lookalike-prospect

name

lookalike-prospect

description

Find companies or contacts similar to a set of references, then enrich results with verified phone numbers. Use when the user says "find companies like my best customers", "find more contacts like these", "expand from these accounts", "who else looks like [company]", "find similar companies to [list]", or any request to discover lookalike targets from a reference set. Requires at least 5 reference companies or contacts for quality results.

Lookalike Prospect

Expand an ICP from a reference set of known-good companies or contacts. Requires a minimum of 5 references — the lookalike model degrades significantly below this threshold.

Step 1 — Validate Input Count

Count the number of reference companies or contacts provided.

If fewer than 5 are provided, stop and explain before doing anything else:

"Lusha's lookalike model needs at least 5 reference [companies/contacts] to return quality results — fewer than that produces unreliable matches. You've provided [N]. Can you add [5−N] more?"

Do not proceed until the user has provided at least 5 references.

If 5 or more are provided, confirm the reference set with the user:

"Running lookalike search using these [N] [companies/contacts] as the reference set: [list]. Shall I proceed?"

Step 2 — Determine Mode

Based on the user's input, determine whether this is a company lookalike or contact lookalike search:

• References are companies (domains, LinkedIn company URLs, or names) → company mode
• References are people (emails, LinkedIn profile URLs, or name + company) → contact mode
• Mixed input → ask the user to clarify

A bare job title is not a valid seed — a lookalike needs concrete reference companies or people. If the user only has a persona/title in mind, route them to prospect (ICP search) or signal-prospect instead.

Step 3 — Assemble the Seed Set

The lookalike tools accept raw identifiers directly as seeds — no enrichment or Lusha-ID resolution is needed in the common case. Pass the references straight through. The seed count (5–100) is the total identifiers across the seed arrays.

Company mode — lookalike_companies.seeds accepts:

• domains (e.g. lusha.com)
• linkedinUrls (company page URLs)

If the user gave company names rather than domains, resolve each name to a domain first with companies_search (enrich: false — you only need the domain, not reveal data), since the seed schema does not accept bare names. If a name can't be resolved, flag it and proceed only if ≥5 seeds remain.

Contact mode — lookalike_contacts.seeds accepts any mix of:

• emails
• linkedinUrls (profile URLs)
• contacts — { firstName, lastName, companyDomain | companyName }
• contactIds — Lusha contact IDs, if you already have them

Pass whatever form the user provided directly. No lookup step is needed.

Step 4 — Run Lookalike Search

Company mode: Use lookalike_companies with the seed set. limit up to 100 (default 25).

Contact mode: Use lookalike_contacts with the seed set. limit up to 50 (default 25).

Pass any known customers/won accounts in exclude (same identifier shape as seeds) to keep them out of the results. Results paginate via dedupeSessionId: omit it on the first call, then pass the returned token back on follow-up calls for the same seeds to fetch more non-duplicate matches (sessions expire after 30 days).

Step 5 — Find Decision Makers (Company Mode Only)

For the lookalike companies, use prospecting_contact_search scoped to them via companyDomains or companyNames, plus the target role. If the user hasn't specified one, ask: "What title or seniority are you targeting at these companies?"

Pass a specific title directly as jobTitles (free-form); for broader targeting, resolve seniority / departments via prospecting_contact_filters first.

Step 6 — Enrich and Reveal Phones

Search results are previews carrying a canReveal[] list per contact. Use prospecting_contact_enrich with the contact ids and reveal set from canReveal[].field to reveal direct and mobile numbers — up to 50 contacts per call. Sum the canReveal[].credits and state the total before enriching large batches.

Step 7 — Present Results

Reference Set Used

List the [N] references that were used. Flag any that could not be resolved.

Lookalike Results

Company mode:

#	Company	Industry	Size	Revenue	Location	Contact Name	Title	Direct Phone	Mobile	Email

Contact mode:

#	Name	Title	Company	Industry	Direct Phone	Mobile	Email

• Phone columns always appear before email — never reversed
• Mark missing phones with —

Summary

• Lookalike [companies/contacts] found: X
• Decision makers enriched: Y
• Verified phones revealed: Z

Step 8 — Offer Next Actions

1. Narrow results — apply additional filters (industry, geography, company size) to the lookalike list
2. Cross with signals — run signal-prospect on this lookalike list to surface which ones are showing buying signals right now
3. Expand the reference set — add more references to improve match quality
4. Export — format as CSV