n8n + Apollo + HubSpot: Lead Enrichment That Saves 6 Hours a Week

Our sales team got 312 form submissions last month. Before this workflow, an SDR spent ~6 hours/week on Apollo lookups, copy-pasting fields into HubSpot, sizing the company, and pinging Slack. We rebuilt it in n8n in two afternoons: 18 nodes, 6 hours/week back. Below is the exact workflow we run for our own sales pipeline — not a generic template, the live one.

TL;DR — What does this enrichment workflow do?

A web form submits to n8n. The workflow takes the work email, runs an Apollo people/match API call to pull the lead's title, LinkedIn, seniority, and employer; then a second Apollo organizations/enrich call gets headcount, revenue band, and tech stack. Claude Haiku scores the lead 0–100 against your ICP. n8n creates a HubSpot contact + company, attaches the score as a custom property, and posts a structured Slack ping to #leads-new. End-to-end runtime: ~7 seconds.

Why this matters now — March 2026

Apollo's per-credit pricing held flat in 2026 but the new "Person+Org enrichment in one call" endpoint (rolled out Feb 2026) lets you do both lookups in a single API hit — that is two credits down to one for the same data. HubSpot's free-tier custom property limit also moved from 10 to 25 in March 2026, which is when this workflow finally became viable on the free CRM tier.

The trigger for us: our SDR Anjali calculated she was spending 1h 12m a day on enrichment grunt work. At her fully loaded cost, that is a real slice of her year going into copy-paste. The workflow paid for itself in 9 working days.

The 5-step pipeline

Prerequisites

• n8n v1.121+ (self-hosted or Cloud — workflow uses no proprietary nodes)
• Apollo.io account with API access (Basic plan includes 600 enrichment credits)
• HubSpot account (free tier works; Pro needed for the workflow association API)
• Anthropic API key with Claude Haiku 4.5 access
• Slack workspace with bot token, scope chat:write
• A web form that can POST JSON — we use Tally, but Typeform/Webflow/native HTML all work

Step 1 — The form webhook + spam gate

The form sends a JSON payload to https://n8n.softechinfra.com/webhook/lead-in. The first n8n node is a standard Webhook in production mode. Right after, a Set + IF combination kills three classes of garbage before they cost an Apollo credit:

The gating Code node:

const FREE_DOMAINS = new Set([
    'gmail.com','yahoo.com','hotmail.com','outlook.com','live.com',
    'icloud.com','rediffmail.com','aol.com','protonmail.com'
  ]);
  const DISPOSABLE = new Set([
    'mailinator.com','tempmail.org','10minutemail.com','guerrillamail.com',
    'throwaway.email','yopmail.com'
  ]);
  const email = ($json.body.email || '').toLowerCase().trim();
  const domain = email.split('@')[1] || '';
  
  if ($json.body.website2) return { json: { route: 'honeypot' } };
  if (DISPOSABLE.has(domain)) return { json: { route: 'spam' } };
  if (FREE_DOMAINS.has(domain)) return { json: { route: 'nurture', email, domain } };
  if (!email.includes('@') || !domain.includes('.')) return { json: { route: 'invalid' } };
  
  return { json: { route: 'enrich', email, domain, ...$json.body } };

The Switch node downstream branches on route. Only route === 'enrich' triggers Apollo. This gate alone saves us ~70 Apollo credits/month.

Step 2 — Apollo people enrichment

The combined enrichment endpoint (rolled out by Apollo in Feb 2026) is POST https://api.apollo.io/v1/people/match with the reveal_personal_emails=false and reveal_organization=true flags. One credit, two enrichments.

Node JSON:

{
    "parameters": {
      "method": "POST",
      "url": "https://api.apollo.io/v1/people/match",
      "sendHeaders": true,
      "headerParameters": {
        "parameters": [
          { "name": "x-api-key", "value": "={{ $credentials.apolloApi.apiKey }}" },
          { "name": "Content-Type", "value": "application/json" },
          { "name": "Cache-Control", "value": "no-cache" }
        ]
      },
      "sendBody": true,
      "specifyBody": "json",
      "jsonBody": "={\n  \"email\": \"{{ $json.email }}\",\n  \"reveal_personal_emails\": false,\n  \"reveal_phone_number\": false,\n  \"reveal_organization\": true\n}",
      "options": {
        "timeout": 20000,
        "retry": { "enabled": true, "maxRetries": 2 }
      }
    },
    "id": "apollo-enrich-001",
    "name": "Apollo Match Person+Org",
    "type": "n8n-nodes-base.httpRequest",
    "typeVersion": 4.2,
    "position": [680, 300]
  }

Apollo's response on a hit gives you about 60 fields. The ones we keep:

A Set node trims the firehose down to the 14 fields HubSpot actually wants.

