gooseworks-ai/orthogonal-gtm-enrichment-deep

GTM Enrichment — Deep (Sixtyfour AI Agent)

Setup

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

All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"

Enrich a lead from an email address (+ optional name) using Sixtyfour's AI agents as the primary enrichment source. Returns person data, company data, funding history, and AI/B2B classification.

Cost: ~$0.20-$0.22 per lead Latency: ~30-60s (Sixtyfour AI agents browse the web)

Input

Required:

• email — the lead's email address (e.g., [email protected])

Optional:

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

Workflow

Step 1: Extract Domain

Extract the domain from the email address. Example: [email protected] -> domain: acme.com

Step 2: Run Sixtyfour Enrichment (parallel)

Fire both calls simultaneously. These are the primary data sources.

Enrich Lead ($0.10):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/enrich-lead"}'
  "lead_info": {
    "email": "{email}",
    "first_name": "{first_name_if_known}",
    "last_name": "{last_name_if_known}",
    "company": "{company_name_if_known}",
    "domain": "{domain}"
  },
  "struct": {
    "full_name": "Full legal name of this person",
    "first_name": "First name",
    "last_name": "Last name",
    "title": "Current job title at their company",
    "linkedin_url": "LinkedIn profile URL (full URL starting with https://linkedin.com/in/)",
    "city": "City where the person is located",
    "state": "State or region where the person is located",
    "country": "Country where the person is located"
  }
}'

Enrich Company ($0.10):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/enrich-company"}'
  "target_company": {
    "domain": "{domain}"
  },
  "struct": {
    "company_name": "Official company name",
    "description": "One-paragraph description of what the company does",
    "linkedin_url": "LinkedIn company page URL (full URL starting with https://linkedin.com/company/)",
    "hq_city": "Headquarters city",
    "hq_state": "Headquarters state or region",
    "hq_country": "Headquarters country",
    "employee_count": "Approximate number of employees (number only)",
    "founded_year": "Year the company was founded (number only)",
    "total_funding_amount_usd": "Total funding raised in USD (number only, no $ sign)",
    "latest_funding_date": "Date of most recent funding round (YYYY-MM-DD format)",
    "latest_funding_stage": "Stage of most recent funding round (e.g., Series A, Series B, Seed)",
    "latest_funding_amount_usd": "Amount raised in most recent round in USD (number only)",
    "is_ai_company": "true or false - does this company build or primarily use AI/ML technology?",
    "ai_evidence": "Brief explanation of why this is or is not an AI company",
    "is_b2b_saas": "true or false - is this a B2B SaaS company?",
    "b2b_evidence": "Brief explanation of why this is or is not B2B SaaS"
  }
}'

Record the status, latency, and any errors for both calls.

Step 3: Fallback — Apollo Person Match (conditional)

ONLY run if Sixtyfour /enrich-lead did NOT return a LinkedIn URL.

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
}'

Cost: $0.01. Extract linkedin_url, and also grab name, title, organization as cross-reference data.

Step 4: Fallback — Apollo Organization Enrich (conditional)

ONLY run if Sixtyfour /enrich-company did NOT return funding data (total_funding_amount_usd is null/empty).

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/organizations/enrich","query":{"domain":"{domain}"}}'

Cost: $0.01. Extract funding events, total funding, latest funding stage, and latest funding amount.

Step 5: Compile Results

Merge all data into the output format below. Apply these rules:

1. Sixtyfour is primary — use its data first for all fields
2. Apollo is fallback — only used to fill gaps Sixtyfour missed
3. Source tracking — for each field, note whether it came from sixtyfour or apollo
4. Confidence:
   • high — Sixtyfour returned the field directly
   • medium — Apollo fallback provided the field
   • low — field was inferred or partially matched

Output Format

Present the results as a JSON code block:

{
  "person": {
    "full_name": "string",
    "title": "string",
    "linkedin_url": "string",
    "location": {"city": "string", "state": "string", "country": "string"},
    "email_verified": "unknown",
    "confidence": "high | medium | low",
    "source": "sixtyfour | apollo"
  },
  "company": {
    "name": "string",
    "domain": "string",
    "linkedin_url": "string",
    "description": "string",
    "geo": {"city": "string", "state": "string", "country": "string"},
    "employee_count": "number | null",
    "founded_year": "number | null",
    "funding": {
      "total_amount": "number | null",
      "total_amount_printed": "string | null",
      "latest_round_date": "string | null",
      "latest_round_stage": "string | null",
      "latest_round_amount": "number | null",
      "rounds": [],
      "confidence": "high | medium | low"
    },
    "classification": {
      "is_ai": {"value": true, "confidence": "high | medium | low", "evidence": ["string"]},
      "is_b2b_saas": {"value": true, "confidence": "high | medium | low", "evidence": ["string"]}
    },
    "buying_signals": {
      "has_enterprise_plan": null,
      "has_self_serve": null,
      "hiring_enterprise_reps": null,
      "website_traffic_rank": null,
      "github_stars": null,
      "tech_stack": null
    },
    "confidence": "high | medium | low",
    "source": "sixtyfour | apollo | merged"
  },
  "meta": {
    "total_cost": "$0.XX",
    "api_calls": [],
    "phases_run": [1, 2],
    "enrichment_timestamp": "ISO datetime"
  }
}

Error Visibility

Track EVERY API call in the meta.api_calls array:

{
  "api": "sixtyfour",
  "endpoint": "/enrich-lead",
  "status": "success | partial | error",
  "cost": "$0.10",
  "latency_ms": 35000,
  "fields_returned": ["full_name", "title", "linkedin_url"],
  "fields_missing": ["city"],
  "error": null
}

If an API call fails, returns empty data, or times out, include it in the api_calls array with status='error' and a clear error message. Never silently skip failures.

Cost Tracking

Sum all API call costs and report in meta.total_cost:

• Sixtyfour /enrich-lead: $0.10
• Sixtyfour /enrich-company: $0.10
• Apollo /api/v1/people/match: $0.01 (only if used)
• Apollo /api/v1/organizations/enrich: $0.01 (only if used)

Example

Input: [email protected]

Expected flow:

1. Extract domain: acme.com
2. Fire Sixtyfour /enrich-lead and /enrich-company in parallel
3. Check if LinkedIn URL returned — if not, call Apollo /people/match
4. Check if funding data returned — if not, call Apollo /organizations/enrich
5. Compile and output JSON with all fields, error visibility, and cost

Tips

• Sixtyfour takes 30-60s per call — be patient, do NOT timeout early
• If Sixtyfour returns partial data, still use what it returned and fill gaps with Apollo
• AI/B2B classification comes from Sixtyfour's web research — it reads the company website
• The struct field in Sixtyfour tells the AI agent exactly what to research — modify fields there if you need different data points