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Playbook

Use CrustData for structured discovery and enrichment with recall-first filtering.
  • Start with free autocomplete (companydb_autocomplete, persondb_autocomplete) to discover canonical values.
  • Default to fuzzy contains operator (.); use strict [.] only when explicitly requested.
  • Use crustdata_companydb_search as the resolver step; for crustdata_enrich_company, prefer domain-based inputs instead of name-only payloads.
  • For job data, use crustdata_v2_job_search for indexed listings or crustdata_v2_live_job_search when freshness is required for a known CrustData company id. Legacy job-listing compatibility actions are hidden from discovery and should not be selected for new workflows.
  • In changed-company email recovery, use Crust as the second step after LeadMagic and before PDL.
  • Keep filters composable and inspect a small sample before adding expensive enrichments.
  • If Crust misses in the first 10 rows for a batch, move it later for the rest of that batch.

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Filter format

filters accepts an array of condition objects (AND-combined automatically). Each condition: {"filter_type":"<field>","type":"<operator>","value":"<val>"} or {"filter_name":"<field>","type":"<operator>","value":"<val>"}. filter_name is syntactic sugar for filter_type; human-friendly aliases (e.g. company_investors → crunchbase_investors, company_funding_stage → last_funding_round_type) are auto-mapped. A single condition object (not in array) also works. Company filter_type values: company_name, company_website_domain, linkedin_industries, hq_country, hq_location, region, year_founded, employee_metrics.latest_count, employee_count_range, employee_metrics.growth_6m_percent, employee_metrics.growth_12m_percent, employee_metrics.growth_12m, follower_metrics.latest_count, follower_metrics.growth_6m_percent, crunchbase_investors, tracxn_investors, crunchbase_categories, crunchbase_total_investment_usd, last_funding_date, last_funding_round_type, estimated_revenue_lower_bound_usd, estimated_revenue_higher_bound_usd, linkedin_id, linkedin_profile_url, company_type, acquisition_status, ipo_date, largest_headcount_country, markets, competitor_ids, competitor_websites. Person filter_type values: current_employers.company_website_domain, current_employers.title, current_employers.seniority_level, headline, region, num_of_connections, years_of_experience_raw. Operators: (.) = fuzzy contains (default), [.] = substring, =, !=, in, not_in, >, <, =>, =<. Person search also supports geo_distance for region. Range filtering: There is NO range operator like [100..500] or between. To filter a numeric range, use TWO separate filter conditions with > and < (or => and =<): 
[
  {
    "filter_type": "employee_metrics.latest_count",
    "type": ">",
    "value": "100"
  },
  {
    "filter_type": "employee_metrics.latest_count",
    "type": "<",
    "value": "500"
  }
]
Headcount filtering: For headcount, prefer employee_count_range (string enum like "51-200", "201-500") with the in operator when exact buckets work. Use employee_metrics.latest_count with > / < only when you need precise numeric boundaries. Note: employee_metrics.latest_count is valid for sorts and filters, but employee_count_range uses string enum values.

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Examples

deepline tools execute crustdata_companydb_autocomplete --payload '{"field":"linkedin_industries","query":"software","limit":5}'

deepline tools execute crustdata_companydb_search --payload '{"filters":[{"filter_type":"linkedin_industries","type":"(.)","value":"software"},{"filter_type":"hq_country","type":"=","value":"USA"}],"limit":5}'

deepline enrich --input accounts.csv --output accounts.csv.out.csv --with '{"alias":"company_lookup","tool":"crustdata_companydb_autocomplete","payload":{"field":"company_name","query":"{{Company}}","limit":1}}'