How Engine turns travel signals into outbound pipeline with Cargo

Engine sells corporate travel to businesses in field services, construction, and oil and gas. Identifying companies with a strong need for travel is central to its outbound motion.

Before Cargo, reps relied on their experience and available information to prioritize accounts. Without clear signals of travel demand, it was difficult to consistently identify the best opportunities and tailor outreach to each company’s specific needs.

Why Engine chose Cargo

Engine’s account intelligence lives in Snowflake and Salesforce. The team needed to put that intelligence to work wherever a commercial decision happened: which account a rep should contact, how to follow up on website interest and which prospects an ad campaign should pursue.

Cargo connected those decisions to execution. Engine could build around its own targeting criteria and customer-value model, with sales approval inside the workflows that needed it. The data could stay in the existing stack while Cargo carried the next action into the tools sales and marketing already used.

Example pattern: the rep decides when outreach starts.

humanReview(
  {
    connectorUuid: slack.uuid,
    channelId: input.ownerChannelId,
    title: "Approve outreach?",
    content: input.travelSignal,
  },
  {
    approved: () => uses.prepareOutreach(input),
  },
);

Cargo waits for approval before calling prepareOutreach, a custom tool for sourcing contacts and starting the sequence.

How Engine built the system

1. Turn travel signals into outreach a rep can approve

A Cargo play starts directly from a Snowflake SQL query, checks a travel signal against Engine’s targeting criteria and brings the opportunity to the account owner in Slack. The rep sees why the account is relevant and can accept or decline contact sourcing and tailored outreach.

Once the rep accepts, Cargo sources contacts and carries the signal into custom copy in the rep’s Outreach sequence. Accounts without an owner are assigned in rotation and follow the same process. Research, approval and follow-through sit in one workflow, so a recommendation arrives with both a reason to act and a way to do it.

2. Connect website interest to the right rep’s follow-up

A visitor event triggers a webhook. Cargo matches the person to a Salesforce lead or creates one, then adds them to the account owner’s Outreach sequence. Growth can test website interest as a source of opportunities without adding a separate manual routing task.

“We built the whole website-visitor flow in one pass, from the webhook to the right rep’s Outreach sequence,” says Will.

Cargo’s CLI and Claude Code use Engine’s Markdown build guides to turn that acquisition idea into a working play. The guides carry the team’s build patterns into the next implementation.

3. Give ad platforms Engine’s definition of customer value

When an inbound lead arrives, Cargo triggers business verification and scoring with Engine’s customer lookalike model. The model predicts spend over the next 12 months. Cargo returns that predicted value to Google Search and paid social platforms to guide bidding.

A form submission can now tell the ad platform more than whether someone converted. It also indicates the account’s expected commercial potential, so campaigns can optimize toward accounts with greater expected spend.

“We send our predicted customer value back to the ad platforms so they know which accounts we want more of,” says Will.

What Cargo makes possible

Sales judgment is part of the workflow. Cargo brings the recommendation to the account owner before sourcing contacts or preparing outreach. Engine can scale the work around the decision while keeping control with the person who owns the relationship.

Existing models become operational. Snowflake queries and Engine’s customer-value model inform actions in Slack, Salesforce, Outreach and ad platforms. The team can use the intelligence it has already built across several sales and acquisition workflows.

Example pattern: let customer value guide the next bid.

const company = uses.verifyBusiness(input);
const value = uses.predictValue(company);
uses.sendAdConversion({
  leadId: input.leadId,
  value: value.expectedSpend,
  currency: "USD",
});

Custom tools connect the team’s value model to its ad platforms. Cargo passes the prediction into the conversion value, keeping scoring and the handoff in one workflow.

Build patterns carry into the next workflow. The CLI and reusable build guides give coding agents Engine’s implementation context. Growth can extend a working pattern to another use case without designing every connection from the beginning.

What changed

An estimated 30% increase in outbound pipeline output

Will Berner, Senior Manager of Growth, estimates that Engine’s outbound pipeline output increased owing to Engine’s broader outbound program enabled by Cargo; data from before its account-scoring model is limited.

Cargo connects the intelligence behind that program to contact sourcing, relevant messages and follow-through that reps approve.

2x higher visitor-to-qualified-opportunity conversion

Website interest has a direct path into the right rep’s follow-up sequence. Matching the lead, assigning the owner and starting the sales workflow happen in the same play.

15% higher qualified conversion from paid acquisition

Returning predicted customer value connects ad bidding to Engine’s commercial model, helping campaigns pursue accounts with greater expected spend.

What other teams can learn

Start with a decision your data should improve, then connect it to an action in the team’s existing tools. Put human approval where judgment matters and automate the preparation and follow-through. A useful account signal earns its value when someone can act on it.