enrich-companies

Enrich a CSV of companies with revenue, credit score, employees, and financial data. Powered by the Score API — 250M+ companies from 40+ countries.

npx enrich-companies leads.csv -o enriched.csv

That's it. No signup needed. Free tier: 50 lookups/month.

Why?

ZoomInfo costs $15,000/yr. Clearbit is dead. D&B charges per lookup. This tool is free for 50 lookups/month.

| | enrich-companies | ZoomInfo | Clearbit | D&B | |---|---|---|---|---| | Price to start | $0 | $15,000/yr | Shut down | Per-lookup | | Company records | 250M+ | ~100M | Was ~44M | ~500M | | Signup required | No | Yes | N/A | Yes | | Self-hosted option | Yes | No | N/A | No | | Open source | Yes | No | No | No |

How It Works

1 command. 0 dependencies. Calls the Score API (250M+ companies, 40+ countries). No signup needed.

1. You pass a CSV with a column of company names
2. Each name is looked up against the Score database
3. Matched records are appended as new columns to your CSV
4. Output goes to a file or stdout

Installation

npm install -g enrich-companies

Or use directly with npx (no install needed):

npx enrich-companies input.csv -o output.csv

Usage

# Basic: enrich a CSV file
enrich-companies companies.csv -o enriched.csv

# Filter by country
enrich-companies leads.csv --country IT -o italian-leads.csv

# Specify which column contains company names
enrich-companies data.csv --column "Company Name" -o enriched.csv

# Choose which fields to add
enrich-companies leads.csv --fields revenue,score,grade,employees -o enriched.csv

# Use with API key for higher limits
enrich-companies leads.csv -k sk_live_your_key -o enriched.csv

# Pipe from stdin
cat companies.txt | enrich-companies - -o results.csv

# Dry run (see what would be enriched)
enrich-companies leads.csv --dry-run

What Gets Added

For each company in your CSV, enrich-companies adds:

| Field | Description | |-------|-------------| | score_revenue | Annual revenue (EUR) | | score_employees | Employee count | | score_score | Credit score 0-100 | | score_grade | Letter grade (AA to E) | | score_country | ISO country code | | score_city | City | | score_sector_desc | Industry description |

Example

Input (leads.csv):

Company,Contact,Email
Tesla,John,[email protected]
Ferrero,Maria,[email protected]
SAP,Hans,[email protected]

Output (enriched.csv):

Company,Contact,Email,score_revenue,score_employees,score_score,score_grade,score_country,score_city,score_sector_desc
Tesla,John,[email protected],96773000000,140000,85,A,US,Austin,Motor Vehicle Manufacturing
Ferrero,Maria,[email protected],19300000000,48697,75,BBB,IT,Alba,Food Manufacturing
SAP,Hans,[email protected],35000000000,107000,88,A,DE,Walldorf,Software Publishing

Data Coverage

250M+ companies across 40+ countries. Top coverage:

Brazil 47M | USA 39M | Australia 20M | France 17M | UK 14M | Germany 10M | India 8M | Japan 7M | Italy 6M | Spain 5M

Contributing

Found a bug? Want a feature? Open an issue.

Pull requests welcome — especially for new column auto-detection patterns and output format options.

Related

• Score API — Full REST API
• scala-score — Node.js SDK
• scala-mcp-server — MCP server for AI agents
• Dataset on Kaggle — Free 994K sample
• Dataset on HuggingFace — Free 1M sample

License

MIT — Built by SCALA AI