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What It Does

  • Retrieves vendor relationships for a target company domain and outputs normalized data.
  • Preserves all input columns while adding vendor-specific columns (vendor_name, vendor_domain, source_url, first_seen_at, last_seen_at).
  • Supports template variables for dynamic domain resolution per row (e.g., @company_domain).
  • Handles column conflicts automatically with numeric suffixing to avoid overwriting existing data.
  • Test mode fetches a single page for fast, deterministic checks before running full-scale enrichment.

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🏁 Getting Started

Get Company Vendors config screenshot
1

Set Company Domain

Enter a static domain (e.g., microsoft.com) or use @ColumnName for dynamic row-based resolution.
2

Configure Limit

Optionally set the max number of vendor records to fetch. Pagination is handled automatically.
3

Run Node

Execute in Test Mode first to preview results, then Standard Mode for full enrichment.

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Inputs

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🛠️ Required Fields

  • Company Domain (✅)
    The domain or URL of the target company (e.g., microsoft.com or https://www.microsoft.com).
    Why it matters: Determines which company’s vendors are retrieved; supports per-row dynamic resolution with @ColumnName.

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🎯 Optional Fields

  • Limit (⚪️)
    Maximum number of vendor records to fetch.
    Why it matters: Controls data volume and credit usage while still ensuring you capture enough vendor relationships.

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Output

  • Adds the following vendor columns to your dataset:
    vendor_name, vendor_domain, source_url, first_seen_at, last_seen_at
  • Preserves all original input columns.
  • Column name conflicts are resolved with numeric suffixes (e.g., vendor_name → vendor_name_1).
✨ If any vendor column name conflicts with your input, a numeric suffix is added automatically to preserve all data.

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How It Works

  1. Validate settings and placeholders (company_domain must be non-empty or a valid column reference).
  2. Load input data if template variables are used.
  3. For each row (or static domain), fetch vendor relationships from PredictLeads using pagination.
  4. Normalize vendor data to standard columns (vendor_name, vendor_domain, source_url, first_seen_at, last_seen_at).
  5. Merge new data with input columns, resolving any conflicts with suffixes.
  6. Output the enriched dataset with all original and new vendor columns.

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🚀 Example Use Cases & Prompts

Use CaseSetup or Prompt Example
Map partner ecosystemFetch vendors for microsoft.com to understand partnerships
Competitive intelligenceUse @company_domain column to enrich competitor list with vendors
Data enrichmentAdd vendor relationships to your CRM dataset for deeper insights

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✨ Pro Tips

  • Use Test Mode first to quickly preview a single page of vendor data without consuming excessive credits.
  • Rename output columns if you plan to join with other datasets (VendorName, VendorDomain) to keep reports clear.
  • When processing multiple rows, reference columns dynamically with @company_domain via the Insert Input button.

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⚠️ Important Considerations

  • Large limits in Standard Mode will use more credits.
  • If a static or template-resolved domain is invalid or empty, the node will fail validation.
  • Column conflicts are handled automatically, but review names to ensure consistency in downstream reports.

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🛠 Troubleshooting & Gotchas

SymptomLikely CauseQuick Fix
Blank outputNo valid company_domain providedVerify static domain or input column references
Unexpected _1 suffixesColumn name conflicts with inputRename outputs for clarity
Fewer vendors than expectedLimit set too low or no vendors foundIncrease limit or verify target company domain

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📝 FAQ

Yes — use a column with domains (@company_domain) as input, and the node will resolve each row dynamically.
New columns will automatically receive a numeric suffix to avoid overwriting.
Test Mode fetches only a single page of results, making it faster and cheaper for validation.

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💰 Pricing

ActionCredit Cost
Row-level vendor fetch1 credit per domain
Credits are consumed per page of results fetched. Using Test Mode helps limit consumption while testing.

Drop this node into your Play to enrich company datasets with vendor insights—automatically. 🚀