Automatic Packaging Layout Marking
Overview of the Client
Our client works with a large number of consumer products that require regular packaging updates. Each product package must contain correct markings, labels, and product information. These elements often change due to design updates, regulatory requirements, or product modifications.
Preparing packaging layout markings manually was slow and required significant effort from designers and product teams.
Challenge
The customer manages over a dozen products, each needing accurate packaging artwork, including the correct types of markings and labels. These elements must be updated very frequently, sometimes every week. Each update to product data, labeling regulations, or design files requires teams to redo the packaging layouts for those products.
To ensure the accuracy of product data changes and confirm that regulatory requirements were met, specialists previously had to manually compare product data in spreadsheets with regulatory labeling requirements. With hundreds of products to manage, the manual process of verifying product data and ensuring compliance with regulations was very time-consuming and became increasingly inefficient and inconsistent.
Primary Objectives
The main goal of the project was to automate the process of preparing packaging layout markings and reduce the amount of manual work required from product and design teams.
- Automatically extract key product information needed for packaging marking.
- Generate accurate packaging layout markings based on extracted data.
- Reduce the time required to update packaging layouts when product information or labeling requirements change.
- Ensure consistent placement of required markings and product information on packaging layouts.
- Support work with a large number of products and frequent packaging updates.
- Support local LLM models only.
Project Overview
The system we have developed processes product data and related design materials, extracts the information required for packaging, and prepares structured labelling data that can be applied to packaging layouts.
The solution uses LangChain to orchestrate data processing and LLM workflows, while Ollama provides local model inference for extracting and structuring product information. This approach allows teams to prepare packaging markings faster, reduce manual checks, and manage frequent product updates more efficiently.
- Location: Europe
- Sector: Retail / Consumer Goods
- Period: 4 months
Solution
To address the client's challenge, we developed an AI-based system that automates the preparation of packaging layout markings. The solution analyzes product data and related documentation, extracts the key information required for packaging, and prepares structured marking data that can be applied to packaging layouts. By using LangChain for workflow orchestration and Ollama for LLM inference, the system processes product information and helps teams generate packaging markings faster and with fewer manual checks.
Platform Features
- Automated extraction of key product information required for packaging marking.
- AI-assisted preparation of packaging layout markings based on extracted data.
- Processing of product data and design-related materials to identify required labeling elements.
- Consistent generation of marking data that can be applied to packaging layouts.
- Reduced manual verification of product information and labeling requirements.
- Supports handling large product portfolios and frequent packaging updates.
Technology Stack
To build the solution for automated packaging layout marking, we used the following technologies:
LLM Orchestration
- LangChain
Model Inference
- Ollama
Related Cases
- AI
- LLM
- Product Information Management
- PIM
- Procurement
- Enterprise Software
- AI
- LLM
- Automation
Core Team
- AI Engineer: Developed the AI pipeline for extracting key product information and generating packaging layout markings.
- Machine Learning Engineer: Configured and optimized LLM inference using Ollama for reliable data extraction and processing.
- Backend Developer: Implemented the data processing logic and integrated the AI components into the system workflow.
- Solution Architect: Designed the overall architecture and selected the technologies used in the solution.
- QA Engineer: Tested the system to ensure correct extraction of product data and accurate generation of packaging markings.
Impact
The implemented solution automated the process of preparing packaging layout markings and significantly reduced the amount of manual work required from product and design teams. By automatically extracting key product information and generating marking data, the system helped the client update packaging layouts faster and handle frequent product changes more efficiently. The solution also improved consistency in packaging markings and made it easier to manage a large number of products without increasing operational effort.