# Enrich a company list with founded year, HQ, CEO and size

**Use case:** 

Upload a spreadsheet of company names and websites and ask your question in plain words. The AI researches every row on the web and adds the answers as new columns (founded year, headquarters city, CEO, number of employees), with the same column names on every row. You get your file back as Excel, ready for a CRM or a prospect list.

## Input

```json
{
  "prompts": [
    "For the company {{Company}} ({{Website}}), find the year it was founded, its headquarters city, its current CEO and its approximate number of employees."
  ],
  "spreadsheet_file": "https://api.apify.com/v2/key-value-stores/I40EWpLzQaVq08gCm/records/companies-sample.csv",
  "system_prompt": "You are a helpful, accurate assistant. Use the available tools when they would improve the answer.",
  "model": "google/gemini-2.5-flash",
  "enable_web_search": true,
  "enable_file_parser": false,
  "temperature": 70,
  "max_tokens": 1000,
  "response_format": "json_object",
  "reasoning_effort": "default",
  "provider_sort": "default",
  "max_retries": 2,
  "concurrency": 4
}
```

## Output

```json
{
  "prompt": {
    "label": "Prompt",
    "format": "text"
  },
  "model": {
    "label": "Model",
    "format": "text"
  },
  "response": {
    "label": "Response",
    "format": "text"
  },
  "tools_used": {
    "label": "Tools used",
    "format": "array"
  },
  "cost_usd": {
    "label": "Cost (USD)",
    "format": "number"
  },
  "error": {
    "label": "Error",
    "format": "text"
  }
}
```

## About this Actor

This example demonstrates how to use [Run ChatGPT, Claude, Gemini & DeepSeek in Bulk (No API Key)](https://apify.com/fayoussef/bulk-llm-runner.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/fayoussef/bulk-llm-runner.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/fayoussef/bulk-llm-runner.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).