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Name Enrichment

Demographic signals from any name

Name Enrichment estimates demographic signals from a first name — age range, gender with confidence score, and top nationalities by probability. It's a fast, lightweight way to add demographic context to a contact list without any personally identifiable data.

When you have a list of first names and need to understand the audience — for personalization, segmentation, or outreach — this tool gives you probabilistic demographic signals in bulk. It uses large reference datasets, so the estimates come with sample sizes and confidence scores rather than black-box outputs.

What you can do

  • enrich_name — get estimated age, gender with probability, and nationality breakdown for a single first name
  • bulk_enrich — enrich up to 10 names at once using a native batch call for speed

Who it's for

Sales and marketing teams who want to personalize outreach without collecting sensitive data. Audience segmentation analysts who need demographic signals from name data alone. Researchers studying name distribution patterns across nationalities or age groups.

How to use it

  1. Use enrich_name for a single name — pass just the first name, case-insensitive
  2. For multiple names, use bulk_enrich with an array of up to 10 first names — it's faster than calling enrich_name in a loop
  3. Check the probability field on gender and nationality results — low values mean the name is ambiguous or culturally diverse
  4. The nationality result returns all predicted country codes with probabilities, not just the top one

Getting started

Pass first names only — not full names. The tool works on any first name from any culture. For large datasets, batch in groups of 10 using bulk_enrich for the most efficient processing.

Enrich Name

Pass a single first name and get back estimated age, gender (with probability), and top nationalities (with probabilities). All three lookups run concurrently for speed.

Returns: Estimated age, gender with confidence, and nationality probabilities for the given name
Bulk Enrich Names

Pass an array of first names (up to 10) and get back demographic estimates for each one. Uses native batch API — only 3 HTTP calls regardless of how many names. Returns results plus summary counts.

Returns: Array of enriched name objects with age, gender, and nationality estimates, plus summary counts
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v0.032026-03-22
  • Added subtitle, expanded description, and agent instructions
v0.022026-03-21
  • Refactored bulk_enrich to use native batch API — 3 HTTP calls instead of 3×N
v0.012026-03-21
  • Initial release

Connect ToolRouter to use Name Enrichment in Claude, ChatGPT, Copilot, and more — no code required.

Connect ToolRouter

What you can do with Name Enrichment

Personalise Outreach by Name

Infer gender and nationality from a contact's name to personalise salutations and communication style at scale.

  1. Provide a contact name or a short list of names
  2. Ask: "Use name-enrichment to enrich these names and infer gender and origin"
  3. Claude returns enrichment signals for each name
  4. Ask Claude to recommend a salutation and any cultural context for personalising your outreach to each contact

Analyse Audience Demographics

Run bulk name enrichment across a customer or contact list to infer demographic composition for research and personalisation.

  1. Provide your name list (CSV format or pasted names work)
  2. Ask: "Use name-enrichment to bulk_enrich these names and infer gender and origin for each"
  3. Claude returns enrichment data across the list
  4. Ask Claude to summarise the demographic composition and identify any notable patterns or gaps

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Frequently Asked Questions

Does this work on full names or just first names?

Use first names only. `enrich_name` is case-insensitive and is designed for a single given name, not a full person record.

What kind of signals does it estimate?

It returns an estimated age range, gender with confidence, and nationality probabilities for the name you pass in.

Can I enrich a list of names at once?

Yes. `bulk_enrich` handles up to 10 names per call, which is faster than looping one name at a time.

What does a low probability mean?

It means the model is less certain about the guess. That is especially important for ambiguous names where gender or nationality can vary widely.