Apollo credit math, real numbers from our March 2026 usage:

If you outgrow Basic, the Professional plan bumps you to 1,500 credits. Per-credit cost stays flat.

Step 3 — Claude Haiku ICP scoring

We score every enriched lead 0–100 against our written ICP. The ICP rubric lives in a Notion doc that we paste into Claude's system prompt; we re-tune it monthly.

Claude system prompt (the one we actually run):

You are a B2B lead scorer for Softechinfra, an Indian IT services firm.
  Score this lead 0-100 against our ICP and return strict JSON only.
  
  ICP heavy positives:
  - Indian-domiciled company, headcount 20-500
  - Industries: D2C, edtech, fintech (RBI-regulated wallet/UPI), logistics, healthcare
  - Title contains: CTO, VP Engineering, Head of Product, COO, Founder
  - Tech stack includes: Node.js, Python, AWS, MongoDB
  - Revenue band: ₹5 Cr - ₹500 Cr
  
  ICP soft positives:
  - SaaS or AI-services adjacent
  - Title contains: Engineering Manager, Tech Lead
  
  ICP negatives (cap score at 30):
  - Headcount <10 or >2000
  - Pure agency / consultancy (we compete)
  - Personal Gmail-only lead
  - Crypto-only company
  
  Schema: { score: int 0-100, tier: 'A'|'B'|'C'|'D', reasoning: string max 200 chars, suggested_action: string max 100 chars }
  
  Tier mapping: A=80-100, B=60-79, C=40-59, D=0-39.

User message body, built from Apollo's response:

Lead: {{ $json.person.name }}, {{ $json.person.title }} at {{ $json.organization.name }}.
  Company: {{ $json.organization.estimated_num_employees }} employees, {{ $json.organization.annual_revenue_printed }}, industry: {{ $json.organization.industry }}.
  Tech stack: {{ $json.organization.technologies.slice(0,8).join(', ') }}.
  Submitted message: "{{ $('Webhook').first().json.body.message }}".

Input is ~500 tokens, output ~80. Across 208 scored leads the scoring cost is basically free.

Our Claude HTTP Request node uses the same structure as Post 1's, with model claude-haiku-4-5-20251115. We pin model versions explicitly — claude-haiku-latest aliases have flipped on us mid-week before.

Step 4 — HubSpot upsert + Slack ping

Two parallel branches. HubSpot first.

The HubSpot Contact node creates-or-updates by email:

{
    "parameters": {
      "operation": "upsert",
      "email": "={{ $json.email }}",
      "additionalFields": {
        "firstname": "={{ $json.person.first_name }}",
        "lastname": "={{ $json.person.last_name }}",
        "jobtitle": "={{ $json.person.title }}",
        "linkedinurl": "={{ $json.person.linkedin_url }}",
        "lifecyclestage": "lead",
        "lead_source": "Web Form - Enriched",
        "icp_score": "={{ $json.score }}",
        "icp_tier": "={{ $json.tier }}",
        "icp_reasoning": "={{ $json.reasoning }}",
        "apollo_match_at": "={{ $now.toISO() }}"
      }
    },
    "id": "hubspot-contact-001",
    "name": "Upsert HubSpot Contact",
    "type": "n8n-nodes-base.hubspot",
    "typeVersion": 2.1,
    "position": [1380, 240]
  }

We then call a HubSpot Company node with the same upsert pattern using organization.website_url as the dedup key, and finally associate the two via the HubSpot Associations API (the native node handles it in v2.1+).

Three custom HubSpot properties you have to create once: icp_score (number), icp_tier (single-line text, enum-style), apollo_match_at (datetime). The free tier allows 25 custom contact properties as of March 2026.

The Slack branch — a single Slack node posting a structured message to #leads-new:

:sparkles: *New lead, tier {{ $json.tier }} ({{ $json.score }}/100)*
  {{ $json.person.name }} — {{ $json.person.title }} at *{{ $json.organization.name }}*
  {{ $json.organization.estimated_num_employees }} ppl · {{ $json.organization.industry }} · {{ $json.organization.annual_revenue_printed }}
  > _{{ $json.reasoning }}_
  > _Action:_ {{ $json.suggested_action }}
  :hubspot: <https://app.hubspot.com/contacts/{{ $env.HUBSPOT_PORTAL_ID }}/contact/{{ $('Upsert HubSpot Contact').first().json.id }}|Open in HubSpot>

Tier A pings @sales-lead with a notification; B/C/D drop quietly into the channel.

Cost comparison vs the manual SDR workflow

For 312 leads/month (our actual volume):

Our n8n + Apollo bar breaks down as: Apollo Basic + Anthropic + Hetzner share + HubSpot free + Slack free + minor n8n storage, plus a buffer.

