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LogisticsAI

For transport operations teams

Automate document work in your transport operations.

Turn transport orders, permits and delivery documents into structured records your team can review. Start with a working example, then assess how the workflow fits your documents and systems.

Try a sampleRequest a workflow assessment

Exceptional transport permit

Synthetic product example

Needs review

SYNTHETIC SAMPLE

Exceptional transport permit

Reference
DEMO-2026-0142
Vehicle combination
Tractor and semitrailer
Permitted width
3.80 m

Extracted data

Permit reference
DEMO-2026-0142
Page 1. High confidence.
Maximum width
3.80 m
Page 1. High confidence.
Validity end
16 Aug 2026
Page 1. Unclear final digit — reviewer confirmation required.

The original permit remains the authoritative source.

LogisticsAI in 32 seconds

See six transport workflows move from files to controlled results.

A short product tour covering transport orders, CMR and POD, permits, billing readiness, emissions and supplier invoices — from batch input to a result your team can review.

Batch input
Source-linked review
Visible operational result

Real product interface · synthetic demo data · external and financial actions remain human-controlled

Find the task your team already does

Which task do you want to automate?

6 prepared examples

Choose the document task that takes time today. Each prepared example shows what goes in, what your team receives and what still needs human review.

01

Automate transport order data entry

Transport Order Intake

Turn booking emails, PDFs and images into one evidence-linked order draft for TMS handoff.

  1. InputBooking email or PDF
  2. AutomateExtract + validate
  3. ResultOrder draft
See order intake
02

Process CMRs and proof of delivery

Delivery Validation

Match POD or CMR evidence to a load and route missing, damaged or ambiguous delivery facts for review.

  1. InputCMR or POD
  2. AutomateMatch + check
  3. ResultReview queue
See delivery checks
03

Extract data from transport permits

Exceptional Transport Permit Parser

Read permit fields with page references, confidence and focused human review before export.

  1. InputPermit PDF
  2. AutomateExtract + cite
  3. ResultApproved record
Try the permit demo
04

Check a transport file before invoicing

Freight Billing Readiness

Find missing billing evidence and review source-cited accessorial charge candidates before invoicing.

  1. InputTransport file
  2. AutomateCheck evidence
  3. ResultInvoice-ready file
See invoice readiness
05

Prepare freight status updates

Freight Status Updates

Turn verified events into controlled customer, carrier or internal update drafts with approval before sending.

  1. InputVerified event
  2. AutomateDraft + approve
  3. ResultStatus update
See status updates
06

Build transport emissions reports

Transport Emissions Reporting

Create evidence-backed emissions workspaces, calculations and versioned reporting outputs.

  1. InputShipment data
  2. AutomateCalculate + trace
  3. ResultVersioned report
See emissions reporting
View all tools

In this example, seven manual touches become two human decisions.

The system still performs several checks, but they no longer consume operator attention. Change the assumptions below to model your own weekly document queue.

Current manual path · 7 human touches

  1. 1Receive and open
  2. 2Read and identify the document
  3. 3Find the shipment reference
  4. 4Copy each relevant field
  5. 5Check required data
  6. 6Update the target system
  7. 7Route or notify the next person

Controlled automated path · 2 human touches

Operator provides or forwards the source

01

LogisticsAI handles the routine middle

Classify → extract → match → validate → prepare the handoff

Operator reviews exceptions and approves

Editable illustrative scenario
Current weekly operator time
13.3 h
Estimated weekly operator time
4.5 h
8.8 hoperator capacity released · per week€309equivalent capacity · per week

66.3% less operator time

Break-even exception rate: 54.2%

Calculated from the values above. Weighted post-automation time = routine review minutes + (exception rate × extra exception minutes). Capacity change = documents × (current minutes − weighted time). Negative results are shown as additional work. Cost uses your loaded hourly cost. Setup, integration and ongoing support are excluded.

One file · no account

See your own work become a reviewable result.

Choose the task your team already does, upload a redacted or non-confidential source and inspect the result before creating an account.

The anonymous preview expires after 24 hours. It cannot be downloaded, shared, approved, sent or handed to another system.

Turn related order files into one draft

Use up to three related email exports, PDFs or images.

Evidence beyond our own product

The scale changes. The repetitive work does not.

Established logistics and industrial teams have published measurable results from automating email, document and data-entry work. These cases validate the operating pattern; your result still depends on your documents, rules and systems.

DHL Global Forwarding, Freight
Company-reported

Shared-service automation

50% higher pilot efficiency

DHL reports that its RPA pilot immediately increased efficiency by 50%; it later described more than 160 bots handling work equivalent to 500 full-time roles while people focused on exceptions and higher-value work.

Read the DHL case
C.H. Robinson
Company-reported

Emailed transport orders

5,500 orders a day in about 90 seconds

C.H. Robinson reports that emailed load tenders which could wait up to four hours and require seven minutes of manual entry are now converted into orders in about 90 seconds, including multi-load attachments.

Read the C.H. Robinson release
Volvo Group
Vendor customer story

Invoice and claims documents

10,000+ manual hours saved

A Microsoft customer story reports that Volvo Group deployed document extraction and translation across complex invoices, claims and freight-related paperwork, saving more than 10,000 manual hours.

Read the Microsoft case
Fugro
Vendor customer story

Multilingual invoice processing

2 minutes reduced to 35 seconds

Rossum reports that Fugro reached a 70% automation rate across invoices from 21 countries and reduced average processing time from two minutes to 35 seconds while retaining correction before ERP posting.

Read the Rossum case
Relationship disclosureIndependent industry examples. These companies are not LogisticsAI customers. Results are reported by the named company or its technology provider and link to the original source.

Company names and trademarks belong to their respective owners and are shown only to identify the cited public case.

Inspect the evidence, not a promise.

These public examples use synthetic data. Open them to see the source, structured result and review boundary for yourself.

Interactive demo

Permit source to reviewed fields

Use the interactive specimen to inspect cited values and the one field deliberately routed to review.

Synthetic source · page references · confidence · review state

Open evidence
Show two more evidence paths
Downloadable sample

Shipment data to emissions evidence pack

Inspect the sample output set used for a transport emissions reporting workflow.

Synthetic dataset · JSON · CSV · methodology · quality notes

Open evidence
Product evidence

Operational workflows and boundaries

Compare standard tools with custom automation examples and see where human approval remains required.

No client logos, testimonials or unsupported performance claims

Open evidence
Discuss your process

Start with one queue your team wants gone.

Send a process description, synthetic file or short screen recording. The first conversation identifies the handoff, exceptions and systems involved; it is not a payment commitment.

Send one workflow

Show one queue

Share the manual path, source documents and target system without sending production personal data.

Define the boundary

Agree what can run automatically, which exceptions stop, and who approves the result.

Test with evidence

Run representative synthetic examples and failure cases; compare every result with its source.

Show the final two delivery steps

Connect the handoff

Only after approval, connect the workflow to the agreed operational destination.

Measure and adjust

Track actual throughput and exceptions, then change rules when documents or processes change.