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Enrich buying committees via API

Pass a target account domain and get back the likely buying committee — mapped by role (economic buyer, champion, influencer, technical evaluator, end user), with verified contact data for each member. Built for ABM teams running multi-threaded outreach.

POSThttps://api.cleanlist.ai/v1/enrich/buying_committee

AuthorizationBearer clnl_live_…

Content-Typeapplication/json

{
  "email": "jane@acme.com"
}
  • 1 creditA verified work email, however many providers it took to find it.
  • 11 creditsEmail and direct dial together, on the same call.
  • 25+ providersQueried in cost order. The first source that returns wins, and a miss costs nothing.
  • 98% / 85%Verified work email and direct dial, measured on a 500-lead stratified test.

One call in.13 fields back.

The payload for a single buying committee lookup, and every field the record can carry. Same shape on every plan.

Response200 OK · buying_committee
{
  "id": "ev_bc4w8h3",
  "status": "completed",
  "buying_committee": {
    "domain": "midmarket.io",
    "company_name": "MidMarket Solutions",
    "company_size": "201-500",
    "committee_size": 7,
    "members": [
      {
        "role": "economic_buyer",
        "name": "Lisa Tran",
        "title": "VP of Sales",
        "email": "lisa.tran@midmarket.io",
        "email_status": "valid",
        "seniority": "vp",
        "confidence": 0.94
      },
      {
        "role": "champion",
        "name": "Ryan O'Brien",
        "title": "Director of Sales Operations",
        "email": "ryan.obrien@midmarket.io",
        "email_status": "valid",
        "seniority": "director",
        "confidence": 0.91
      },
      {
        "role": "influencer",
        "name": "Priya Sharma",
        "title": "Head of Revenue Operations",
        "email": "priya.sharma@midmarket.io",
        "email_status": "valid",
        "seniority": "director",
        "confidence": 0.88
      },
      {
        "role": "technical_evaluator",
        "name": "Jake Morrison",
        "title": "Salesforce Administrator",
        "email": "jake.morrison@midmarket.io",
        "email_status": "valid",
        "seniority": "individual_contributor",
        "confidence": 0.85
      },
      {
        "role": "end_user",
        "name": "Aisha Patel",
        "title": "Senior SDR",
        "email": "aisha.patel@midmarket.io",
        "email_status": "valid",
        "seniority": "individual_contributor",
        "confidence": 0.82
      }
    ],
    "seniority_mix": {
      "vp": 1,
      "director": 2,
      "individual_contributor": 2,
      "manager": 2
    }
  },
  "meta": {
    "credits_used": 77,
    "providers_queried": 25,
    "providers_matched": 8,
    "confidence": 0.89,
    "enriched_at": "2026-04-15T10:48:55Z"
  }
}
Field reference13 fields
  • domainstringTarget account domain used for the lookup.
  • company_namestringCompany name associated with the domain.
  • company_sizestringEmployee count range — affects expected committee size.
  • committee_sizeintegerNumber of committee members identified.
  • membersarrayArray of committee members with role, contact data, and confidence.
  • members[].roleenumBuying role: economic_buyer, champion, influencer, technical_evaluator, end_user, blocker.
  • members[].namestringFull name of the committee member.
  • members[].titlestringCurrent job title, normalized.
  • members[].emailstringVerified work email address.
  • members[].email_statusenumEmail verification result: valid, invalid, risky, catch-all.
  • members[].seniorityenumSeniority level classification.
  • members[].confidencefloat0.0 to 1.0 confidence that this person is part of the buying committee for your product category.
  • seniority_mixobjectBreakdown of committee members by seniority level.

Call it from anything.Pay for what comes back.

One POST from curl, Node or Python. Credits are drawn from one wallet the whole team shares, and a call that finds nothing is never charged.

Client librariesenrich.buying_committee
curl
curl -X POST https://api.cleanlist.ai/v1/enrich/buying_committee \
  -H "Authorization: Bearer $CLEANLIST_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"email": "jane@acme.com"}'
Node.js
import Cleanlist from "cleanlist";

const client = new Cleanlist(process.env.CLEANLIST_API_KEY);

const result = await client.enrich.buying_committee({
  email: "jane@acme.com",
});
console.log(result.buying_committee.title);
Python
from cleanlist import Cleanlist

client = Cleanlist(api_key=os.environ["CLEANLIST_API_KEY"])

result = client.enrich.buying_committee(email="jane@acme.com")
print(result["buying_committee"]["title"])
What a call costsper buying_committee
  • SearchFree, unlimited
  • Verified work email1 credit
  • Direct dial10 credits
  • Email and direct dial11 credits
  • Validation0.5 credits
  • AI qualification5 credits
  • A call that finds nothing0

One shared wallet across the team. The Free plan runs on 30 credits a month.

See all pricing

What teams do with it, and what they ask first.

  • ABM multi-threaded outreach

    Map the full buying committee at each target account, then run personalized sequences to every stakeholder simultaneously. Economic buyers get ROI messaging, champions get product value, technical evaluators get integration details.

  • Deal acceleration

    When a deal stalls at a single contact, use buying committee mapping to identify and engage other stakeholders. Multi-threading increases win rates by 30-50% compared to single-thread selling.

  • Expansion selling

    Map the buying committee in adjacent departments at existing customer accounts. Identify new champions for cross-sell opportunities without starting from scratch on account research.

  • Competitive deal strategy

    When you learn a target account is evaluating a competitor, map the full committee and engage stakeholders who may not be involved in the competitor evaluation yet.

How does Cleanlist identify buying committee members?

Cleanlist analyzes the target company's org structure using LinkedIn data, corporate profiles, and historical deal patterns. It identifies contacts whose titles and departments match typical buying committee roles for your product category. You can configure your product category (e.g., 'sales engagement software') to improve role mapping accuracy.

How many committee members are typically returned?

Committee size depends on company size. Companies with 50-200 employees typically return 3-5 members. Mid-market companies (200-1000) return 5-8 members. Enterprise companies (1000+) can return 8-15 members. The endpoint returns all identified members — you can filter by confidence score to focus on the most likely participants.

What does the confidence score represent for committee members?

The confidence score reflects how likely each person is to be involved in a purchase decision for your product category. Scores above 0.9 indicate a strong match (title and department clearly align with buying authority). Scores between 0.7 and 0.9 indicate a probable match. Scores below 0.7 are included for completeness but may be adjacent stakeholders rather than core committee members.

How are credits calculated for buying committee lookups?

Buying committee enrichment charges 11 credits per committee member returned (since each member includes verified contact data with phone numbers). A typical 7-person committee costs 77 credits. Unmatched lookups where no committee can be identified are not charged. You can set a max_members parameter to control costs.

Enrich buying committees in production.

The public API is on Pro and Scale. Search costs nothing on every plan, and a call that returns nothing is never charged.

Start free trialRead the API reference

Related reading: data enrichment and waterfall enrichment. Plans and credit rates are on pricing.