AI agents for GTM tasks
Autonomous workers research, qualify, and act on GTM signals without manual intervention. The product page shows research, qualification, and SDR-style agent examples.
Cargo
RivendicaCargo is GTM infrastructure for agents, workflows, tools, and data models across web app, API, CLI, and Claude Code. Automate research, enrichment, qualification, routing, and outreach.
What Cargo is
Cargo is GTM infrastructure for teams that want to automate revenue operations around a shared set of primitives: agents, plays, tools, and data models. The site describes it as a layer for data, handoffs, and automation, built to scale revenue without scaling headcount.
It supports both visual and programmable workflows. Users can design and deploy in the app, or work from the API, CLI, Claude Code, and Cursor, which makes it suitable for teams that want a central system for research, enrichment, qualification, routing, and outreach.
Core capabilities
Triggered multi-step plays
Always-on workflows run when a signal fires, such as a new lead, signup, website visitor, mention, or funding event. Plays are designed to repeat the same process reliably.
Reusable tools across the stack
Reusable GTM functions can be called by agents, the UI, the API, or Claude Code. The tools page shows examples like stakeholder lookup, email finding, and revenue extraction.
Unified data models
A unified data model connects CRM, product, and enrichment data so downstream workflows run from one source of truth. The site positions this as the base layer for orchestration.
UI and code-based building
Users can build visually in the UI or programmatically through the API and CLI. Cargo shows TypeScript examples for listing plays, creating batch runs, and triggering agents.
Operational controls for workflows
The platform includes run-level observability, version history, controlled rollouts, retries, and rollback support. These controls are presented as part of the infrastructure approach.
Common workflows
Account list building
Generate lookalike account lists from your best-fit customers and use them in outbound or campaign planning.
CRM enrichment
Keep CRM records current by combining multiple enrichment providers and updating account data from one workflow.
Research and enrichment for outreach
Research prospects across the web, news, and LinkedIn to gather contact details and background information for personalized outreach.
Lead scoring and prioritization
Score accounts and leads from intent and engagement signals so reps can focus on higher-priority opportunities.
Lead routing and assignment
Route incoming leads to the right rep based on territory, capacity, and account fit to reduce handoff delays.
Pros and Cons
Pros
- Combines agents, workflows, tools, and data models in one infrastructure layer.
- Supports both no-code building in the UI and programmatic use through the API and CLI.
- Includes observability, version history, and rollout controls for operational debugging and governance.
- Covers common GTM workflows such as account list building, CRM enrichment, lead scoring, routing, and deal risk detection.
- Pricing page shows a free trial, pay-as-you-grow pricing, and no feature gating across plans.
Cons
- The collected pages do not show a complete public integration catalog, only that Cargo connects to 100+ integrations.
- The source gives examples of workflows and primitives, but not every configuration detail or output format.
- Some pricing specifics are shown as credits and plan tiers, but the site text in scope does not provide full limits for every plan.
FAQ
What is Cargo used for?
Cargo is designed for GTM teams that want to build AI agents, workflows, tools, and data models in one system. The source shows setup and use through the app, API, CLI, Claude Code, and Cursor.
Can teams use Cargo without coding?
Yes. The site shows two ways to build: visually in the UI or programmatically with Claude Code and the API/CLI. It also shows examples of listing plays or agents and creating batch runs from code.
How do the main Cargo primitives fit together?
Cargo positions agents, plays, tools, and data models as the core building blocks. Agents handle autonomous GTM tasks, plays run triggered workflows, tools provide reusable functions, and data models unify CRM, product, and enrichment data.
How does Cargo pricing work?
Cargo’s pricing page says every plan includes AI agents, workflows, and 100+ integrations, with a free trial and pay-as-you-grow pricing. It also says there is no feature gating.
What integrations and tools does Cargo support?
The source highlights 100+ integrations, but it does not provide a full integration catalog in the collected text. The tools page shows examples such as finding stakeholders, finding email, and extracting revenue data.
Quick Facts
- Category
- GTM infrastructure
- Platform
- Web app, API, CLI
- Primary users
- GTM and revenue operations teams
- Source domain
- getcargo.io
- Integrations
- 100+ integrations
- Pricing model
- Free trial, then pay-as-you-grow credits