Export:
  • Returns ALL matching results (up to 1,000)
  • For events: returns ALL events per entity (often 10-100+ per entity)
  • Consumes credits as shown in estimate
  • Generates downloadable CSV
Important: The sample preview is NOT the full dataset. Always export to get complete data.

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Dataset Not Ready

If you see “dataset is processing”:
  1. Wait 30-60 seconds - Large datasets take time
  2. Check status - “Ready” means you can export
  3. Try again - Processing usually completes quickly

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Credit Management

Check Your Balance: 
How many credits do I have?
Estimate Before Exporting: Always review the cost estimate before approving export. The system shows:
  • Total credits required
  • Cost breakdown by enrichment type
  • Whether you have sufficient credits
Partial Exports: If you lack credits for a full export, the system may return partial results proportional to your available balance.

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Tips & Tricks

Start broad, then refine: 
Step 1: "Find SaaS companies with 100-500 employees"
Step 2: "From those results, show me only ones using Salesforce"
Step 3: "Now find CTOs at those companies"

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2. Combine Signals

Stack multiple indicators for higher quality: 
Find VPs of Sales at companies that:
- Raised funding in the last 90 days
- Are hiring in sales department
- Use Salesforce and Outreach
- Have 50-200 employees

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3. Use Exclusions Strategically

Create segments: 
Query 1: "Find fintech prospects" (exports 500, all excluded)
Query 2: "Find fintech prospects" (returns 500 NEW prospects)

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4. Leverage Reference Tables

Build on previous work: 
Step 1: Find target companies
Step 2: Enrich with firmographics
Step 3: Use those companies to find multiple personas (CTO, VP Eng, Head of Product)

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5. Validate Before Scaling

Test with small samples: 
"Find 50 prospects matching [criteria]"
Review data quality
If good: "Find 1000 prospects matching [criteria]"

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API Access

Vibe Prospecting is also available via the AgentSource API for programmatic access. Visit the API documentation for:
  • Authentication & setup
  • Endpoint reference
  • Code examples in Python, Node.js, and cURL
  • Webhook integration
  • Rate limits & quotas

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Support & Feedback

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Getting Help

  • In-app: Use the chat to ask questions about features or troubleshoot issues
  • Documentation: Visit developers.explorium.ai for guides and API reference
  • Support: Contact [email protected] for technical assistance

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Providing Feedback

Help us improve Vibe Prospecting:
  • Share feature requests with your account manager
  • Report bugs or data quality issues to support

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What’s Next?

Now that you understand Vibe Prospecting, try these next steps:
  1. Run your first search: Start with a simple query to get familiar
  2. Explore the Hub: Upload a small CSV to test enrichment
  3. Set up exclusions: Configure your default exclusion list
  4. Try different use cases: Experiment with events, intent signals, and statistics
  5. Scale up: Once comfortable, run larger searches and build your prospecting workflow
Happy prospecting! 🎯