Forecasting & Machine Learning for Logistics & Freight
Know what you'll sell, staff, and stock next month — and order against it. Shaped by the real problems in logistics & freight.
In one line
Forecasting & Machine Learning, shaped for Logistics & Freight
Plenty of logistics & freight decisions still get made on gut feel and last year's spreadsheet — how much to stock, whom to staff, what to set aside for risk. A brokerage runs on email and phone calls, and both are quietly expensive. RFQs sit in an inbox while a faster broker covers the load, BOLs and PODs get rekeyed before an invoice can go out, and dispatchers spend the day making check calls for status a system could already know. Your own history holds a better answer than instinct, if something turns it into a forward view.
For your sector the models that pay off predict the things you plan around — quote-from-email automation and carrier paperwork without rekeying. We build them on your actual data and pick the technique on merit: often a well-chosen statistical model beats a neural network on limited data, and we say so honestly rather than reaching for the most impressive-sounding tool.
We validate every model against data it hasn't seen and tell you plainly where it's reliable and where it isn't — a forecast with honest error bars beats a single confident number. The models, code, and documentation are yours to run and retrain, on your infrastructure, with no per-prediction fee.
What we predict for logistics
A brokerage runs on email and phone calls, and both are quietly expensive. RFQs sit in an inbox while a faster broker covers the load, BOLs and PODs get rekeyed before an invoice can go out, and dispatchers spend the day making check calls for status a system could already know.
Quote-from-email automation
emailed RFQs parsed, rated against your lane history and benchmarks, and answered in minutes instead of hours
Carrier paperwork without rekeying
BOLs, PODs, lumper receipts and rate cons read and matched to the right load so billing doesn't wait on data entry
Exception prediction & proactive notification
flag the shipments that will run late before the customer calls to ask where their freight is
Automated check calls & tracking
status pulled from ELD, telematics and carrier updates and pushed to customers without a dispatcher working the phones all day
Detention & accessorial capture
catch the billable extras that quietly slip through and never make it onto an invoice
Carrier vetting & fraud screening
authority, insurance and double-brokering risk checked before a load is ever tendered
This is for you if
- You plan logistics stock, staffing, or cash on gut feel
- You want to know which customers churn, or which cases carry risk
- You need to spot fraud, failures, or problems before they cost you
- You have years of data but nothing turning it into a forward view
What you get
- A forecasting or scoring model built on your logistics & freight history, validated honestly
- The right technique for quote-from-email automation — classical ML or deep learning, chosen on merit
- Clear accuracy expectations, including where the model is and isn't reliable
- Integration so predictions reach the people and systems that act on them
- The models, code, and documentation handed over — yours to run and retrain
However we build it, you own it
Forecasting & Machine Learning for Logistics — answered
Less than most people assume — a couple of years of ordinary logistics & freight history is often enough, and some scoring problems need surprisingly little. Before we commit, we look at your data and tell you honestly what it can support, rather than building something that looks confident and isn't.
No — and anyone who says so is selling something. For a lot of logistics & freight forecasting, a well-chosen statistical model beats a deep network: more accurate on limited data, cheaper to run, easier to trust. We pick the technique that performs best on your problem, not the one that sounds most advanced.
We validate against data the model hasn't seen and tell you plainly how accurate it is and where it's weak. You'll know when to lean on the forecast and when to apply your own logistics judgment — which beats a single confident number that hides its uncertainty.
Yes — connecting to your TMS (McLeod, Turvo, or whatever you run) is part of the build, not an afterthought. The system reads emails and documents, then writes loads, statuses, and billing data back into the tool your team already lives in, so it acts inside your workflow instead of becoming another tab to check.
No. It takes the repetitive keyboard work off them — rating routine RFQs, reading paperwork, making check calls — so they spend their time negotiating, covering hard lanes, and handling exceptions. Anything the system is unsure about is routed to a person with the context already gathered, not decided blindly.
Forecasting & Machine Learning for Logistics. Let's scope it.
A short call, then a clear, agreed scope in writing. No obligation, and an honest no if it isn't a fit.