Operational AI · real examples

Nine operational AI examples running in production.

Not concepts. These are workflows from systems we built and operate: our own TMS, a 200-org inspection platform, and client operations in logistics, field services, and industrial work. Each one follows the same loop, and each one keeps a person in charge of the judgment calls.

Try one live: the document extractor →Price your own workflows
01. The shape they share

Every example is the same loop wearing different clothes.

Fig. — The operational loopevery deployment, same shape
01
Capture
emails, PDFs, portal screens, texts become structured records
02
Structure
records join to the jobs, loads, or cases they belong to
03
Propose
the AI drafts the action: an extraction, a match, an exception flag
04
Approve
a person rules on it — one click, full context
05
Act & log
the system executes and writes the audit trail
Capture the messy input, structure it, let the AI propose, let a person approve, act and log. Every example below is this loop pointed at a different bottleneck.
02. Intake & documents

Where the re-keying dies first.

Rate-con email → load
BeforeDispatcher re-keys stops, references, and rates from a PDF into the TMS.
AfterForwarded email becomes a drafted load; a dispatcher approves or corrects it.
minutes → seconds per loadLive in Tandem →
BOL / freight doc extraction
BeforeBilling hunts paperwork across inboxes and types totals into accounting.
AfterDocuments become structured records on arrival, joined to the right job.
try it on a real BOLInteractive demo →
Field report structuring
BeforeInspectors' photos and notes land in email; someone assembles reports by hand.
AfterFindings file themselves into rated, photo-backed reports with an audit trail.
200+ orgs on DocuPaintCase study →
Document automation service
03. Visibility & exceptions

The system watches. People decide.

Document verification queue
BeforeMismatched paperwork is discovered at billing time, weeks after delivery.
AfterAI compares BOL against rate con on arrival; a person rules on each finding.
errors caught pre-invoiceHow the queue works →
Live ETAs & exception surfacing
BeforeCheck calls all day; problems surface when the customer calls first.
AfterGPS-driven ETAs; the board stays quiet unless a load needs a decision.
check calls mostly goneThe board →
Credit & policy gates
BeforeA customer over their limit gets three more loads before anyone notices.
AfterEvery new order is checked against exposure automatically; humans handle the flag.
policy enforced at intake
The human-in-the-loop architecture
04. Billing & reporting

Clean data upstream makes money move downstream.

Invoice assembly & gating
BeforeInvoices go out incomplete and bounce; DSO stretches while paperwork is chased.
AfterBilling is gated on verified documents; invoices leave complete the first time.
first-pass invoicesThe money path →
Settlement & reconciliation prep
BeforeSomeone matches payments to jobs across systems in a spreadsheet, monthly.
AfterPayments join to their loads continuously; the ledger reconciles itself daily.
month-end shrinks
Report drafting
BeforeOps managers assemble the Friday report by hand from four systems.
AfterThe draft writes itself from live data; the manager edits judgment, not tables.
hours back every weekDocument automation →
Which of these fits your operation? →
05. Go deeper

The rest of the Operational AI picture.

Start with the frameworkif you haven’t, then price your own version of these workflows.