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.

Start free trial Read the API reference

REST · JSON in, JSON out13 fieldsPublic API from Pro

POSTapi.cleanlist.ai/v1/enrich/buying_committee200 OK

Request

Authorization Bearer $CLEANLIST_API_KEY

Content-Type application/json

{
  "email": "jane@acme.com"
}

Waterfall

Responsebuying_committee

domain"midmarket.io"
company_name"MidMarket Solutions"
company_size"201-500"
committee_size7
members[5]
seniority_mix{…}

credits_used77

1 creditA verified work email, however many providers it took to find it.
11 creditsEmail and phone number together, on the same call.
25+ providersQueried in cost order. The waterfall stops at the first deliverable result.
98% / 85%Verified work emails and phone numbers found on our 500-lead benchmark.

10,000+ users, from founders to full sales teams

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What one call returns. Up to 13 fields.

A sample response for one buying committee lookup, beside every field the record can carry.

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 curl, Node or Python. Pay for what comes back.

It's one POST request. Credits come out of one wallet the whole team shares, and a call that finds nothing costs nothing.

RequestPOST · 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

const response = await fetch("https://api.cleanlist.ai/v1/enrich/buying_committee", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.CLEANLIST_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ email: "jane@acme.com" }),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

Python

import os
import requests

response = requests.post(
    "https://api.cleanlist.ai/v1/enrich/buying_committee",
    headers={"Authorization": f"Bearer {os.environ['CLEANLIST_API_KEY']}"},
    json={"email": "jane@acme.com"},
)
response.raise_for_status()
print(response.json())

What a call costsper buying_committee

SearchFree, unlimited
Verified work email1 credit
Phone number10 credits
Email and phone number11 credits
Validation0.5 credits
AI qualification5 credits
A call that finds nothing0

What teams do with it.

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.

The questions that come up first.

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, and 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.