Gtm people enrichment

People Enrichment

Enrich contacts with work email, phone number, and/or LinkedIn URL. Returns an updated CSV.

Read .agents/skills/_shared/conventions.md before executing.

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When to Use

• You have a contact list and need emails and/or phone numbers
• Contacts from people-search need enrichment before outreach
• A user-provided CSV has names + companies but missing contact info

Inputs

Input	Required	Source
Contact CSV	Yes	People Search output or user-provided
LinkedIn profile URLs	Recommended	In CSV or from People Search
Company domains	Recommended	In CSV
Enrichment type	Yes	email, phone, or both

Demo Mode

When invoked from the demo flow: email only, skip all phone providers entirely.

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Provider Selection

Email

Ask the user which provider to use. Default recommendation:

Priority	Provider	Hit Rate	Cost	Speed	Notes
1st	FullEnrich v2	80% (SA), 83% (DACH)	0.8 cr/contact	instant	Clear winner
2nd	Pipe0 waterfall	60% (SA), 83% (DACH)	1.0–3.5 cr/contact	~3 min/20 contacts	Solid backup
3rd	BetterContact (email-only)	~60% test / 14% production SA	0.35 cr/contact	30–90 min	Not recommended — slow, unreliable production

Recommended flow: FullEnrich first → Pipe0 for FE misses → skip BC for email.

Phone

Skip entirely in demo mode.

Priority	Provider	Hit Rate	Cost	Speed	Notes
1st	FullEnrich v2	~60% DACH	~0.8 cr/contact	instant	Good DACH coverage. Prefer most_probable_phone.number over phones[0] (phones[0] can be landline, most_probable is mobile)
2nd	Pipe0 phone waterfall	~92% DACH (n=74) · 0% SA	2–7 cr/contact	~30s/batch	Response field: mobile (not phone). Works reliably as FE fallback — on 7 FE contacts missing phones, recovered 5 (71%). Also strong as primary when contacts come from Pipe0 search (68/74 = 92%)
3rd	BetterContact	13–74% (~22% SA, ~53% DACH)	~1–2 cr/contact	5–10+ min for small batches	Unreliable speed — took 10+ min for 2 contacts in DACH test. Retest before production

LinkedIn URL (when missing)

• Pipe0 people:profileurl:name@1 — 0.60 cr/person, needs name + company_name + location_hint
• PhantomBuster Profile Scraper (config key: PB_AGENT_PROFILE) — scrape full profile data from existing LinkedIn URL; use /phantombuster skill with phantom name "LinkedIn Profile Scraper" and CSV path. See _shared/phantombuster.md.

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Execution Protocol

1. Assess Input Data

• Check what fields are available (LinkedIn URL, domain, name, company)
• Determine which enrichment types are needed
• Estimate credit cost: n_contacts × max_cost_per_record
• Confirm credit balance with user before proceeding

2. Test Batch (5–10 contacts)

• Run chosen provider on 5–10 contacts in production
• Inspect every result: email validity status, phone format, match accuracy
• Check hit rate — if below 10%, investigate before proceeding

3. Review with User

• Present test results with hit rate, status breakdown, sample data
• Confirm provider choice and credit spend for full batch
• Get approval

4. Full Batch

• Split into chunks of max 100 contacts per request (all providers)
• Save request/task IDs immediately
• Poll with appropriate timeouts:
  • FE: 15s interval, 600s timeout (usually instant)
  • Pipe0: 30s interval, 600s timeout (≤100 contacts)
  • BC phone: 30s interval, 900s timeout
  • BC email: 60s interval, 3600s timeout
• Save results incrementally after each batch

5. Fallback Pass

• Identify contacts with missing email/phone after primary provider
• Run fallback provider on misses only
• Merge results

6. Final Output

• Merge all provider results into single CSV
• Add email_source and phone_source columns for traceability
• Flag email status (deliverable, catch_all, undeliverable)
• Write to csv/output/contacts_enriched.csv

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Provider 1: FullEnrich v2 (Email + Phone)

Endpoint: POST https://app.fullenrich.com/api/v2/contact/enrich/bulk Auth: Authorization: Bearer $FULLENRICH_API_KEY

Submit (max 100 contacts/batch)

{
  "name": "My enrichment job",
  "data": [
    {
      "firstname": "Jane",
      "lastname": "Doe",
      "domain": "example.com",
      "linkedin_url": "https://www.linkedin.com/in/janedoe",
      "enrich_fields": ["contact.emails"],
      "custom": {"row_id": "0"}
    }
  ]
}

For phone: "enrich_fields": ["contact.phones"] or both: ["contact.emails", "contact.phones"]

Poll

GET https://app.fullenrich.com/api/v2/contact/enrich/bulk/{enrichment_id}

Done when: response["status"] == "FINISHED" Speed: usually instant — often finished before first poll. Use 15s interval, 600s timeout.

