Lead Hand

Autonomous lead generation that discovers, enriches, and delivers qualified leads on a schedule

Overview

Lead Hand is an AI-powered lead generation engine that continuously discovers prospects, enriches their profiles with business intelligence, and delivers qualified leads on your schedule.

Category: Data
Icon: 📊

What It Does

1. Define Target Profile

   Configure your ideal customer profile (industry, role, company size, geography)

2. Discover Leads

   Multi-source web research to find decision-makers matching your criteria

3. Enrich & Score

   Gather company data, growth signals, and score each lead by fit

4. Deduplicate

   Cross-reference against your existing database to avoid duplicates

5. Deliver Reports

   Generate CSV/JSON/Markdown reports on your schedule (daily, weekly, etc.)

Configuration

Target Profile

Setting	Description	Example
Target Industry	Industry vertical to focus on	SaaS, fintech, healthcare
Target Role	Decision-maker titles	CTO, VP Engineering, Head of Product
Company Size	Filter by employee count	startup (1-50), smb (50-500), enterprise (500+)
Geographic Focus	Region to prioritize	US, Europe, APAC, global

Lead Discovery

Setting	Options	Description
Lead Source	web_search, linkedin_public, crunchbase, custom	Primary discovery method
Leads Per Report	10, 25, 50, 100	Number of leads per report
Delivery Schedule	daily_7am, daily_9am, weekdays_8am, weekly_monday	When to generate reports

Enrichment Settings

Setting	Depth	What You Get
Basic	Quick	Name, title, company
Standard	Balanced	+ company size, industry, tech stack
Deep	Thorough	+ funding, recent news, social profiles

Activation

Basic Setup

openfang hand activate lead

Configure your target profile:

openfang hand config lead \
  --set target_industry="SaaS" \
  --set target_role="CTO, VP Engineering" \
  --set company_size="smb" \
  --set geo_focus="US" \
  --set delivery_schedule="daily_9am"

Example Workflow

Target: SaaS companies (50-500 employees) in the US
Role: CTOs and VP Engineering
Delivery: Daily at 9 AM, 25 leads per report

> Find leads for B2B SaaS dev tools targeting engineering leaders

Lead Hand will:

1. Construct 5-10 search queries combining your criteria
2. Execute web research across multiple sources
3. Discover 50-75 raw leads (2-3x your target)
4. Enrich each lead with company data
5. Score and rank by fit
6. Deduplicate against existing database
7. Generate lead_report_2026-03-06.csv with top 25 leads

How It Works

1. Target Profile Construction

Builds an Ideal Customer Profile (ICP) from your settings:

{
  "industry": "SaaS",
  "roles": ["CTO", "VP Engineering", "Head of Product"],
  "company_size": {"min": 50, "max": 500},
  "geography": "US",
  "signals": ["hiring", "funding", "product launch"]
}

Stored in the knowledge graph for consistency across runs.

2. Lead Discovery

Executes multi-query web research:

Query construction examples:

"SaaS CTO hiring US"
"SaaS companies series B funding"
"top SaaS startups 2026"
"50-500 employee SaaS companies US list"
"B2B SaaS engineering leaders"

For each query:

1. web_search to find results
2. web_fetch promising company websites, LinkedIn profiles, job boards
3. Extract structured data:
   • Person: name, title, company
   • Company: URL, industry, employee count
   • Signals: hiring, funding, news

Target: 2-3x your leads_per_report setting for quality filtering.

3. Lead Enrichment

Based on enrichment_depth setting:

Basic Enrichment

• Name, title, company (from discovery)

Standard Enrichment

Additionally fetches:

• Company website → extract employee count, industry, tech stack, product description
• Job boards → hiring signals (growth indicator)

Deep Enrichment

Additionally fetches:

• Recent funding news: web_search "[company] funding round"
• Recent company news: web_search "[company] news 2026"
• Social profiles: web_search "[name] [company] linkedin twitter"

Knowledge graph storage:

knowledge_add_entity({type: "lead", name: "Jane Smith", title: "CTO"})
knowledge_add_entity({type: "company", name: "Acme SaaS Inc", industry: "SaaS"})
knowledge_add_relation({from: "Jane Smith", to: "Acme SaaS Inc", type: "works_at"})

