AI assistant for business document automation

The client faced inefficiencies caused by manual processing of business documents across the entire process chain. Employees were retyping data from received inquiries, orders, delivery notes, and invoices into different systems, which was time-consuming and error-prone. At the same time, the company struggled to effectively use knowledge stored in internal documents and guidelines. These processes led to delays in customer response, errors in records, high administrative costs, and lost business opportunities. Automation was key to improving speed, accuracy, and competitiveness.

An AI solution was developed to automate business processes from document intake to electronic receipt. The system automatically reads and analyzes incoming business documents, extracts product data, and matches it with items in the company’s database. Based on the extracted information, it automatically generates orders and delivery notes, which are then forwarded to production and sent to electronic goods receipt. The solution eliminates manual product searches, automates item matching with the database, and ensures smooth data transfer between business documents, production, and warehouse. It significantly accelerates the entire process from request intake to goods receipt.

Sales department, purchasing/product specialists, production planners, warehouse/logistics staff, system administrators.

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Nette Framework, PHP, JavaScript/TypeScript, Python, OpenAI API, Tesseract OCR, MySQL/PostgreSQL, REST API, JSON/XML parsers, PyPDF2/PDFPlumber, Docker.

Head of Innovation / Digital Transformation

Joint implementation with the client

Shared management

Process specialists, logistics specialists, end users.

Server capacity for AI models and Nette applications; Database server (MySQL/PostgreSQL); Integration with existing ERP and production systems; Secure storage for documents and archiving; API services for AI models; Licensing fees for OCR technologies; Data backup and archiving; Monthly monitoring and AI model maintenance; Technical support for critical issues; Model retraining when document types change; Training for new users during expansion.

The solution significantly improved the quality and consistency of business processes by eliminating errors from manual document transcription and standardizing order processing across teams. Decision-making accelerated thanks to immediate access to precise order data and better traceability across the entire chain from inquiry to delivery. Communication between departments was automated, reducing calls about order status and improving response times to customer inquiries. Employees gained more time for value-added tasks instead of repetitive administration, stress from potential errors decreased, and overall user experience improved with the intuitive automated system.

Faster processes, improved production and capacity planning.

The greatest benefit was saving employees’ time, who could now focus on strategic tasks instead of document transcription. Automatic product matching eliminated most errors and accelerated the entire process from order to production.

A phased implementation with continuous testing ensured timely issue detection. Active involvement of end users in testing and their constructive feedback significantly contributed to successful adoption. Flexible project management with quick adjustments to client needs and stable technical integration with existing ERP systems formed the foundation for success.

The main challenge was the variety of incoming document formats from different customers and suppliers, which was solved by developing robust parsing algorithms and gradually expanding the template database. Complex integration with legacy ERP systems required the creation of custom API connectors.

We recommend starting with a thorough analysis of current processes and identification of key pain points before designing the solution. Invest time in quality preparation and cleaning of training data—it determines the project’s success. Plan phased implementation with pilot operation on a limited document set. Ensure active involvement of end users from the start and provide training. Prepare for integration challenges with existing systems and establish a plan for continuous monitoring and improving AI model accuracy after go-live.