When NOT to do this

Three contexts where this workflow is not the right call.

You have under 40 leads/month. Apollo's per-credit waste plus the workflow ops overhead exceeds the SDR's actual time. Use a spreadsheet and 15 min/day until you cross ~100/month.

Your ICP is consumer (B2C). Apollo is a B2B database. Pulling a college student or a homemaker returns no data and burns the credit anyway. For B2C, use Clearbit Reveal on the website-visit side instead.

You sell into Tier-2/Tier-3 India only. Apollo's coverage on companies headquartered in places like Indore, Coimbatore, or Vizag is patchier — we see closer to 70% match rates vs 93% for Bangalore/Mumbai/Delhi. The workflow still works; you just need a manual-research fallback branch and a slightly bigger time saving claim ("4 hours/week" not "6").

Mini case — a Mumbai SaaS startup, ₹4 Cr ARR

Client: a 22-person product analytics SaaS firm out of Powai. They were getting ~180 leads/month from inbound + paid ads, scoring them by gut, and burning their two SDRs on triage. We ported this exact workflow with their ICP rubric (theirs caps Indonesia/Vietnam leads at score 50 — different market positioning).

After 6 weeks: SDRs reported saving 8.4 hours/week combined. SQL conversion rate on Tier A leads (their score 75+) went from 17% to 31% — not because the leads got better, but because the SDRs could now focus on the top 30% of inbound instead of every form fill. Their CEO's note to me: "First automation that actually saved my team time instead of creating a new dashboard nobody opens."

This is the same enrichment pattern we use inside our AI automation services practice — built originally to power the lead flow into TalkDrill, our in-house English fluency app for Indian adults. The graph is identical; only the ICP rubric changes per client.

Common mistakes

Mistake 1 — Enriching every form submission. The gate is not optional. Without the free-email + honeypot filter you will burn credits on garbage and exhaust Apollo Basic by the 18th of the month.

Mistake 2 — Storing Apollo data as the source of truth. Treat Apollo as a snapshot. The lead's title may have changed; the company may have raised a round. Re-enrich on a 90-day TTL via a scheduled Cron node, not on every workflow trigger.

Mistake 3 — Letting Claude pick the HubSpot lifecycle stage. It will hallucinate "MQL" vs "SQL" and your reporting goes sideways. Hard-code the lifecycle stage in n8n based on tier, not Claude's free-text guess.

Mistake 4 — Webhook auth wide open. Your /lead-in endpoint should require a secret header. If the URL leaks, every script kiddie on r/sweatshop_lead_gen will pump fake submissions and you will burn through Apollo credits in a weekend.

FAQ

How fast is the enrichment per lead?

Median 6.4 seconds, 95th percentile 11 seconds. Apollo's API responds in ~1.5s, Claude Haiku in ~2.1s, HubSpot in ~1.3s, the rest is n8n overhead. We have run 312 leads in a single day without throttling.

Can I use Hunter or Lusha instead of Apollo?

Yes. Swap the Apollo HTTP Request for Hunter's /v2/email-finder or Lusha's /v2/person endpoint. Hunter is cheaper but coverage on Indian B2B is weaker — our test showed 64% match rate vs Apollo's 93%. Lusha is closer to Apollo at 88% match.

Will this work with Pipedrive or Zoho instead of HubSpot?

Yes. n8n has native nodes for both. Pipedrive's API is cleaner for custom fields; Zoho's needs you to fetch the field IDs first via the metadata endpoint and pin them in a Set node. Workflow shape is the same.

How do I prevent duplicate HubSpot contacts?

Use upsert by email, not create. The native HubSpot node's "upsert" operation hits HubSpot's /contacts/v3/objects/contacts/upsert endpoint which deduplicates server-side. If you create-then-update via two separate operations you will race-condition under load.

What about leads from LinkedIn ads or Meta?

Both push lead data via webhook on submission. Point them at the same /lead-in endpoint with a query param like ?source=linkedin and your Set node tags the source for downstream reporting. We have wired all three (Tally + LinkedIn + Meta) to one workflow.

Does the workflow run when n8n is offline?

No — and that is a real failure mode. We use n8n Cloud's automatic restart, or for self-hosted we wrap the Docker container in a systemd service with Restart=always. We also send a daily "heartbeat" ping to Better Uptime from a Cron node, so a dead workflow is paged within 5 minutes.

Is the Apollo data accurate?

Mostly. Apollo's data freshness is ~6 months on titles and company headcount in our experience. For high-stakes leads the SDR still verifies on LinkedIn before reaching out. The point is the workflow gets you from "no data" to "good-enough data" in 7 seconds; the human still owns the final call.