Entity Company Search

entity-company-search

Direct lookup of the company entity (table ONFIRE.COMPANIES) via ask_onfire. The canonical LinkedIn company entity — firmographics, technologies, funding, social channels, and enriched metadata in one place.

ask_onfire does not take SQL. It takes a structured QueryIR object — {entity, select, filters, insight_filters, joins, order_by, distinct_by, limit} — that the server compiles to SQL against the semantic model. You author logical field names (lowercase, from the schema below), never raw columns or FROM/WHERE.

Billing & limit (read first). ask_onfire bills 1 credit per row returned. Always set a small explicit limit for exactly what the user asked for. If you leave limit unset (or set it large), the call is bounced with needs_confirmation (stage: "row_budget") — nothing is billed and no rows come back — it just reports how many rows match so you can pick a budget and resubmit. Do not reflexively set confirmed: true to push a large pull through; lower the limit instead.

When to use this

• "Find cybersecurity companies in Germany with 200-500 employees"
• "Look up Northwind's company record — give me their LinkedIn, size, industry"
• "Which of these companies are B2B? Filter by is_b2b = true"
• "Get the GitHub URL for Artifex"
• "Find publicly traded companies in a sector"
• Any company lookup expressible as structured filters on company firmographics

Skip this for:

• Searching companies by the people they employ → use entity-people-search
• Scoring companies for propensity → use ai_prospecting
• Monthly headcount growth trends → use company-growth-trends

Logical fields (from the company entity schema)

Author the QueryIR with these logical names. Call describe_onfire_schema(["company"]) to confirm if unsure — never guess a field.

Filterable dimensions (use in filters with op eq | in | gte | lte | contains):

Returnable attributes (use in select, not filterable as dimensions): name, website, domain, description, enriched_summary, followers, github_url, company_ticker, stock_exchange, funding_last_round_type, funding_last_round_date.

Measure: company_count (= COUNT(DISTINCT linkedin_url)) — select alone for a count.

linkedin_url is the primary key — keep it in select. The soft-delete / "live" filter (DELETED_AT IS NULL) is applied server-side; you no longer author it.

QueryIR templates

Lookup by domain (or LinkedIn URL)

ask_onfire(query={
  entity: "company",
  select: ["linkedin_url", "name", "website", "size_band", "industry",
           "employee_count", "location_country", "company_type",
           "funding_last_round_type", "funding_last_round_date"],
  filters: [{dimension: "domain", op: "eq", value: "northwind.com"}],
  limit: 1
})

Swap the filter for {dimension: "linkedin_url", op: "eq", value: "<url>"} to look up by LinkedIn URL.

By industry + size + country

ask_onfire(query={
  entity: "company",
  select: ["linkedin_url", "name", "website", "size_band", "employee_count",
           "location_country", "is_b2b"],
  filters: [
    {dimension: "industry", op: "contains", value: "cybersecurity"},
    {dimension: "size_band", op: "in", value: ["201-500", "501-1000"]},
    {dimension: "location_country", op: "eq", value: "germany"}
  ],
  order_by: [{field: "employee_count", direction: "desc"}],
  limit: 100
})

industry and location_country are stored lowercase — match a lowercase literal ("germany", not "Germany"), and use contains for fuzzy industry matches.

By technology adoption

The native technologies dimension is a self-declared array and cannot be filtered with a scalar op. For adoption depth, use insight_filters with kind: "technology" — these are Onfire-derived insight concepts resolved server-side, with min_count = minimum active contacts carrying the technology:

ask_onfire(query={
  entity: "company",
  select: ["linkedin_url", "name", "website", "size_band", "industry",
           "location_country"],
  insight_filters: [{kind: "technology", value: "Kubernetes", min_count: 1}],
  filters: [{dimension: "is_b2b", op: "eq", value: true}],
  limit: 100
})

Technology/persona values are concepts, not literals — pass the human term and the server canonicalises it, or confirm the exact value with resolve_insights (carry kind) first. Multiple insight_filters AND together — run one query per concept and merge if you need an OR.

Publicly traded companies in a sector

ask_onfire(query={
  entity: "company",
  select: ["linkedin_url", "name", "website", "size_band",
           "company_ticker", "stock_exchange", "location_country"],
  filters: [
    {dimension: "company_type", op: "eq", value: "Public"},
    {dimension: "industry", op: "contains", value: "security"}
  ],
  limit: 50
})

NOTE — funding filtering is not expressible: funding_last_round_type / funding_last_round_date are returnable attributes only, not filterable dimensions, so you cannot filter "raised a Series B since 2025-01-01" in the QueryIR. You can select them to read each match's last round, but the round-type / date-range filter itself must be applied after the fact (e.g. on the downloaded dataset) — flag this to the user rather than dropping it silently.

Bulk lookup by domain list (e.g. from CRM)

ask_onfire(query={
  entity: "company",
  select: ["linkedin_url", "name", "domain", "size_band", "industry",
           "employee_count", "is_b2b", "company_type"],
  filters: [{dimension: "domain", op: "in",
             value: ["northwind.com", "sendline.com", "pathwatch.com", "frostbyte.com"]}],
  limit: 50
})

Get social / community URLs for a company

ask_onfire(query={
  entity: "company",
  select: ["name", "linkedin_url", "github_url", "website"],
  filters: [{dimension: "domain", op: "eq", value: "artifex.com"}],
  limit: 1
})

NOTE — only github_url is exposed on the company entity. Discord / Reddit / other community URLs are not selectable fields here; if the user needs those, say so rather than implying they were returned.

Output handling

ask_onfire returns a dataset handle plus preview_rows (first 20).

1. Show preview_rows to the user as a table.
2. State total_count; if > 20, mention the full CSV is available.
3. Offer download_dataset(dataset_id="...") for the full result.
4. For follow-up filters ("now filter to B2B only", "add GitHub URLs"), use query_datasets against the dataset_id — do not re-run ask_onfire (a re-run re-bills per row).

Common pitfalls

• Authoring SQL or a DELETED_AT filter — ask_onfire takes a QueryIR, not SQL, and the live/soft-delete filter is applied server-side.
• Leaving limit unset / too large — bounced for confirmation (nothing billed); set the exact row budget you need.
• Using size_band as a number — it's a TEXT bucket ("201-500"). Use in.
• Filtering on technologies directly — it's a self-declared array; use a technology insight_filter (with min_count) for adoption.
• Filtering funding — funding_* are returnable attributes only, not filterable dimensions (see the publicly-traded template note).
• Case on free text — industry / location_country / enriched_category are stored lowercase; match lowercase literals.
• domain vs website — domain is the bare domain ("northwind.com"); prefer it for exact eq / in matching.
• Re-running for "now add the GitHub URLs" — enrich with query_datasets on the existing dataset_id instead of re-billing a fresh ask_onfire.