Lathe.
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Autonomous platformLogistics · 2025

An ops platform where agents do the chasing and humans do the judging

of quotes produced with zero human touch

70%

median quote turnaround, down from 3.5 hours

8 min

quote volume handled by the same ops team

3.2×

The problem

§ 01

Castor's operations team spent most of every day on work a careful reader could automate: pulling rates from carrier portals and inboxes, assembling quotes, chasing confirmations. Quoting speed was the business — and theirs was capped by headcount.

Leadership wanted automation but had been burned before by 'RPA' that shattered every time a carrier changed an email template. They needed autonomy that failed safely.

The build

§ 02

We ran an architecture sprint first and agreed the success metric in writing: percentage of quotes produced end-to-end without human touch, at equal-or-better accuracy. Build commitment came after, not before.

The platform runs a small fleet of agents — rate extraction, quote assembly, confirmation chasing — on durable workflows that resume after failures instead of dying silently. Anything below a confidence threshold escalates to a human with every document and decision attached.

The ops team got an observability console, not a black box: every agent action is traced, costed and replayable. Trust was a feature we built, and rollout went team by team as the numbers earned it.

LangGraphClaude APIDurable workflowsHuman-in-the-loopOpenTelemetryNext.js console

The escalation design is the whole product. My team stopped being data-entry clerks and started being underwriters.

Operations Director, Castor Freight

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