US Air Force
Document AI

The Problem

Air Force logistics runs on paper — even when the paper is digital. Supply requisitions, personnel transfer forms, and maintenance documentation all moved through manual, human-in-the-loop processes: someone reads a form, decides what kind of document it is, keys the relevant fields into the system of record, and routes it to the next person in the chain. At the volume a logistics operation runs at, that manual handling wasn't a minor inefficiency — it was a bottleneck that slowed everything downstream of it: parts didn't move until requisitions were processed, personnel actions stalled until transfer paperwork cleared, and maintenance records lagged behind the work they were supposed to document.

The documents themselves weren't uniform. Different forms, different formats, some handwritten annotations, some scanned copies of copies — the kind of unstructured, inconsistent input that breaks simple rule-based automation and traditionally requires a person to make sense of it.

What We Built

Tebeau Group developed the core AI processing engine behind an Intelligent Document Processing (IDP) platform: the underlying models and pipeline that take unstructured documents — scanned forms, PDFs, mixed-format submissions — and turn them into structured, system-ready data without a person manually transcribing each one. That engine was integrated into the broader solution delivered to the Air Force by our federal-contract partner (see “Our Role”).

The processing engine combines several layers working together:

Classification

Incoming documents are automatically identified by type — a supply requisition looks different from a personnel transfer form, which looks different from a maintenance record — so each one is routed into the correct extraction and handling logic without a human presorting the queue.

Extraction

Machine learning models pull the specific fields that matter out of each document type — requisition numbers, part identifiers, quantities, transferring and gaining unit information, maintenance codes and dates — converting free-form or semi-structured input into clean, structured data.

Routing

Once a document is classified and its data extracted, the platform routes it to the right downstream system or queue automatically, instead of waiting for someone to read it and decide where it goes next.

Validation

Because this data feeds real logistics and personnel decisions, the system flags low-confidence extractions and edge cases for human review rather than silently guessing — automation handles the volume, people handle the judgment calls.

Why It Mattered

This wasn't a back-office paperwork project — it was infrastructure for operational tempo. Every requisition sitting in a manual processing queue is a part that isn't moving. Every personnel transfer form waiting on data entry is a service member whose records aren't current. Every maintenance form lagging behind the actual work is a gap in the documentation that readiness reporting depends on. Reducing the time and manual effort spent turning paper into data gave the Air Force logistics workflow it was applied to more throughput and fewer opportunities for transcription error, without asking anyone to change how the source documents were generated in the first place.

How We Approached It

We didn't start by picking a model. We started by mapping the actual document workflow — what forms existed, where they came from, where the manual bottlenecks actually sat, and what “correct” looked like to the people receiving the structured output on the other end. The AI is only as useful as its fit to that real workflow, so the build followed the diagnosis, not the other way around: understand where documents entered the process and where they needed to land, then design classification, extraction, and routing to fit that path — with human review built in at the points where a wrong answer would actually cost something.