/gtm-enrichment-smart

name: gtm-enrichment-smart
description: Multi-provider waterfall lead enrichment. Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback. Cost-efficient (~$0.04-$0.10/lead) with confidence scoring and full error visibility.
source: orthogonal

GTM Enrichment — Smart (Multi-Provider Waterfall)

Setup

Choose the available runtime before doing any credential setup:

• **Terminal-free client:** skip the shell commands below. Use connected MCP tools. For ScrapeCreators operations, read `scrapecreators-api` and prefer `call_data_provider`. If a required enrichment provider has no connected tool, report that part of the waterfall as unavailable rather than fabricating enrichment data.
• **Local terminal:** use the GooseWorks credentials and proxy commands below.

Read your credentials from ~/.gooseworks/credentials.json:

export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: `npx gooseworks login`

The local proxy endpoints use Bearer auth: `-H "Authorization: Bearer $GOOSEWORKS_API_KEY"`. ScrapeCreators operation descriptions below remain environment-neutral in both runtimes.

Enrich a lead from an email address (+ optional name) using a waterfall strategy: start with cheap APIs ($0.01 each), cross-reference for confidence, then use expensive AI agents only for gaps. Spends proportionally to lead quality.

**Cost**: $0.04 (best) to ~$0.12 (typical with buying signals) to ~$0.26 (worst, Sixtyfour fallback) **Latency**: ~5-15s typical, up to 60s if Sixtyfour fallback triggers

Input

Required:

• **email** — the lead's email address (e.g., `jane@acme.com`)

Optional:

• **name** — full name if known (improves match rate)

Workflow

Step 0: Extract Domain + Free Email Check

Extract the domain from the email. Check if it's a free email provider.

**Free email providers** (skip Brand.dev if match): `gmail.com`, `yahoo.com`, `hotmail.com`, `outlook.com`, `aol.com`, `icloud.com`, `mail.com`, `protonmail.com`, `zoho.com`, `yandex.com`, `gmx.com`, `live.com`

Set `is_free_email = true/false` — this gates whether Brand.dev runs in Phase 1.

---

PHASE 1 — Core (always run, parallel) — ~$0.03-$0.06

Run ALL of these simultaneously:

**1a. Apollo People Match** ($0.01):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"apollo","path":"/api/v1/people/match"}'
  "email": "{email}",
  "reveal_personal_emails": true
}'

Extract: `person.name`, `person.title`, `person.linkedin_url`, `person.city`, `person.state`, `person.country`, `person.organization.name`, `person.organization.id` (save org_id for Phase 4), `person.organization.industry`, `person.organization.estimated_num_employees`, `person.organization.keywords`, `person.organization.funding_events`, `person.organization.total_funding`.

**1b. Hunter Combined Enrichment** ($0.01):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"hunter","path":"/v2/combined/find","query":{"email":"{email}"}}'

Extract: `data.person.first_name`, `data.person.last_name`, `data.person.linkedin_handle`, `data.person.title`, `data.company.name`, `data.company.domain`, `data.company.industry`, `data.company.description`, `data.company.headcount`, `data.company.technologies`, `data.company.twitter`, `data.company.category`.

**1c. Brand.dev Retrieve** ($0.03 — CONDITIONAL: only if `is_free_email == false`):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"brand-dev","path":"/v1/brand/retrieve","query":{"domain":"{domain}"}}'

Extract: `title` (company name), `description`, `industries` (including `eic` code), `socials` (twitter URL, github URL, linkedin URL), `employeeCount`, `foundedYear`, `location`.

**SKIP this call if `is_free_email == true`** — saves $0.03.

**1d. Hunter Email Verifier** ($0.01):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"hunter","path":"/v2/email-verifier","query":{"email":"{email}"}}'

Extract: `data.status` (valid/invalid/accept_all/webmail/disposable/unknown), `data.result` (deliverable/undeliverable/risky).

---

PHASE 1 MERGE — Cross-Reference & Confidence

After all Phase 1 calls complete, merge data:

**Person merge rules:** 1. Full name: prefer Apollo (structured), cross-ref with Hunter 2. Title: prefer Apollo, cross-ref with Hunter 3. LinkedIn URL: prefer Apollo `linkedin_url`, fallback to Hunter `linkedin_handle` (prepend `https://linkedin.com/in/`) 4. Location: prefer Apollo (structured city/state/country) 5. If Apollo and Hunter **agree** on name+title: `confidence = "high"` 6. If only one source has data: `confidence = "medium"` 7. If they **disagree** on name or title: flag conflict, keep both, `confidence = "low"`

**Company merge rules:** 1. Name: prefer Apollo org name, cross-ref with Hunter + Brand.dev 2. LinkedIn URL: prefer Brand.dev socials, fallback Apollo 3. Description: prefer Brand.dev (richer), fallback Hunter 4. Employee count: prefer Apollo, cross-ref with Brand.dev + Hunter headcount 5. Funding: use Apollo `funding_events` and `total_funding` 6. Geo: prefer Apollo org location, cross-ref with Brand.dev 7. Tech stack: use Hunter `technologies` 8. Social URLs: use Brand.d