orthogonal-sh/gtm-enrichment-smart

GTM Enrichment — Smart (Multi-Provider Waterfall)

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., [email protected])

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):

orth run apollo /api/v1/people/match --body '{
  "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):

orth run hunter /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):

orth run brand-dev /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):

orth run hunter /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.dev socials (twitter, github)

AI/B2B Classification (zero extra cost):

Cross-reference three sources from Phase 1:

| Source | AI Signals | B2B Signals | |--------|-----------|-------------| | Brand.dev description + industries.eic | Parse description for: AI, ML, machine learning, deep learning, neural, LLM, GPT, NLP, computer vision | Parse for: SaaS, B2B, enterprise, platform, API, developer tools, infrastructure | | Apollo keywords[] + industry | Match keywords against AI terms | Match keywords against B2B terms | | Hunter category + company description | Check for AI/ML terms | Check for software/SaaS/B2B terms |

Confidence rules:

• high: 2+ sources agree
• medium: 1 source has signal
• low: weak inference only (e.g., "tech company" but no explicit AI/B2B terms)

---

PHASE 2 — Gap-Fill (conditional) — $0.00-$0.02

2a. Apollo Organization Enrich ($0.01 — ONLY if Apollo Phase 1 returned NO funding_events or funding data is empty):

orth run apollo /api/v1/organizations/enrich -q 'domain={domain}'

Extract: organization.funding_events[], organization.total_funding, organization.latest_funding_stage, organization.latest_funding_amount, organization.estimated_num_employees, organization.annual_revenue.

2b. Tomba Enrich ($0.01 — ONLY if Apollo and Hunter disagree on person name OR title):

orth run tomba /v1/enrich --query 'email={email}'

Use as tie-breaker. If Tomba agrees with Apollo: use Apollo data. If Tomba agrees with Hunter: use Hunter data. If all three disagree: keep Apollo as primary, flag conflict.

---

PHASE 3 — Sixtyfour Fallback (conditional, expensive) — $0.00-$0.20

3a. Sixtyfour Enrich Lead ($0.10 — ONLY if person NOT found after Phases 1-2, meaning no name AND no title AND no LinkedIn URL from any source):

orth run sixtyfour /enrich-lead --body '{
  "lead_info": {
    "email": "{email}",
    "domain": "{domain}"
  },
  "struct": {
    "full_name": "Full legal name of this person",
    "title": "Current job title",
    "linkedin_url": "LinkedIn profile URL (full URL)",
    "city": "City",
    "state": "State or region",
    "country": "Country"
  }
}'

3b. Sixtyfour Enrich Company ($0.10 — ONLY if company has major gaps AND org has >500 employees):

Major gaps = missing 2+ of: LinkedIn URL, description, employee count, funding data.

orth run sixtyfour /enrich-company --body '{
  "target_company": {
    "domain": "{domain}"
  },
  "struct": {
    "company_name": "Official company name",
    "description": "One-paragraph description",
    "linkedin_url": "LinkedIn company page URL",
    "employee_count": "Number of employees",
    "total_funding_usd": "Total funding raised in USD",
    "latest_funding_date": "Most recent funding round date",
    "latest_funding_stage": "Most recent round stage",
    "latest_funding_amount_usd": "Most recent round amount"
  }
}'

---

PHASE 4 — Buying Signals (qualified leads only) — $0.00-$0.04

Gate: Only run Phase 4 if the company is:

• Funded (total_funding > 0) AND
• Classified as B2B (is_b2b_saas = true) AND
• Has >50 employees

4a. Brand.dev AI Products ($0.03 — extracts products, pricing tiers, and features from the website):

orth run brand-dev /v1/brand/ai/products --body '{
  "domain": "{domain}"
}'

From the products response, extract buying signals:

• has_enterprise_plan: Check if any product has "enterprise" in name, tier, or target_audience
• has_self_serve: Check if any product has a listed price (self-serve) vs "Contact sales" pricing
• target_market: Infer from target_audience arrays across products

4b. Apollo Job Postings ($0.01 — ONLY if organization_id was captured from Phase 1):

orth run apollo /api/v1/organizations/{organization_id}/job_postings -q 'organization_id={organization_id}'

Search job postings for enterprise sales signals: titles containing "Enterprise", "Account Executive", "Solutions Engineer", "Sales Director", "Customer Success". If found, set hiring_enterprise_reps = true.

---

PHASE 5 — Cheap/Free Signals — $0.00-$0.01

5a. GitHub Stars (free — ONLY if Brand.dev socials or Apollo data returned a GitHub URL):

# Extract org name from GitHub URL, e.g., https://github.com/ngrok -> ngrok
# Use the GitHub public API (no auth needed for public repos):
curl -s "https://api.github.com/orgs/{org_name}/repos?sort=stars&per_page=5" | jq '[.[] | {name: .name, stars: .stargazers_count}]'

Sum the top repo stars or report the flagship repo star count.

5b. Twitter/X Followers (Scrape Creators — ONLY if a Twitter handle was found in Brand.dev socials or Apollo data):

orth run scrapecreators /v1/twitter/profile -q 'handle={twitter_handle}'

Extract: legacy.followers_count, legacy.friends_count, legacy.statuses_count, legacy.description.