Parse

for entry in result.get("data", []):
    row_id = str(entry.get("custom", {}).get("row_id", ""))
    ci = entry.get("contact_info", {})
    # Email
    best = ci.get("most_probable_work_email", {})
    email = best.get("email", "") or ""
    if not email:
        for e in ci.get("work_emails", []):
            if e.get("email"):
                email = e["email"]; break
    email_status = best.get("status", "")  # DELIVERABLE / HIGH_PROBABILITY / CATCH_ALL
    # Phone
    phone_list = ci.get("phones", [])
    phone = phone_list[0].get("number", "") if phone_list else ""

Key Notes

• Base URL is app.fullenrich.com (NOT api.fullenrich.com)
• Use v2 (not v1)
• Max 100 contacts per batch
• Submit response key is enrichment_id (UUID)
• Email: contact_info.most_probable_work_email.email
• Phone: contact_info.phones[0].number
• If credits run out mid-batch: status = "CREDITS_INSUFFICIENT", partial results returned
• Email status values: DELIVERABLE, HIGH_PROBABILITY, CATCH_ALL

Cost

~0.8 credits/contact

Docs

https://docs.fullenrich.com

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Provider 2: Pipe0 Email Waterfall

Pipe ID: people:workemail:profileurl:waterfall@1 Auth: Authorization: Bearer $PIPE0_API_KEY Use curl — Python requests blocked by Cloudflare.

Submit

{
  "config": {"environment": "production"},
  "pipes": [{"pipe_id": "people:workemail:profileurl:waterfall@1"}],
  "input": [
    {"id": "1", "profile_url": "https://linkedin.com/in/janedoe"}
  ]
}

Key Notes

• Submit response key is id (not task_id) — use this value for polling: GET /pipes/check/{id}
• email_validation_status field is also returned alongside work_email in each record
• Email quality: ~15–25% of hits may be stale (old employer email) for EU audiences. Waterfall picks up the most recent known email regardless of current role. Always post-filter: remove personal domains (gmail, yahoo, etc.) and cross-check email domain against current company. In EU SME test: 37/43 raw hits, 30/43 valid after filtering 7 stale/personal.

Poll

GET https://api.pipe0.com/v1/pipes/check/{id}

Done when: status == "completed". Use 30s interval, 600s timeout (~3 min for 43 contacts). Keep batches ≤100 contacts.

Parse

records = result.get("records", {})
for rid, rec in records.items():
    fields = rec.get("fields", {})
    email  = (fields.get("work_email", {}) or {}).get("value", "") or ""
    status = (fields.get("email_validation_status", {}) or {}).get("value", "") or ""
    # work_email.value is a plain string, NOT nested

Post-filtering (Recommended)

After enrichment, filter out stale/personal emails:

PERSONAL_DOMAINS = {'gmail.com','yahoo.com','hotmail.com','outlook.com','icloud.com'}
df['email'] = df['email'].apply(
    lambda e: "" if (e and e.split('@')[-1].lower() in PERSONAL_DOMAINS) else e
)
# Also manually flag emails where domain doesn't match current company

Waterfall Providers & Cost

Provider	Cost
HunterMail	1.00 cr
LeadMagic	2.00 cr
FindyMail	2.00 cr
Crustdata	3.50 cr
Stops at first hit. Total: 1.00–3.50 cr/contact.

