Companies need to navigate their data in real time, and although all organizations seem to understand that investments in data and analytics are one of their top priorities, many are struggling to leverage the information they already have in their records. This is largely because much of this information is stored in contracts, emails, documents, and other unstructured records that often go unnoticed.
The good news is that companies today can rely on emerging technologies to respond to new demands. The incorporation of generative AI can provide solutions that leverage these formats and respond to queries in natural language.
For example, until now, when it was necessary to know what profitability a client was generating or what contracts had been signed with them, it was most likely that a dashboard would be accessed to obtain the information, or a report would be displayed in some transactional system. In the best case, the most experienced collaborators would attempt to execute a precise query in SQL to find the answers, as long as that information had been previously systematized. Today, there are different options: document management solutions based on LLM (large language models) combine AI models with traditional databases to answer questions in natural language and manage data with a different approach.
This combination of LLM with traditional data management techniques creates more powerful solutions for document analysis, finding exact information, and accessing responses in natural language. A synergy that provides more robust, secure, and faster data management, significantly improving companies’ ability to handle and leverage information in various business contexts.
Process Optimization
Many companies today have an unprecedented capacity to store information and even process that data. But they are often overwhelmed by it and fail to extract value from it. They accumulate enormous amounts of documents and feel overwhelmed by this abundance. That is why they require solutions that offer a more efficient information management, providing quick and accurate access to relevant data.
Thanks to new LLM tools and document management, employees no longer need to spend much time manually searching through vast collections of documents. As LLMs are designed to understand and process natural language, they can interpret and respond to queries accurately. This is especially useful in situations where specific information is needed quickly, such as in customer service, technical support, and strategic decision-making. In these cases, the speed of response improves customer satisfaction and the internal efficiency of the organization.
On the other hand, faster access to accurate information facilitates more informed decision-making. Executives and managers can use these solutions to obtain reports, analyses, and specific data that support strategic decisions, which reduces the risk of errors and increases the effectiveness of the actions implemented.
Considering the automation of document search and analysis, companies can significantly reduce labor costs associated with these tasks. Moreover, the accuracy and speed in obtaining information reduce costs related to potential errors and rework.
In other words, these solutions provide two fundamental benefits: they increase productivity and reduce operational costs.
Scale and Customization
A software based on language models can easily scale to handle a growing volume of data and queries without compromising performance. This scalability is vital for growing companies that need systems that evolve with their needs, without requiring significant investments in additional infrastructure.
Additionally, these tools can be trained and adjusted to understand specific terminology and unique contexts of different industries. This allows customizing the solution to adapt to the particular needs of each business, whether in the manufacturing, financial sectors, or activities such as legal, accounting, and more.
Key Advantages
The implementation of a document management system that uses LLM provides companies with a significant competitive advantage. The ability to respond quickly to queries and obtain insights from the available information can differentiate a company from its competitors, attracting more customers and strengthening its market position.
In this context, these document management solutions become a strategic investment, offering multiple benefits: efficiency, accuracy, cost reduction, scalability, and significant improvements in decision-making processes.
Among other advantages, these tools ensure:
- Fast indexing and query.
- Efficient storage.
- Data integrity and consistency.
- Data update and maintenance.
- Data security and privacy.
- Capability to handle structured and unstructured data.
- Assisted data loading.
- Advanced query and analysis capabilities.
Where It Applies
Solutions that analyze documents and respond to queries in natural language can be applied in multiple real business cases. Their versatility allows them to be applied in various sectors to improve the efficiency, accuracy, and responsiveness of organizations in different business situations, in a language much closer to people. For example, they are ideal for:
- Customer support: providing detailed instructions and solutions based on manuals and support databases; answering frequently asked questions and offering information about products and services quickly and accurately.
- Legal document analysis: reviewing and analyzing contracts, laws, and regulations, providing summaries and responses to specific queries from legal teams.
- Financial advice: providing recommendations and answers to questions about investments, financial products, and tax regulations.
- Fraud detection assistance: analyzing large volumes of transactions and documents to identify suspicious patterns.
- Talent management: analyzing resumes and candidate profiles to identify the best matches for open positions; providing automatic and precise responses to employee queries about company policies, benefits, and internal procedures.
On the other hand, these solutions can offer support in areas such as health and medicine (for consulting medical information), in education or research and development (for analyzing educational or scientific material), and in marketing and sales (for gaining insights into market trends).
Benefits of a Proprietary Model
The open-source LLM models available today (such as Google’s BERT or Meta’s Llama) allow us to have a proprietary model for this type of solution. Although it does not offer all the capabilities of private models like GPT, it provides important advantages, as it can be trained with the company’s specific data and documents to better adapt to its business. Additionally, they allow greater control over the data, reducing the risk of leaks and facilitating compliance with privacy and data protection regulations.
At the same time, a proprietary model can be optimized for the company’s specific infrastructure and technological resources. Moreover, it ensures a reduction in long-term costs: although the initial investment in development and training may be significant, in the long run, it may be more economical than paying licenses or usage fees for third-party models; additionally, organizations can better manage and control their maintenance and update costs.
In short, a proprietary, adapted, and optimized LLM model can provide significant differentials compared to companies using generic or third-party solutions. At Baufest, we believe that a proprietary solution adapted to the right business cases can increase value generation by combining the analysis and interaction capabilities of LLM with the structure and precision of traditional data techniques.
This approach increases productivity and reduces operational costs in document and data management. Furthermore, it leaves organizations in a better position to leverage the vast volumes of information available today in different formats, using them to gain competitiveness for their business.