AI agents for sales teams in logistics

An RFQ arrives at 10:05 and the shipper has three other brokers on the thread, but the rep has to open the rate sheet, check the market, work out margin, and type the reply, so the quote lands after the shipper has already picked someone.

→An agent reads each RFQ, prices the lane from your carrier cost history and the current fuel surcharge, and drafts the reply, so the rep adjusts the margin and approves a quote while the request is still fresh.

A 200-lane tender package takes a rep most of two days to price in a spreadsheet, and the team only discovers later that it bid on lanes no carrier will cover at that rate or volume.

→An agent prices every lane from your history, cross-checks volumes against carrier coverage, and assembles the response package, leaving the sales lead to decide which lanes to bid and at what margin.

Quotes go quiet and nobody notices, because the follow-up lives in a rep's memory and the CRM stage still says negotiating three weeks after the shipper stopped answering.

→An agent watches every sent quote, drafts the next touch for approval when one goes two days unanswered, and keeps the CRM stage honest so the pipeline report shows what is really alive.

Contract rates are renewed on instinct because nobody has time to pull a year of lane margin before the call, so the accounts that quietly stopped paying their way are repriced like the ones that did.

→An agent flags agreements ending within 90 days, pulls lane margin and volume trend for each account, and drafts the renewal offer, so the rep walks in knowing which lanes to hold and which to reprice.