B2B Stakeholder Mapper — Buying Committee & Contacts

Stop selling to a single inbox. A B2B deal is won by a committee, not a contact. Hand this Actor a list of target company domains and one line about what you sell, and for each account it maps the buying committee: which job roles to target, who champions you, who signs the cheque, who blocks you — the order to reach them in, an opening angle for each, plus any people named on the company's own site and the likely corporate email pattern.

You bring the account list. It hands back a multi-threading playbook per company.

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The flow

1. Domains + what you sell       2. Read the site + enrich          3. Get the committee map
   stripe.com, notion.so     →      homepage · about · team    →      roles · personas · order ·
   "We sell SOC2 automation"        + firmographics (size)            angles · contacts · pattern

1. Give target domains and your offering. The committee is always mapped relative to what you sell — a security platform and a payroll tool pull a completely different set of roles out of the same company.
2. The Actor reads each company's own website (homepage + about / team / leadership / people / contact) and optionally enriches firmographics (industry + size) so seniority is calibrated — a 5,000-person company buys through VPs and Directors; a 20-person startup buys through its founders.
3. AI builds the buying committee for that specific purchase and returns it ready to paste into your CRM or sequencer.

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What you get per company

For every company, the Actor:

1. Crawls the homepage + up to 7 relevant subpages (about, about-us, team, leadership, company, people, contact) with a fast HTTP client.
2. Extracts role-based contacts published on the site (info@, sales@, hello@, …) plus links to LinkedIn / X / Facebook / Instagram / YouTube.
3. Finds on-site people — any names and titles printed on team/leadership pages (onSitePeople), confirmed by the AI from the page text. Empty when the site names nobody.
4. Infers the corporate email pattern from a single real personal address on the site: jane.doe@company → {first}.{last}@company; jdoe@company → {f}{last}@company. The evidence email is recorded, and the pattern is null when only generic inboxes exist.

Then the AI layer (OpenAI gpt-4o-mini, a fast and low-cost model) returns:

• buyingCommittee — an array of { role, seniority, personaType, whyRelevant, whatTheyCareAbout, likelyObjection }. seniority is one of C-level · VP · Director · Manager · IC; personaType is one of champion · economic buyer · technical buyer · end user · blocker.
• recommendedOrder — the roles in the sequence you should actually reach out in (usually champion / technical buyer first, economic buyer later).
• openingAngleByRole — a concrete opening angle tailored to each role.

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Three ways people use it

• Account mapping — turn a flat list of target domains into a structured map of who to contact at each company, with the firmographics (industry, size) that justify the seniority.
• Buying committee / multi-threading — stop single-threading a deal: get every persona in the committee (champion, economic buyer, technical buyer, end user, blocker), what each one cares about, and their likely objection, so you can multi-thread with intent.
• Outbound targeting & email pattern discovery — know the exact roles to prospect, the order to hit them, an opening line per role, the role-based inboxes already on the site, and the inferred email pattern so you can construct addresses for the people you target.

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Input

Field	Type	Notes
domains	array	Required. Company domains, website URLs or emails (the domain is extracted). One per line.
offering	string	Required. What you sell. The committee is mapped relative to this.
maxResults	integer	Cap how many companies to map. Default 50. One charge per company mapped.
maxConcurrency	integer	Companies processed in parallel. Default 5.
model	string	Override the OpenAI model. Default gpt-4o-mini.
useEnrichment	boolean	Default true. Call AI Company Enrichment first for industry + size, which calibrate seniority. Turn off for cheaper, less precise mapping.

Minimal input example

{
  "domains": ["stripe.com", "notion.so"],
  "offering": "We sell a SOC2 compliance automation platform for SaaS companies."
}

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Output example

{
  "domain": "stripe.com",
  "companyName": "Stripe",
  "industry": "Financial technology / payments infrastructure",
  "sizeSignal": "5000+ employees",
  "buyingCommittee": [
    {
      "role": "Chief Information Security Officer (CISO)",
      "seniority": "C-level",
      "personaType": "economic buyer",
      "whyRelevant": "Owns the security and compliance posture that a SOC2 automation platform directly improves.",
      "whatTheyCareAbout": "Reducing audit toil and continuous compliance across a large engineering org.",
      "likelyObjection": "We already have mature internal tooling and a dedicated GRC team."
    },
    {
      "role": "Head of Security / GRC",
      "seniority": "Director",
      "personaType": "champion",
      "whyRelevant": "Runs the day-to-day audit evidence collection your platform automates.",
      "whatTheyCareAbout": "Cutting manual evidence gathering before the next SOC2 cycle.",
      "likelyObjection": "Switching costs and integration effort with existing controls."
    }
  ],
  "recommendedOrder": [
    "Head of Security / GRC",
    "Chief Information Security Officer (CISO)",
    "VP of Engineering"
  ],
  "openingAngleByRole": [
    { "role": "Head of Security / GRC", "angle": "Lead with hours saved on evidence collection before the next audit window." }
  ],
  "onSitePeople": [],
  "roleBasedEmails": ["press@stripe.com"],
  "emailPattern": "{first}@stripe.com",
  "emailPatternEvidence": "patrick@stripe.com",
  "socials": {
    "linkedin": "https://linkedin.com/company/stripe",
    "twitter": "https://twitter.com/stripe"
  }
}

onSitePeople is [] when the site names no individuals; emailPattern is null when only generic inboxes are found.

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Pay-per-event pricing

This Actor uses pay-per-event billing with a single event:

Event	Price	Charged when
account-mapped	$0.15	Once per company mapped into a buying committee with target roles, outreach order, on-site contacts and the inferred email pattern.

The Actor logs its OpenAI token usage and the estimated AI cost at the end of every run, so the event price always stays safely above the model cost.

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Setup

The AI layer calls the OpenAI API. Set the environment variable / secret OPENAI_API_KEY on the Actor. Without it the run fails fast — the committee mapping is the whole product.

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No LinkedIn / a note on privacy

This Actor does not scrape LinkedIn or any people database — those sources are blocked and legally risky. It reads only the company's own public website and reasons about roles with AI. It will surface a named individual only when that person is already published on the company's own team/leadership page, and it never invents specific people. The email pattern is inferred from one real address on the site; it is a format, not a scraped list of personal addresses.

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Use it from an AI agent (Apify MCP server)

This Actor is available through the Apify MCP server, so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it domains and an offering, and read back the buying-committee map — with no custom integration code.

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Run it locally

npm install
# put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...
apify run -p

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Related actors

• B2B Account Brief — Turn a domain into a sales-ready account brief: what they do, signals and angles
• AI Cold Email Personalizer — Turn a list of domains into personalized cold-email openers from each company's site
• B2B Intent Radar — Spot companies showing buying signals so you reach out at the right moment
• Email Finder — Find email addresses for any domain — crawls the homepage plus contact/about/team pages

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Part of the No Niner toolkit — no-code web scraping, enrichment & AI data tools, all pay-per-use. Browse the full set at noniner.io.