Our client is a large company that needs to manage large volumes of data. Over the course of almost 90 years, they have become a leader in the automotive and mobility industry, providing fleet management services to more than 300,000 customers in over 50 countries.
The client faced the challenge of managing massive amounts of internal data distributed across multiple physical and digital locations. With no centralized data retrieval interface, searching for information across storage systems/locations was time- and resource-consuming for the company’s employees. Struggling to locate the information required for their everyday work, employees were under constant stress, which affected their productivity, job satisfaction, and retention.
The client turned to Intellias to develop a platform that could resolve the increasing difficulty of enterprise-scale information search and generally streamline knowledge management flows. The platform was to become a single entry point for data search and management for all of the company’s employees.
In the long term, the solution was expected to provide sufficient scalability to support the company’s growth and regional expansion. At the same time, it needed to be focused on enhancing the employee experience through facilitating internal collaboration, enabling effective onboarding of new employees, and providing access to the internal information base via various communication channels.
To unify and centralize the company’s knowledge management platform, Intellias started by evaluating the potential of large language models. These deep learning algorithms trained on large volumes of data simplify information search through parsing natural language queries and providing intelligent responses.
We examined the potential of LLM as a knowledge management technology, focusing on its ability to enhance information processing and retrieval. Our goal was to create a unified platform that organizes the company’s internal information and facilitates intuitive search across all resources, fostering better contextual understanding and insight generation.
To refine and enhance search and retrieval across the company’s database, we supplemented the LLM with a retrieval-augmented generation (RAG) framework, increasing the precision of the model’s responses to user queries and boosting accuracy and trust. For advanced search and insight generation, the solution supports AI-native search in vector databases of structured and unstructured data, such as text, images, video, audio files, and any combinations thereof.
The platform integrated various information repositories maintained in the company, enabling data discovery from both enterprise collaboration tools and database engines. The implemented LLMs were additionally customized and fine-tuned using artificial intelligence technologies to optimize their performance and enhance the context. To empower the company’s employees to easily access information, the platform supports integration with widely used communication tools.
We are building an all-encompassing platform unifying the diverse data accumulated by our client and facilitating information search and access for employees. The solution introduces a centralized interface for data retrieval, which raises the overall efficiency of data management and contributes to maintaining a high level of employee satisfaction by streamlining multiple business processes. The platform offers:
- Easy and intuitive information search using natural language processing techniques
- Centralized access to all information resources across the company
- Use of communication tools adopted in the company to ensure a smooth transition to the new information management system
- Refined employee onboarding procedures
- Improved effectiveness of internal collaboration
- Increased employee retention as a result of higher employee satisfaction and less stress