Order AI

Order AI is an enterprise SaaS platform that eliminates manual order entry for the drayage and intermodal logistics sector. It sits between a drayage company's clients and their Transportation Management System as a centralized hub that takes in shipping documents in many formats, PDFs, tabular files, and multi-page batches, then automatically classifies each type, extracts structured order data, and routes verified orders into the TMS.

The product existed to solve a problem specific to this corner of logistics: document diversity at scale. Drayage companies receive orders from dozens of clients, each sending documents in their own format, layout, and data structure. Before Order AI, the only way to turn those documents into TMS orders was for a trained operator to read each one and re-key the data by hand. Traditional OCR could not keep up, because it cannot adapt to format variation without extensive per-client configuration.

Drayage companies receive hundreds of shipping orders daily from multiple clients, each sending documents in different formats, layouts, and structures. The core challenge was twofold, and both halves compounded as order volume grew.

First, manual order entry. Operations staff spent the majority of their time reading incoming documents and typing order data (container numbers, addresses, billing templates, equipment requirements, reference codes) into their TMS. It was time-consuming, since every document needed a trained operator to interpret and transcribe. It was error-prone, since human transcription introduced costly mistakes that cascaded into shipping delays, missed pickups, and billing disputes. And it was unscalable, since growing volume demanded proportionally more data-entry staff, directly cutting into margins.

The approach was to combine intelligent document classification with automated data extraction, and to wrap both in a human review layer that operators could trust. Instead of forcing every client onto one template, the platform learns to recognize each client's document variants, matches an incoming document to a known variant with a confidence score, and adapts as new formats appear. Once a document is classified, Order AI extracts the structured order fields automatically, so the operator's job shifts from re-keying data to verifying it.

That verification is a first-class part of the design rather than an afterthought. A built-in 4-eyes review workflow lets a reviewer validate extracted data against the original document side by side, through Compare and Switch Preview modes, before anything is dispatched. Verified orders flow directly into the client's TMS with real-time status tracking (Processed, Sent to TMS, Accepted by TMS, or TMS Warning), and incoming work is queued in a Request Inbox grouped by company and variant, supporting single-order and multi-order documents in one workflow.

Designing an AI-assisted workflow taught me that the interface has to earn trust before it can save time. Operators would only stop re-keying data if they could see the system's confidence and check its work quickly, so the confidence scores and the side-by-side review were not polish on top of the automation, they were what made the automation usable.

Treating document diversity as the normal case, rather than an edge case, reshaped the whole design. Once I stopped assuming clean, uniform input and built the classifier, the review flow, and the inbox around variation, the messy reality of dozens of client formats became something the platform absorbed instead of something operators had to fight.