Businesses have to deal with vast volumes of data, especially in digital content, where documents, knowledge assets, and user resources continue to grow at scale. With the rise of data and documents, it becomes problematic to quickly find, understand, and interact with information. Traditional keyword-based search and manual navigation often fall short, leading to slower discovery, poor user experience, and reduced engagement.
One of our clients, a global leader in digital content, was facing a similar problem. However, GenAI with RAG can transform the way users interact with documents. Let’s understand how the DivIniSoft GenAI team helped the client overcome this problem with an RAG-powered solution.
Challenges
The client is a global leader in digital content, managing vast repositories of documents consumed by users across multiple regions and languages. The client had partnered with a third-party translation service. Now, with each document replicated across multiple languages, the size and complexity of the content repository grew exponentially, intensifying search and accessibility challenges.
Here’s the list of problems that the client faced as the content and data repository started scaling, but their technology stack remained the same:
1. With manual searching, users struggled to find relevant information quickly within large,multilingual document repositories.
2. Manual document search reduced user engagement and increased time spent locating content.
3. Third-party translation services introduced high recurring costs and limited scalability.
4. The original document formatting across languages was not maintained properly. The team had to spend extra hours to format the documents as per brand guidelines.
5. The existing technology stack could not efficiently support real-time document interaction.
So, the client reached out to DivIniSoft GenAI experts to get an AI-driven system for efficient searching and real-time document interaction. Also, they required an in-house, scalable translation solution that preserved the original formatting and reduced dependence on third- party translation service providers.
Our Strategy
After analyzing the client’s service, data repository, and challenges, our Gen AI experts recommend a GenAI solution based on RAG to expedite search across documents and support accurate translation of documents in different languages. The team used a lightweight multilingual large language model (LLMs) to translate documents end-to-end while maintaining contextual accuracy, terminology consistency, and the original document structure.
RAG, or Retrieval-Augmented Generation, is an AI framework that enables a GenAI system to index millions of documents using vector embeddings. The relevant content can be retrieved semantically rather than through keyword-based search.
Our Solutions
Here’s how we implemented the GenAI with the RAG solution for the client:
AI-Powered Translation
We built a scalable workflow to translate the multilingual documents efficiently:
1. We converted the documents into HTML/CSS files, and OCR was used to extract the information from images.
2. Used GPT-4o model for end-to-end document translation while maintaining original formatting.
3. Implemented a rolling context window to ensure coherence across pages for easy readability.
4. Used AWS S3 with automated workflows for cloud-optimized storage of all documents and processed outputs.
Conversational Document Interaction
To get the information easily from the documents across the repository, we deployed an intelligent and conversational RAG-powered system.
1. We used the advanced text extraction libraries to process a wide range of documents in the client’s repository to train the GenAI system.
2. A chunking strategy was chosen to segment content into logically meaningful sections.
3. Then, each document chunk was converted into semantic embeddings using text-embedding-3-large embedding model.
4. Vector embeddings were stored in PineCone vector database.
Business Outcomes
Conversational document interaction enabled users to find relevant information faster and saved time by more than 75%
The scalable translation system reduced the dependency on third-party translators and costs by 50%.
Automation helped in streamlining workflows, reducing manual efforts, and expediting time-to-market for new features.
Tech Stack
OpenAI GPT-4o, text-embedding-3-large embedding model, LangChain, PineCone vector database, AWS S3
Conclusion
By implementing a Gen AI–powered document intelligence and translation solution, our GenAI experts helped the client transform how users interact with large-scale digital content. The GenAI system with RAG now supports faster information discovery, accurate multilingual translation, and consistent document experiences across users.
GenAI is an important lever that can streamline content generation, ideation, document analysis, and intelligent search. Let our experts find the areas in your workflow that can highly benefit from GenAI and RAG solutions.