> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/RightNow-AI/openfang/llms.txt
> Use this file to discover all available pages before exploring further.

# 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

<Steps>
  <Step title="Define Target Profile">
    Configure your ideal customer profile (industry, role, company size, geography)
  </Step>

  <Step title="Discover Leads">
    Multi-source web research to find decision-makers matching your criteria
  </Step>

  <Step title="Enrich & Score">
    Gather company data, growth signals, and score each lead by fit
  </Step>

  <Step title="Deduplicate">
    Cross-reference against your existing database to avoid duplicates
  </Step>

  <Step title="Deliver Reports">
    Generate CSV/JSON/Markdown reports on your schedule (daily, weekly, etc.)
  </Step>
</Steps>

## 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

```bash theme={null}
openfang hand activate lead
```

Configure your target profile:

```bash theme={null}
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:

```json theme={null}
{
  "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:**

```javascript theme={null}
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`:

```javascript theme={null}
// 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

```csv theme={null}
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

```json theme={null}
[
  {
    "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

```markdown theme={null}
| # | 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:

```bash theme={null}
# 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

<Note>
  **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
</Note>

### 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

```bash theme={null}
# 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

<CardGroup cols={2}>
  <Card title="Researcher Hand" icon="flask" href="/hands/researcher">
    Deep-dive research on your top leads
  </Card>

  <Card title="Collector Hand" icon="magnifying-glass" href="/hands/collector">
    Monitor competitor companies continuously
  </Card>
</CardGroup>