---

FINAL — Compile & Output

Merge all phase results into the output format. Track which phases ran.

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": "deliverable | undeliverable | risky | unknown",
    "confidence": "high | medium | low",
    "source": "apollo | hunter | sixtyfour | tomba | merged"
  },
  "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": [{"date": "", "type": "", "amount": 0, "investors": ""}],
      "confidence": "high | medium | low"
    },
    "classification": {
      "is_ai": {"value": true, "confidence": "high", "evidence": ["Brand.dev description mentions ML", "Apollo keywords include 'artificial intelligence'"]},
      "is_b2b_saas": {"value": true, "confidence": "high", "evidence": ["Hunter category: software", "Apollo industry: SaaS"]}
    },
    "buying_signals": {
      "has_enterprise_plan": "boolean | null",
      "has_self_serve": "boolean | null",
      "hiring_enterprise_reps": "boolean | null",
      "website_traffic_rank": "number | null",
      "github_stars": "number | null",
      "twitter_followers": "number | null",
      "tech_stack": ["array | null"]
    },
    "confidence": "high | medium | low",
    "source": "apollo | hunter | brand-dev | sixtyfour | merged"
  },
  "meta": {
    "total_cost": "$0.XX",
    "api_calls": [
      {
        "api": "apollo",
        "endpoint": "/api/v1/people/match",
        "status": "success",
        "cost": "$0.01",
        "latency_ms": 1200,
        "fields_returned": ["name", "title", "linkedin_url", "organization"],
        "fields_missing": [],
        "error": null
      }
    ],
    "phases_run": [1, 2, 4, 5],
    "enrichment_timestamp": "ISO datetime"
  }
}

Error Visibility

Track EVERY API call in the meta.api_calls array with this structure:

{
  "api": "string (apollo | hunter | brand-dev | sixtyfour | tomba | scrapecreators | github)",
  "endpoint": "string",
  "status": "success | partial | error | skipped",
  "cost": "$0.XX",
  "latency_ms": 0,
  "fields_returned": [],
  "fields_missing": [],
  "error": "string | null"
}

Rules:

• If an API call fails, returns empty data, or times out: include it with status='error' and a clear error message. Never silently skip failures.
• If an API call was skipped due to gating logic (e.g., Brand.dev skipped for free email): include it with status='skipped', cost='$0.00', and reason in error field (e.g., "Skipped: free email provider").
• If an API call returns partial data: use status='partial', list what was returned and what was missing.

Cost Tracking

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

| API | Endpoint | Cost | When | |-----|----------|------|------| | Apollo | /api/v1/people/match | $0.01 | Always (Phase 1) | | Hunter | /v2/combined/find | $0.01 | Always (Phase 1) | | Brand.dev | /v1/brand/retrieve | $0.03 | Phase 1, skip for free email | | Hunter | /v2/email-verifier | $0.01 | Always (Phase 1) | | Apollo | /api/v1/organizations/enrich | $0.01 | Phase 2, only if funding missing | | Tomba | /v1/enrich | $0.01 | Phase 2, only if person data conflicts | | Sixtyfour | /enrich-lead | $0.10 | Phase 3, only if person not found | | Sixtyfour | /enrich-company | $0.10 | Phase 3, only if major gaps + >500 employees | | Brand.dev | /v1/brand/ai/products | $0.03 | Phase 4, only if funded + B2B + >50 employees | | Apollo | /organizations/{id}/job_postings | $0.01 | Phase 4, only if org_id available | | Scrape Creators | /v1/twitter/profile | ~$0.01 | Phase 5, only if Twitter handle found | | GitHub API | public | $0.00 | Phase 5, only if GitHub URL found |

Example

Input: [email protected]

Expected flow:

1. Domain: acme.com, is_free_email = false
2. Phase 1 (parallel): Apollo people/match, Hunter combined, Brand.dev retrieve, Hunter email-verifier
3. Phase 1 merge: Cross-reference person data, classify AI/B2B from descriptions+keywords
4. Phase 2: Check if Apollo returned funding — if not, call Apollo org enrich. Check if person data conflicts — if so, call Tomba.
5. Phase 3: Skip if person found and company data sufficient
6. Phase 4: If company is funded + B2B + >50 employees, run Brand.dev AI products + Apollo job postings
7. Phase 5: If GitHub URL found, grab star counts. If Twitter handle found, grab follower count via Scrape Creators
8. Compile and output JSON

Tips

• Phase 1 calls should all fire simultaneously — they're independent
• Apollo's people/match is the single best-value call — it returns person AND embedded company data including funding events
• Brand.dev is the richest source for company description, industry classification, and social URLs — but costs 3x more than Apollo/Hunter, so skip it for free email providers
• The AI/B2B classification uses data already returned by Phase 1 — no extra API calls needed
• Hunter's technologies array is the only source of tech stack data — valuable for technical buyers
• Phase 3 (Sixtyfour) should be rare — Apollo + Hunter find most people. Only trigger for truly obscure leads
• Phase 4 buying signals are the most actionable data for GTM — but gate them to avoid wasting $0.04 on unqualified leads
• GitHub stars and Twitter followers are cheap/free social proof signals — always grab them if URLs/handles are available