# Access the extracted data
company = CompanyData(**data["data"])
print(f"Company: {company.name}")
print(f"Funding: {company.funding_total}")
print(f"Employees: {company.employee_count}")
print(f"Industry: {company.industry}")
print(f"Founded: {company.founded_year}")

```

```plaintext
Company: OpenAI
Funding: null
Employees: 251-500
Industry: Artificial Intelligence (AI)
Founded: 2015

```

In a later step, we will integrate this process into our app but will use the `batch_scrape_urls` method instead of `extract` to enable concurrent scraping.

## Building the Funding Data Scraper Step-by-Step

We will take a top-down approach to building the app: starting with the high-level UI components and user flows, then implementing the underlying functionality piece by piece. This approach will help us validate the app’s usability early and ensure we’re building exactly what users need.

### Step 1: Adding brief app information

We turn our focus to the `src/app.py` file and make the following imports:

```python
import streamlit as st
import pandas as pd
import anthropic
from typing import List
from dotenv import load_dotenv