Data Entry Assistant

An AI-powered data entry assistant that reads the flood of documents a logistics operation receives (PDFs, Excel files, scans, orders buried in emails) and turns them into clean, machine-readable data in your transport or ERP system. No more manual keying, no more transcription errors.

The problem: drowning in documents

The logistics industry runs on communication scattered across systems, emails and documents: generated PDFs, Excel files, scanned paperwork, orders tucked inside email threads. For planners, keeping up is labour-intensive, error-prone, and it doesn’t scale as data flows grow.

There’s a clear need for a robust way to help planners accurately extract that data and feed it straight into their own logistics systems.

How Digitrans works

How Digitrans works

The end user simply forwards documents to the data entry assistant by email. Every message is automatically processed into a machine-readable file and delivered to the system of choice (a transport management system or ERP), ready to act on.
Built entirely on open source

Built entirely on open source

PyTorch + semantic AI

Analyses, interprets and classifies documents of any format.

Tesseract OCR & OpenCV

Turns document images into precise, machine-readable text.

Google Translate

Handles multilingual documents out of the box.

Python · Nomad · Docker

Orchestrates the AI models and deploys them at scale.

Built for production with MLOps

Built for production with MLOps

Building the models is only the start. MLOps practices automate the workflows, enable continuous integration, and streamline deployment and monitoring, so the system keeps learning and improving in production rather than going stale.
The impact

The impact

  • More efficiency: automated processing cuts manual data entry and errors
  • Scalability: a microservices architecture scales with growing volumes
  • Continuous improvement: MLOps keeps the models current
  • Better data accuracy: advanced NLP and ML deliver precise recognition and higher data quality

How many documents did your planners type in today?

Share a sample batch. We'll show you the clean data it becomes.