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AI for Logistics & Transportation

AI for route optimization, fleet telemetry, warehouse vision, and supply-chain visibility — built for operators where every mile, minute, and pallet shows up on the P&L.

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24%
Reduction in delivery times
18%
Fuel cost savings
35%
Warehouse efficiency improvement
29%
On-time delivery improvement

Trusted by teams at MatchWise, ServiceCore, QuantFi, Desson Abogados, Mexico Por el Clima, and others across the US and LATAM.

What we build

Anatomy of an AI workflow for Logistics & Transportation

Each ships in 8–12 weeks. Pick a workflow to see what goes in and what comes out.

Route optimization & ETA prediction

Multi-vehicle, multi-stop VRP solvers tuned to your network — modeling driver hours, vehicle capacity, time windows, customer SLAs, fuel cost, and return loads simultaneously. Predictive ETAs trained on your historical lane data, not generic mapping APIs.

TMS-only routing baseline15–25% fewer miles, 15–20% lower fuel

Inputs we read

  • Loads, stops, and time windows from TMS
  • Driver hours-of-service from ELD
  • Vehicle capacity and equipment constraints
  • Traffic, weather, and toll-network data
  • Historical lane and carrier performance

Outputs delivered

  • Optimized multi-stop routes per vehicle
  • Predictive ETAs per stop with confidence bands
  • Re-optimization on disruption (call-outs, traffic, reschedules)
  • Driver-app turn-by-turn with proof-of-delivery
  • Cost-per-mile and cost-per-stop telemetry

Decide your path

Build, buy, or partner?

Three real options, each with different trade-offs on cost, control, and customization.

FourKites · project44 · Flexport AI

Vendor SaaS

Best for: Mid-size operators wanting better visibility and ETAs without custom engineering

Data control
Vendor-controlled; data flows to vendor cloud
Customization
Low to medium — preset playbooks
Time to value
Weeks
Cost (3 yr)
Significant recurring per-shipment fees
Recommended

Clearframe partner build

Best for: Operators with distinctive networks — LATAM, multi-modal, last-mile fleets, fragmented carrier bases

Data control
Your environment; no third-party training
Customization
High — tuned to your network, lanes, and constraints
Time to value
10–16 weeks per workflow
Cost (3 yr)
Predictable; usually pays back in 6–12 months
DIY

In-house build

Best for: Operators with mature engineering and OR teams

Data control
Full control
Customization
Full
Time to value
12+ months
Cost (3 yr)
Highest upfront, lowest recurring

What is AI for logistics and transportation?

AI for logistics is the application of operations research, machine learning, and computer vision to the routing, scheduling, warehousing, and visibility decisions that determine whether a logistics business makes or loses money on a load. Used correctly, it cuts miles, cuts fuel, cuts dwell time, and improves on-time delivery — simultaneously, not as trade-offs.

In logistics, every minute and every mile shows up on the P&L. Most operators are still routing in a TMS that hasn't seen a meaningful algorithm update since 2015, dispatching from spreadsheets, and reacting to disruptions after customers call. We build AI systems that compress those decision loops and turn the data already in your TMS, ELD, telematics, and WMS into routes, schedules, and alerts you can ship today.

Glossary

Key terms on this page

TMS (Transportation Management System)

The system of record for loads, carriers, and shipments — Oracle TMS, Manhattan, MercuryGate, McLeod, and similar.

WMS (Warehouse Management System)

The system of record for inventory, receiving, picking, and shipping inside a facility.

ETA (Estimated Time of Arrival)

Predicted arrival time for a load. AI ETAs are tuned to your lanes and carriers and are meaningfully more accurate than generic visibility-platform ETAs.

OS&D (Over, Short & Damaged)

The receiving exception category covering shipments that arrive with too much, too little, or damaged freight — and the workflow for resolving them.

EDI (Electronic Data Interchange)

Standardized B2B message formats (204 load tender, 210 invoice, 214 status, 990 acceptance) used by carriers, brokers, and shippers to exchange shipment data.

How we work

What the engagement looks like

A typical first engagement runs 10 to 16 weeks and ships a single production-grade capability — most often route optimization, predictive ETAs, or warehouse vision — measured against the operator's P&L.

1–2 weeks

Step 1

Paid scoping sprint

Map data sources (TMS, WMS, ELD, telematics, carrier APIs), define success metrics, and align with operations leadership on the target workflow.