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Provider 3: BetterContact (Email or Phone)

Endpoint: POST https://app.bettercontact.rocks/api/v2/async Auth: X-API-KEY: $BETTERCONTACT_API_KEY

Submit (max 100 contacts/batch)

{
  "enrich_email_address": false,
  "enrich_phone_number": true,
  "data": [
    {
      "first_name": "Jane",
      "last_name": "Doe",
      "company_domain": "example.com",
      "linkedin_url": "https://www.linkedin.com/in/janedoe",
      "custom_fields": {"row_id": "0"}
    }
  ]
}

Poll

GET https://app.bettercontact.rocks/api/v2/async/{request_id}

Done when: response["status"] == "terminated" (NOT "completed")

• Phone only: ~3–5 min/100 contacts. Use 900s timeout.
• Email only or both: 30–90 min. Use 3600s timeout.
• on hold = credits exhausted, batch paused — no cancel endpoint.

Parse

for entry in result.get("data", []):
    cf = entry.get("custom_fields", [])  # ARRAY, not dict
    row_id = next((f["value"] for f in cf if f.get("name") == "row_id"), "")
    phone = entry.get("contact_phone_number") or ""
    email = entry.get("contact_email_address") or ""
    email_status = entry.get("contact_email_address_status") or ""

Key Notes

• Base URL: app.bettercontact.rocks (not api.)
• Max 100 contacts per batch
• Submit response key: id (not task_id or request_id)
• status for completion: "terminated", not "completed"
• custom_fields sent as object {"key": "val"}, returned as array: [{"name": "key", "value": "val", "position": 0}]
• Email field: contact_email_address, status: contact_email_address_status
• Phone field: contact_phone_number
• enriched: False entries can still have email values — these are low-confidence (catch_all_not_safe). Count but flag.
• Summary total field excludes not_found — use len(data) for verification

Cost

• Email only: ~0.35 cr/contact
• Phone only: ~1–2 cr/contact
• Can spike to 7+ cr/contact for enterprise audiences (complex email infra)

Docs

https://doc.bettercontact.rocks

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Provider 4: Pipe0 LinkedIn URL (When Missing)

Pipe ID: people:profileurl:name@1

Request

{
  "config": {"environment": "production"},
  "pipes": [{"pipe_id": "people:profileurl:name@1"}],
  "input": [
    {"id": "1", "name": "John Smith", "company_name": "Example Inc", "location_hint": "United States"}
  ]
}

Cost

0.60 credits/operation

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Provider 5: Pipe0 Phone Pipes

Pipe IDs:

• people:phone:profile:waterfall@1 — input: profile_url (LinkedIn URL)
• people:phone:workemail:waterfall@1 — input: work_email

Cost

2.00–7.00 credits/operation

Known Coverage

• DACH: ~92% hit rate (n=74), April 2026 DACH e-commerce SME run — response field is mobile, not phone
• SA: 0% hit rate observed — skip for SA audiences
• Solid as both primary (when contacts come from Pipe0 search → no FE phone available) and FE-fallback (71% recovery on FE contacts missing phones, n=7). Use FE first if the contact already went through FE search/enrich (you've paid for it); use Pipe0 waterfall on the remainder.

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Output

Updated CSV at csv/output/contacts_enriched.csv with added columns:

email, email_status, email_source,
phone, phone_source

All original columns preserved.

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Troubleshooting

Pipe0: Task processing timeout

Batch too large. Keep batches ≤100 contacts. Re-poll task_id — partial results may be in records.

Pipe0: CreditBalanceInsufficient

Batch fails but credits for provider responses are consumed. Top up, save task_id, do NOT resubmit. New batch for failed records only.

BetterContact: Job never terminates

Timeout too short. Phone: 900s min. Email: 3600s min.

BetterContact: custom_fields parse error

custom_fields is an array [{"name": "row_id", "value": "0"}], not a dict.

FullEnrich: 404 on results

Wrong path. Correct: https://app.fullenrich.com/api/v2/contact/enrich/bulk/{enrichment_id}

FullEnrich: Phone field empty

Prefer contact_info.most_probable_phone.number (typically mobile) over phones[0].number (can be landline). If most_probable_phone is absent, fall back to phones[0]. If both empty, fall back to Pipe0 people:phone:profile:waterfall@1 on the LinkedIn URL.

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What's Missing (To Document)

• Pipe0 phone pipe coverage by geography (currently only DACH/SA tested)
• FullEnrich v2 phone hit rates by geography
• BetterContact email-only production hit rates outside SA