Freight and logistics: answering quote requests in minutes

In freight, the quote that arrives second often loses the job. Requests tend to share the same handful of facts: an origin, a destination, a weight, a volume, a date. Yet in many forwarding and transport companies, someone still reads each request, opens the right rate sheet, adds the surcharges, formats the quote and sends it. Meanwhile, customers are messaging to ask where their cargo is, and carrier invoices sit in a pile waiting to be checked.

If you run a logistics business, this pressure comes from every direction. Requests arrive around the clock from shippers in other time zones, by email, by WhatsApp and through portals, in several languages. Every new lane adds to the back-office load.

I don't run a logistics company. I founded Oliverlist in 2016, without outside investors, and I manage more than 100 people day to day. One lesson from those years applies to many businesses: back-office work looks much the same from one industry to the next. Read a request, apply a rule, reply, warn, check an invoice. Here is what AI can take off your team's plate in freight and logistics, and what should stay with you. These are possibilities to adapt to your operation, not client case studies.

Quotes: let AI do the reading and the pricing

Quote requests rarely arrive in a clean format. A three-line email, a packing list in PDF, a buyer's spreadsheet, a photo of a handwritten note. AI can read all of these and extract what matters: pickup and delivery points, number of pallets or containers, weight, dimensions, requested date, and special conditions such as dangerous goods, temperature control or a fixed delivery window.

It then applies your rules, not its own: your lane rates, your minimum charges, this month's fuel surcharge, your customer-specific terms, and the currency each customer is billed in. It drafts the quote in your template and spells out every assumption. If something is missing, it does not guess. It drafts the question to send back to the customer.

Decide in advance who signs off. A standard request on a known lane for a known customer can go out after a quick check. Anything off the rate sheet, a new customer, an unusual volume or a negotiated price goes to whoever owns pricing. That is where you win on response time: your people stop retyping and focus on the quotes that need judgment.

Tracking: tell customers about a delay before they call

A delay you report yourself tells the customer you are watching their cargo. A delay they discover on their own forces them to call, wait and chase.

To get ahead of that call, connect your tracking data first: driver status updates, carrier and shipping line portals, data feeds from your partners, emails from subcontractors. AI then compares every shipment with the date you promised. When a gap appears, it drafts a message with the new estimate and the known cause. For a few hours' delay on a regular customer, the message can go out automatically. For a key account, or a delay that means missing a vessel cut-off, it goes to your operations team first, ready to send or edit.

The same data handles the "where is my shipment?" messages. The answer comes from statuses you already have, without pulling an operator away from their work. Your team keeps its time for real problems: a breakdown, a customs hold, a customer who needs a phone call rather than a message.

Carrier invoices: check every line, not a sample

Matching carrier and subcontractor invoices is tedious, so it is often done by sampling. AI can check every line. It compares each invoice with the transport order, the agreed rate, the proof of delivery and the extras you accepted. Waiting time billed without evidence, a surcharge charged twice, a rate that does not match the agreement: each discrepancy is listed with the document that proves it, and your finance team decides what to dispute.

This is where my Oliverlist experience applies directly. Since 2020, we have automated the entire chain, from importing contacts to issuing the invoice. Between 2019 and 2023, that automation tripled our revenue per person. The jump did not come from generative AI, which entered our product in May 2023. It came from automating predictable work. AI comes next, to read what is unstructured: an email, a PDF, a supplier invoice.

Where to start

Do not launch all three projects at once. Pick the one that weighs on your team or your customers today.

Then take a rough baseline: time from request to quote, share of quotes reworked by hand, number of "where is my shipment?" messages per week, value of the discrepancies found in one month of invoices. Three months later, these numbers will tell you whether the tool changed anything.

Finally, put the approval rule in writing: what goes out on its own, what goes through a person, and who that person is. At Oliverlist, I follow a simple principle: AI prepares, and a person approves anything that commits the company. In freight, that means a price, a promised delivery date, a dispute with a subcontractor.

Key takeaways

• Quoting suits AI because it is repetitive: read the request, apply your rates, draft the document. Off-sheet pricing stays a human decision.
• Warning customers about delays requires connected tracking data. Without it, AI has nothing to watch.
• Carrier invoice checks can cover every line instead of a sample.
• Start with one flow, measure before and after, and write down what a person must approve.