> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nrev.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Search Company

> Find and enrich company records using firmographic filters—perfect for prospecting, targeting, and segmentation.

## What It Does

* **Finds companies based on filters** like location, employee size, revenue, or keywords
* **Returns firmographic and contact details** like website, phone, and social links
* **Lets you return only the enrichment fields you need** — or everything if left blank
* **Delivers clean, ready-to-use results** for segmentation, scoring, or enrichment

***

## 🏁 Getting Started

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/nurturev/images/Search%20Company%20Node%20config%20screenshot.png" alt="Search Company Node config screenshot" style={{ borderRadius: '0.5rem', width: '100%', margin: '1.5rem 0' }} />
</Frame>

<Steps>
  <Step title="Add the Node">
    Drag **Search Company** into your flow.
  </Step>

  <Step title="Set Your Filters">
    Choose at least one filter: `Location`, `Employee Count`, `Min/Max Revenue`, or `Keywords`.\
    You can enter static values or map dynamic ones using `@Insert Input`.
  </Step>

  <Step title="Set a Limit">
    Pick how many companies to return — default is 100, max is 10,000 (via dropdown).
  </Step>

  <Step title="(Optional) Select Enrichment Fields">
    Choose only the data you actually need — leave blank to return **all available fields**.
  </Step>

  <Step title="Run the Flow">
    Companies matching your filters will be returned as enriched records.
  </Step>
</Steps>

***

## Inputs

### 🛠️ Required Fields

* **Search Criteria (✅)**\
  You must define at least one:\
  `Location`, `Employee Count`, `Min/Max Revenue`, `Keywords`,\
  `Latest Funding Amount`, `Total Funding`, or `Latest Funding Date`\
  *Why it matters:* These filters decide which companies show up in your results.

* **Limit (✅)**\
  Sets how many companies to return per run.\
  *Why it matters:* Keeps your output size and credit usage in check.

* **Enrichment Fields (✅)**\
  Choose which data points to include (or leave blank for all).\
  *Why it matters:* Prevents downstream clutter and keeps things lean.

<Note>All dropdown options are validated — no typos, over-limit values, or manual errors allowed.</Note>

### 🎯 Optional Sub-Fields

* **Location (⚪️)**\
  City, region, or country (e.g., `New York`, `Germany`)\
  *Why you’d use it:* Filter companies based on where they’re headquartered.

* **Employee Count (⚪️)**\
  Select from ranges like `1–10`, `51–200`, or `1000+`\
  *Why you’d use it:* Focus on company size — helpful for ICP or TAM segmentation.

* **Min Revenue / Max Revenue (⚪️)**\
  Annual revenue range (e.g., Min: `1,000,000`, Max: `10,000,000`)\
  *Why you’d use it:* Narrow by company revenue to focus on target bands.

* **Keywords (⚪️)**\
  Comma-separated terms like `fintech`, `machine learning`, `retail`\
  *Why you’d use it:* Match based on company descriptions or focus areas.

* **Latest Funding Amount (⚪️)**\
  Set a min or max to filter by the most recent round size\
  *Why you’d use it:* Find recently funded companies that are growing or spending.

* **Total Funding Raised (⚪️)**\
  Filter by cumulative funding over time\
  *Why you’d use it:* Identify well-backed companies at any stage.

* **Latest Funding Date (⚪️)**\
  Use a min/max date (`YYYY-MM-DD`) to focus on funding recency\
  *Why you’d use it:* Target fresh rounds or avoid outdated data.

***

## 🧾 Available Enrichment Fields

You can choose exactly what fields to return — or leave the field list blank to get **everything**.

### 🔹 Identity & Web Presence

* `org.name`
* `primary_domain`
* `website_url`
* `logo_url`
* `crunchbase_url`
* `angellist_url`
* `linkedin_url`
* `twitter_url`
* `facebook_url`
* `blog_url`
* `linkedin_uid`

### 🔹 Contact Info

* `primary_phone.number`
* `primary_phone.sanitized_number`
* `primary_phone.source`
* `phone`
* `sanitized_phone`
* `languages`

### 🔹 Company Profile

* `founded_year`
* `alexa_ranking`
* `publicly_traded_symbol`
* `publicly_traded_exchange`

<Note>Only the fields you select will be returned — leave it blank to return all available attributes for bulk enrichment.</Note>

***

## Output

The node returns a clean table of matched companies with the enrichment fields you picked.

***

## How It Works

1. You define filters and limit
2. You optionally pick output fields
3. The platform queries the vendor source
4. Matched companies are returned, preserving your original columns

***

## 🚀 Example Use Cases & Prompts

| Use Case           | Setup Example                                   |
| ------------------ | ----------------------------------------------- |
| Prospect by region | `location = Europe`, `keywords = SaaS`          |
| Segment by size    | `employee_count = 51–200`                       |
| Enrich CRM exports | Return `linkedin_url`, `logo_url`, `revenue`    |
| Build ICP list     | Filter by `industry`, `location`, and `revenue` |

***

## ✨ Pro Tips

<Tip>
  Use dynamic values like `@region` or `@industry` to personalize your searches across runs.
</Tip>

<Tip>
  Keep filters focused — narrow ranges return more relevant matches and save credits.
</Tip>

<Tip>
  Looking for what to do next?\
  Drop your results into **Search People** to find contacts, **AI Scorer** to prioritize, or **CSV Write**/**CRM Export** to share with your team — your RevOps superheroes just leveled up! 💪
</Tip>

<Tip>
  Select only the enrichment fields you'll actually use downstream — it saves clutter and improves speed.
</Tip>

***

## ⚠️ Important Considerations

<Warning>
  The **Limit** defaults to 100 — adjust up to 10,000 via dropdown if needed.
</Warning>

<Warning>
  Credits are charged **per page**, not per row — each page = up to 100 companies = 5 credits.
</Warning>

<Warning>
  Output may include suffixes like `_1` if column names clash — naming conflicts are auto-resolved.
</Warning>

***

## 🛠 Troubleshooting & Gotchas

| Symptom                 | What’s Going On              | Quick Fix                       |
| ----------------------- | ---------------------------- | ------------------------------- |
| Blank output            | No companies matched filters | Broaden your filters            |
| Unexpected column names | Clashes with original data   | Look for suffixes like `name_1` |
| High credit usage       | Limit is too large           | Reduce limit or adjust filters  |

***

## 📝 FAQ

<AccordionGroup>
  <Accordion title="Can I run this without an input table?">
    Yep — static filters work too, and you'll get company records as new rows.
  </Accordion>

  <Accordion title="What if I don’t select enrichment fields?">
    You’ll get **all available fields** by default — unless you pick specific ones.
  </Accordion>

  <Accordion title="How are credits calculated?">
    Each page (up to 100 companies) costs **5 credits**.\
    Example: Limit 550 → 6 pages → **30 credits**.
  </Accordion>

  <Accordion title="What if no companies match my filters?">
    You’ll get an empty output and **no credits charged** — smart and safe.
  </Accordion>
</AccordionGroup>

***

## 💰 Pricing

| Action           | Credit Cost        |
| ---------------- | ------------------ |
| Per page fetched | 5 credits per page |

<Note>Credits are based on pages fetched (up to 100 companies). No results = no charge.</Note>

***

<p style={{ fontSize: '1rem', fontWeight: 'bold', marginTop: '1.5rem' }}>
  Search and enrich high-fit companies with precision—ready for scoring, segmentation, or GTM action. 🏢
</p>