Cargo

Documentation Index

Fetch the complete documentation index at: https://docs.getcargo.ai/llms.txt

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Cargo ships with a built-in Cargo data connector — a managed B2B dataset of companies and people you can use directly inside any workflow. Use it to source target lists, match inbound records against firmographic data, enrich businesses and prospects across 13+ dimensions (firmographics, technographics, financials, hiring trends, intent, and more), and listen in real time for buying signals like funding rounds, role changes, or new product launches.

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How to set up Cargo

The Cargo connector is available in every workspace by default. To use it:

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Concepts

The Cargo dataset is organised around two core entities, each identified by a stable ID returned by the matching/sourcing actions: The typical flow is:

1. Match an inbound record (domain, email, LinkedIn URL, name) → get a business_id or prospect_id.
2. Enrich that ID with one or more enrichment actions to pull in firmographics, technographics, funding, intent, etc.
3. Listen for events on the matched record to trigger Plays when something changes.

Alternatively, use Fetch actions and extractors to source new businesses or prospects from scratch based on ICP filters.

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Sourcing actions

Use these actions to discover new accounts and contacts that match your ICP.

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Fetch businesses

Search the Cargo dataset for companies that match a set of ICP filters and return up to 100 matches. Useful inside Plays or Agents that need to dynamically pull a working list. Configuration

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Fetch prospects

Search the Cargo dataset for people matching a set of ICP filters and return up to 100 prospects. Configuration

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Fetch business statistics

Return aggregate counts (total companies, by size, by industry, by tech stack, etc.) for any combination of the same firmographic filters used in Fetch businesses. Useful to size a market before sourcing.

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Matching actions

Use matching actions to resolve external records into Cargo IDs so you can chain enrichment actions.

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Match business

Find a business_id from a company name and/or domain. Configuration Returns ✅ Match with the matched business object, or ❌ No match when no record was found.

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Match prospect

Find a prospect_id from any combination of identifiers. Configuration

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Business enrichment actions

All business enrichment actions take a business_id (from Match business or Fetch businesses) and return the corresponding enrichment payload. Enrich business Bombora intent topics also accepts:

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Prospect enrichment actions

All prospect enrichment actions take a prospect_id (from Match prospect or Fetch prospects).

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Event actions

Pull historical signals on demand for a single business or prospect.

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Fetch business events

Return business events that happened on or after a given timestamp. Configuration

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Fetch prospect events

Same as above, scoped to a prospect. Configuration

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Extractors

Extractors sync data from Cargo into a Model, where it can power Plays, segments, and CRM syncs. They come in two flavours:

• Fetch extractors run paginated, on-demand pulls of records that match a filter.
• Listen extractors enroll a set of records and stream new events into the model in real time.

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Fetch businesses extractor

Bulk-pulls businesses into a Model using the same ICP filters as the Fetch businesses action, but paginates up to 60,000 records per run (page size 500). The output model is keyed on business_id and exposes the following columns: business_id, name, domain, logo, country_name, city_name, number_of_employees_range, yearly_revenue_range, website, business_description, region, naics, naics_description, sic_code, sic_code_description, business_intent_topics, events, linkedin_profile.

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Fetch prospects extractor

Same pattern as Fetch businesses extractor, applied to prospects.

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Listen business events

Enroll a set of businesses (sourced from a Model column of LinkedIn company URLs or domains) and continuously ingest business events into Cargo as they happen. Each new event becomes a record in the destination Model and is unified onto the matching account. Configuration Each ingested event is materialised as a row with event_id, event_name, event_time, business_id, domain, linkedin_company_url, and an event_data object containing the structured event payload.

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Listen prospect events

Same pattern, scoped to prospects. Enroll a set of prospects from a Model column of LinkedIn profile URLs and stream prospect_changed_role, prospect_changed_company, and prospect_job_start_anniversary events into a Model in real time.

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Reference

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Business event types

The following event types are available for Fetch business events and Listen business events: Funding & corporate

• ipo_announcement
• new_funding_round
• new_investment
• new_product
• new_office
• closing_office
• new_partnership

Department growth / contraction

• increase_in_engineering_department (and similar for sales, marketing, operations, customer_service, all_departments)
• decrease_in_engineering_department (and similar for the same departments)

Hiring signals

• employee_joined_company
• hiring_in_engineering_department (and similar for creative, education, finance, health, human_resources, legal, marketing, operations, professional_service, sales, support, trade, unknown departments)

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Prospect event types

• prospect_changed_role
• prospect_changed_company
• prospect_job_start_anniversary

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Use cases

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Best practices

1. Match before you enrich. Every enrichment action requires a business_id or prospect_id. Cache the match output on the model so subsequent enrichment runs are free.
2. Batch with extractors. Use the Fetch extractors (not the Fetch actions) when you need more than 100 records — they paginate to 60,000.
3. Pick the right event surface. Use Fetch business/prospect events for ad-hoc lookups; use Listen business/prospect events when you want a Play to react to new signals automatically.
4. Be specific with intent topics. Bombora intent works best when you supply 12+ topics in the same category — fewer than that produces noisy scores.
5. Caching is on by default. Identical inputs (same business_id, same filter set) won’t be re-billed within the caching window — design your workflows to lean on idempotent inputs.