Mallory Alexander Turned Import Paperwork Into $18M

A freight forwarder can move a container across an ocean and still get stranded by a document. That was the awkward constraint inside Mallory Alexander International Logistics: the physical network was built for movement, but every import still arrived with a small blizzard of commercial invoices, arrival notices, importer security filings and letters of credit. Each file had to be read, keyed, checked and passed along. More volume meant more hands on keyboards.

That trade-off mattered because Mallory Alexander was not planning for incremental growth. The Memphis-rooted company, whose operating history reaches back more than a century, was pursuing an ambition to grow revenue from $250 million to $1 billion through a mix of organic expansion and acquisitions. In that plan, back-office capacity was not a housekeeping concern. It was part of the capital strategy.

The details were unforgiving. Importer security filings go to the government. Letters of credit are governed by banks and documentary terms. A misplaced digit is not a cosmetic defect; it can delay cargo, create fees or jeopardize payment.

Letters of credit have to be perfect. 99.5% isn’t good enough. It’s got to be a 100% perfect.Paul Svindland · CEO, Mallory Alexander

That sentence captures the real tension in operational AI. Speed is easy to demonstrate in a demo. Reliability is the product. Mallory Alexander needed more throughput without turning compliance into a wager.

Three workflows, not a moonshot

Svindland inherited an AI initiative already underway with another vendor. He paused it, reassessed the market and chose Pallet, citing its freight-forwarding knowledge and ability to integrate with CargoWise. The decision is revealing: the winning pitch was not intelligence in the abstract. It was fluency in the actual environment where operators spend their days.

The first deployment stayed deliberately narrow. Pallet’s agents were assigned three repetitive import workflows: importer security filings, letters of credit and arrival notices. These were not glamorous edge cases. They were the recurring chores consuming operator time across thousands of shipments. The software extracted information from documents, validated it and moved the work through the existing operating system.

That is a useful pattern for any operator shopping for automation. Pick a workflow with enough volume to matter, enough structure to measure, and enough pain that employees will notice when it disappears. Then define the acceptable error rate before rollout. “Use AI everywhere” is a slogan. “Process these three document types to this standard inside CargoWise” is an operating plan.

Trust also entered through the side door. Svindland’s endorsement of Pallet was strikingly unromantic: “You don't oversell. You do what you say you're going to do. We just felt comfortable.” In enterprise software, comfort is often the residue of promises kept at workflow level.

The denominator changed

Within months, Mallory Alexander said each import operator could handle three times as many files. The company also reported 100% accuracy on importer security filings and letters of credit. Put together, those measures tell a more useful story than “hours saved.” The numerator—shipment volume—could rise while the denominator—operators required to process it—did not have to climb in lockstep.

That difference is what converts a productivity project into a growth system. If the same team can absorb substantially more work, a company can pursue new accounts or integrate acquisitions without immediately rebuilding the back office. The employee experience changes, too: people spend less of the day retyping fields and more of it handling exceptions, talking with customers and making judgment calls that software cannot settle.

Mallory Alexander attached a larger number to that operating leverage: $18 million in enterprise value created. The figure is a management-reported estimate in Pallet’s customer case study, not a disclosed transaction price or independent valuation. Its importance is less about decimal-point precision than about the language it introduces. Automation was not presented as an IT expense with a vague innovation halo. It was connected to capacity, cost structure and the value of the whole business.

Accuracy earns permission

Much of the conversation around AI begins with possibility. Operations teams tend to begin with consequences. What happens when the model is wrong? Who catches it? Which customer, regulator or bank finds out first? In import logistics, those questions are not resistance to change. They are the job.

Mallory Alexander’s reported result suggests a practical adoption sequence: use accuracy to earn permission, then use throughput to expand the business case. The company did not ask operators to accept lower quality in exchange for more speed. It says the error rate fell while output rose. That pairing matters because it turns automation from a labor-saving device into a risk-control mechanism.

The lesson travels beyond freight forwarding. A claims processor, procurement team or healthcare administrator faces the same choice whenever documents arrive in messy formats but decisions must land in a structured system. The best starting point is often not the most creative task. It is the boring one with a visible queue, a stable definition of done and a costly mistake.

There is also a design lesson here. Integration beat reinvention. By working with CargoWise, Pallet could remove steps without demanding that Mallory Alexander replace its system of record. The new layer became valuable because it respected the old plumbing. In transformation projects, the shortest route to the future often runs through the software already open on everyone’s screen.

From imports to the rest of the ledger

Once the import workflows proved themselves, the map widened. Mallory Alexander began extending Pallet into accounts payable and accounts receivable—two functions where documents, matching rules and exceptions again pile up at scale. This is how durable automation usually spreads: not as one enormous launch, but as a series of adjacent victories.

The sequence matters. A successful first workflow produces more than a return on investment. It creates internal evidence. Operators learn where the system is strong, managers learn how to set controls, and executives learn which measurements deserve attention. The organization develops an automation muscle before taking on the next process.

There is a temptation to read the Mallory Alexander story as a referendum on headcount. The sharper reading is about capacity allocation. The stated goal was to scale without expanding staff proportionally, not to make expertise disappear. Freight remains full of exceptions: damaged cargo, missing information, changing trade rules, customer promises and physical events that refuse to fit neatly into a form. Removing repetitive data work gives experienced people more room to deal with exactly those moments.

That is also why the three-times figure travels. It is easy for another operator to carry into a budget meeting. If your volume doubled tomorrow, where would work queue up? Which documents would require another hire? Which of those steps could be extracted, validated and entered automatically? The bottleneck points to the first experiment.

Find the paperwork that makes growth expensive

The most reusable idea in Mallory Alexander’s result is not a particular model or vendor. It is the order of operations. First, tie automation to a business ambition large enough to matter. Second, choose a narrow workflow with high volume and clear rules. Third, integrate with the operating system rather than building a parallel universe. Fourth, make accuracy a gate, not an aspiration. Finally, translate throughput into financial language the company already uses.

That playbook sounds almost too ordinary for a technology sold with science-fiction vocabulary. Good. Operations are where grand claims go to meet Tuesday morning. A system wins when the file is correct, the employee trusts it, and the next shipment does not require another person to retype the same fields.

Mallory Alexander’s larger ambition remains unfinished: quadrupling revenue over five years is a destination, not a case-study statistic. Acquisitions, market conditions and execution will decide how far the company travels. But the import operation offers a concrete early signal. The business says it can now process three times the files per operator, at perfect reported accuracy for the critical documents in scope.

We expect to grow immensely from our partnership with Pallet.Paul Svindland · CEO, Mallory Alexander

The useful part for everyone else is more modest and more actionable. Find the paperwork that makes growth expensive. Make it disappear without letting accuracy disappear with it. Then count what the business can do next.

Reporting based on Pallet’s Mallory Alexander customer story and Mallory Alexander company materials. Performance and enterprise-value figures are reported by the companies.