All agents

Spot lanes priced from history and market rates

Prices spot rate requests from lane history, market rates and buy cost under your margin rules, then quotes and builds the load in the TMS on acceptance.

How it works
The reality

Spot freight goes to the fastest answer.

Every spot request needs lane history, current market rates and a margin check. Reps juggle that with covering loads and calling carriers. Requests pile up and win rates fall.

A shipper emails eight spot requests at 9 am. The rep quotes three by noon, and the other five go to brokers who answered first.
A typical day for freight brokerage
How it works

Spot quote desk, step by step.

The agent does the work inside your systems. Your team approves what matters.

  1. 1

    Read the request

    Pulls lane, equipment, dates and weight from emails, portals and load boards.

  2. 2

    Price the lane

    Checks your lane history, market rates and buy cost and applies margin rules.

  3. 3

    Quote and book

    Sends the quote and builds the load in the TMS once the shipper accepts.

What changes

Your team keeps the judgment calls.

The agent takes the repetitive work. People approve, decide and talk to customers.

  • Higher win rate on spot requests
  • Consistent margins by lane
  • Reps spend time covering, not quoting

Works inside the systems you already run

EmailTMSSpot quote deskRate dataLoad board
Questions we get

Before you ask.

Prices spot rate requests from lane history, market rates and buy cost under your margin rules, then quotes and builds the load in the TMS on acceptance. It works in three steps: read the request, price the lane and quote and book.

It works inside the tools your team already uses, including email, TMS, rate data and load board. Nothing to rip out and nothing new to learn.

It starts in shadow mode, drafting the work for your team to approve or correct. You decide what it handles on its own, and every action is logged.

Your data is used only to run the workflows you approve. We do not use your proprietary data to train public or unrelated AI models.

Get started

Start with one agent.

Pick the workflow that hurts most. We'll scope it on the first call.