Data-source inventoryBaseline metrics (cost per mile, OTIF, dwell time)Success criteria signed by operations leadership
6–10 weeks

Step 2

Build

Same senior engineers from kickoff to deploy. Weekly demos against your actual loads and lanes — never a synthetic dataset. Validate against historical loads before any production traffic.

Working model with weekly demosHistorical backtest reportTMS and ELD integration adapters
Week 10–16

Step 3

Shadow & production deploy

Run in shadow mode for 2–4 weeks alongside the existing process, then graduate to production behind a feature flag — one terminal, lane, or facility — before scaling firm-wide.

Shadow-mode comparison reportFeature-flag rolloutLive ops dashboard and on-call runbook

We don't ship demos. Every deployment is measured against cost per mile, cost per stop, on-time-in-full, claims rate, dwell time, and driver utilization.

How we handle your data

Logistics AI has to clear two bars: data residency and operational safety. Models that touch dispatch or routing are wrapped in operational guardrails — hours-of-service compliance, restricted-route enforcement, and human override at every irreversible decision.

What we do

Your data stays in your environment
No third-party model training
Per-shipment audit logs
Per-tenant data segregation
Human override on every irreversible decision

Architectures designed to meet

SOC 2 controls
GDPR and LATAM equivalents (LFPDPPP, LGPD)
CTPAT and customs documentation (CFDI, CTe, ACE, ACI)
ISO 27001
FMCSA ELD and hours-of-service documentation

We don't carry these certifications ourselves — your firm's compliance posture stays yours to claim.

Frequently asked questions about AI for logistics & transportation

How is AI route optimization different from Google Maps or a TMS routing module?
Generic mapping APIs minimize a single trip's drive time. AI route optimization solves a multi-vehicle, multi-stop, multi-constraint problem with hundreds of variables — driver hours, vehicle capacity, time windows, customer SLAs, fuel cost, traffic forecasts, return loads. Our models routinely cut total miles 15–25% and fuel cost 15–20% versus TMS-only routing.
Do we need clean data before AI is worth doing?
You need usable data, not perfect data. Most logistics operators have everything they need in their TMS, ELD, telematics, and WMS — it's just siloed. The first 2–3 weeks of a typical engagement is data plumbing; the AI work starts the moment those pipes are connected.
Will AI computer vision work in our warehouse lighting and dust conditions?
Yes — warehouse vision models are trained on your environment, not a clean lab. We use site-specific data collection, fine-tune on your camera feeds, and monitor model drift continuously. Damage detection, dimensioning, label OCR, and pallet counting all run reliably in production warehouse conditions.
How long until route optimization pays back?
Operators typically see fuel and labor savings in the first quarter — 15–20% on routed miles is normal. Full payback (including integration cost) usually lands in 6–9 months for fleets of 50+ vehicles. Smaller fleets pay back faster on a per-vehicle basis but the absolute dollar savings are smaller.
Can AI predict supply chain disruptions before they happen?
Predict perfectly, no. Surface the right early-warning signals — port congestion building, carrier capacity tightening, weather affecting a key lane, supplier on-time-in-full slipping — yes, and that's where the real value sits. We build models that turn the data already in your TMS, port APIs, and weather feeds into actionable disruption alerts 24–72 hours before they bite.
How does this compare to Project44, FourKites, or our existing TMS?
Project44 and FourKites give you visibility — where the freight is. Our work is decisioning — what to do about it. Most operators run both: a visibility platform for the data layer and custom AI on top for routing, predictive ETAs tuned to their network, and exception handling. We integrate with both rather than replacing them.
Does this work for LATAM logistics operations?
Yes. LATAM is one of our core markets — we account for the realities of Mexican, Brazilian, and Andean operations: complex fiscal documents (CFDI/CTe), varied infrastructure, security routing constraints, last-mile fragmentation, and bilingual driver apps. Our route models incorporate local traffic patterns and toll networks, not just the highway grid.

Related services

ai machine learningcomputer visionworkflow automationchatbots ai agentsretrieval augmented generationinternal tooling

Most logistics & transportation teams we work with ship to production in 90 days.

Worth 30 minutes to see what that would look like for your firm? Book a call with one of our senior engineers — no sales handoff, no deck.

Book a 30-minute call