4. Scoring & Ranking

Each lead scored 0-100:

Criteria	Points	Example
ICP Match	+30	Industry, role, size, geo all match
Growth Signals	+20	Hiring, funding, recent news
Enrichment Completeness	+20	All fields populated
Recency	+15	Company active recently
Accessibility	+15	Public contact info available

Example:

Jane Smith, CTO @ Acme SaaS Inc
Score: 85/100
- ICP match: 30/30 (SaaS, CTO, 120 employees, US)
- Growth: 18/20 (hiring 3 engineers, Series B last year)
- Enrichment: 20/20 (full profile)
- Recency: 12/15 (news from 2 months ago)
- Accessibility: 5/15 (LinkedIn public, email pattern inferred)

5. Deduplication

Compares against leads_database.json:

// Normalize and match
const key = `${normalizeCompanyName(lead.company)}_${normalizeName(lead.name)}`

if (existingLeads.has(key)) {
  // Update existing lead with new enrichment data
  leads[key] = mergeEnrichment(existing, newData)
} else {
  // Add new lead
  leads[key] = newLead
}

6. Report Generation

Generate report in configured format:

CSV Format

Name,Title,Company,Company URL,Industry,Company Size,Score,Discovery Date,Notes
Jane Smith,CTO,Acme SaaS Inc,https://acmesaas.com,SaaS,120,85,2026-03-06,"Hiring engineers, Series B funded"

JSON Format

[
  {
    "name": "Jane Smith",
    "title": "CTO",
    "company": "Acme SaaS Inc",
    "company_url": "https://acmesaas.com",
    "industry": "SaaS",
    "size": 120,
    "score": 85,
    "discovered": "2026-03-06",
    "enrichment": {
      "funding": "Series B, $15M",
      "hiring": true,
      "tech_stack": ["React", "Python", "PostgreSQL"]
    }
  }
]

Markdown Table Format

| # | Name | Title | Company | Score | Signal |
|---|------|-------|---------|-------|--------|
| 1 | Jane Smith | CTO | Acme SaaS Inc | 85 | Hiring, Series B |

Saved as: lead_report_YYYY-MM-DD.{csv,json,md}

Output

Lead Hand generates:

File	Description
lead_report_YYYY-MM-DD.*	Daily/weekly lead report in your chosen format
leads_database.json	Cumulative database of all discovered leads

Dashboard Metrics

• Leads Found — Total unique leads discovered
• Reports Generated — Number of reports delivered
• Last Report — Date of most recent report
• Unique Companies — Count of unique companies in database

Scheduling

Lead Hand runs automatically on your configured schedule:

# Check active schedules
openfang schedule list

# Example output:
Schedule: lead-hand-daily
Frequency: daily at 09:00
Last run: 2026-03-06 09:00:00
Next run: 2026-03-07 09:00:00

Manual trigger:

> Generate a lead report now

Tips & Best Practices

For best results:

• Start with Standard enrichment depth — Deep can be slow for large batches
• Use specific role titles — “VP Engineering” works better than “executive”
• Combine with Researcher Hand to verify company fit before outreach
• Export to CSV and import into your CRM
• Review leads weekly to refine your ICP criteria

Common Issues

“No leads found”
Broaden your criteria — try removing geography filter or increasing company size range.

“Too many duplicate leads”
Lead Hand deduplicates automatically. If you’re seeing repeats, delete leads_database.json to reset.

“Enrichment data missing”
Some companies have minimal public data. Lower scoring leads often have incomplete profiles.

“Wrong industry/role in results”
Web search can return tangential results. Check your target_industry and target_role settings are specific.

Advanced Usage

Custom Lead Sources

Find leads from companies mentioned in this Crunchbase list: [URL]

Competitor Targeting

Find decision-makers at companies using [Competitor Product]

Event-Based Discovery

Find CTOs at companies that recently raised Series A funding in fintech

Export & Integration

# Export to CRM-ready format
openfang hand run lead --export-format salesforce

# Or use the JSON output with custom scripts
cat lead_report_2026-03-06.json | jq '.[] | select(.score > 80)'

Next Steps

Researcher Hand

Deep-dive research on your top leads

Collector Hand

Monitor competitor companies continuously