For this open deal, find the rest of the buying committee and add them as contacts
Expand this Attio deal with VP and Director stakeholders at the same company
Surfaces the rest of the buying committee for an open Attio deal. Starting from the deal's associated company, it searches MoltSets for people in the target titles and seniority levels, dedupes them against the people already linked to the deal, fills email gaps, creates the new person records in Attio, and links each one to the deal's people reference attribute. It finds the stakeholders your reps did not know to look for.
Attio open deal record
to attio/get-record (deal + associated company domain, read directly off the deal's reference attributes)
to MoltSets:
search_people (company_domain + target titles / seniority / department)
to dedupe against existing deal-linked people + suppression rules
to linkedin_to_best_email (fill email gaps where search_people has none)
to attio/create-record (create new person records)
to attio/update-record (add new people to the deal's people reference attribute)
search_people filtered on the company domain plus your target titles (via query), seniority, and department filtersbusiness_email returned by search_people where present, and runs linkedin_to_best_email to fill any gaps| Name | Title | Seniority | Company | Risk | New/Existing | Linked to Deal |
|---|
Deal company: X
Existing deal people: X
Candidates found: X
New records added: X
Emails found: X/N (XX%)
MoltSets tokens used: X
Email risk scores:
A: X B: X C: X D: X F: X
kept (accepted grades): X
filtered (excluded): X
search_people filters on query, company, company_domain, country, state, city, seniority, department or functional_area, industry, linkedin_industry, naics_code, employee_range, and revenue_range.
query, so a filters-only search runs ~40x cheaper and lands more precisely. A bare query is never the right shape; at minimum it gets country attached (~99% fill rate).industry buckets, LinkedIn's ~150 niche labels, or a NAICS code at any depth. Shortening the code widens the net.seniority/industry/department are ~60% filled and naics_code/linkedin_industry ~50%, and every filter is exact-match, silently dropping records where the field is empty.count_only: true returns a match count without fetching data or charging tokens, and every response carries results.total to read before paginating.search_people returns a business email in many records, so check that field before spending a token on a separate email callcompany_domain for the account match rather than free-text company name, since domain is exactGet the Attio Buying Committee Expansion skill file. We'll send occasional updates - no spam.