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What It Does

  • Finds companies similar to ones you already know — based on firmographics, keywords, tech, and more
  • Takes in one or more domains and returns companies with similarity scores
  • Includes enrichment fields like industry, size, revenue, and technology — customizable
  • Returns high-similarity matches (score 0–100) that fit your ICP

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🏁 Getting Started

Find Lookalikes node configuration screenshot
1

Add the Node

Drag Find Lookalikes into your flow.
2

Enter Lookalike Domains

Provide one or more company domains you want to find matches for.
3

Set Similarity Threshold (Optional)

Choose a minimum similarity score between 0 and 100. Higher = more precise matches.
4

Apply Filters (Optional)

Narrow your lookalikes by industry, country, tech, size, keywords, and more.
5

Set Output Count

Choose how many lookalike companies you want returned.
6

(Optional) Select Enrichment Fields

Return only the data points you need — or leave blank to get all fields.
7

Run the Node

Each lookalike will be returned as a row with score and enrichment data.

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Inputs

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🛠️ Required Fields

  • Lookalike Domains (✅)
    One or more domains to use as the “source” for finding similar companies.
    Why it matters: This is the core input — your known good companies.
  • Number of Companies (✅)
    Set how many lookalikes to return.
    Why it matters: Controls output size and cost.

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🎯 Optional Fields

These optional fields let you narrow your lookalike results or control the output. Use them to improve precision or reduce noise.

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🔍 Filters

  • Industries (⚪️)
    Target companies by their primary industry (e.g. SaaS, Healthcare).
  • Industry Categories (⚪️)
    Broader buckets that group similar industries together (e.g. B2B Tech, Consumer Services).
  • Countries (⚪️)
    Limit matches to companies headquartered in specific countries.
  • Company Size (⚪️)
    Filter by employee count (e.g. 11–50, 500–1000) to match your ICP.
  • Revenue (⚪️)
    Target companies by estimated annual revenue bands.
  • Year Founded (⚪️)
    Filter by founding year to focus on mature, new, or mid-stage companies.
  • Keywords (⚪️)
    Search by descriptive terms found in company profiles (e.g. AI, retail, logistics).
  • Technologies (⚪️)
    Return companies using specific tools (e.g. HubSpot, Salesforce, Snowflake).
  • Include Domains (⚪️)
    Force-include companies by domain, even if they wouldn’t match filters.
  • Exclude Domains (⚪️)
    Prevent specific companies from showing up in results.
  • Exclude Industries (⚪️)
    Filter out industries you don’t want (e.g. Education, Nonprofit).
  • Department Size (⚪️)
    Narrow by team size within functions like Marketing, Sales, or Engineering.
  • Countries Count (⚪️)
    Target companies operating in a specific number of countries (e.g. global vs local).
  • Minimum Similarity Score (⚪️)
    Only return companies with a match score above this threshold (0–100).

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🧾 Enrichment Fields

  • Enrichment Fields (⚪️)
    Choose which data columns to return.
    Why it matters: Reduces noise and keeps downstream tables lean.
    By default: All enrichment fields are returned.
Leave enrichment fields blank to return everything — or select from options like company_keywords, company_technologies, company_revenue, score, etc.

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Output

The node returns a clean table of lookalike companies — one row per match — including a score column to show how similar they are to your input.
Find Lookalikes output example
✨ If your dataset already contains any of the selected column names, suffixes (_1, _2, etc.) will be applied automatically to avoid conflicts.

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How It Works

  1. Takes your source domains and filter inputs
  2. Sends them to the lookalike engine to fetch high-similarity matches
  3. Filters results by your score threshold and other criteria
  4. Appends selected enrichment fields to each match
  5. Adds one row per company to your dataset

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🚀 Use Cases & Prompts

Use CaseSetup Example
ABM ExpansionLookalikes of stripe.com, filtered to SaaS in EMEA
Territory ModelingFind similar firms to zendesk.com in Germany
Mid-Market TargetingScore ≥ 85, employee_count = 51–500, industry = SaaS
Signal-Based ListsMatch monday.com, return those using Salesforce or Drift
Exclude Existing AccountsUse Exclude Domains to block current customers or prospects

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✨ Pro Tips

Use multiple lookalike domains for broader training (e.g. your top 10 customers).
Set similarity to 70+ for higher quality, or 40+ to explore looser matches.
Pair this node with AI Scorer to prioritize matches based on recent funding, size, or fit.

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⚠️ Important Considerations

Output column names may include suffixes if you’ve already used fields like industry, keywords, or score.
Broader filters may return more matches, but lower quality — tune your score threshold accordingly.

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🛠 Troubleshooting & Gotchas

SymptomLikely CauseQuick Fix
No results returnedFilters too strict or high scoreLoosen filters or lower similarity threshold
Missing fieldsEnrichment fields not selectedLeave enrichment blank to return all fields
Duplicate domainsIncluded in both input & outputUse Exclude Domains to remove them

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📝 FAQ

Yes — you can provide a list of domains to find lookalikes for.
Each result has a score (0–100) that shows how closely it matches your inputs.
You’ll get all available fields by default — no data left behind.
That depends on your credit budget and limit — typically 100–1000 per run.

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💰 Pricing

ActionCredit Cost
Per company returned5 credits
You’re only charged for the number of lookalikes returned.
Example: 300 matches = 1500 credits

Turn your best-fit customers into an endless stream of smart, similar prospects — ready for outbound. 🔍✨