What are LLMs and how can you apply them to your business using your own data?

The adoption of Large Language Models (LLMs) is advancing significantly, driven by cutting-edge models such as LLaMA 3 (Meta), Claude 3 (Anthropic), Mixtral (Mistral), and the constant updates from OpenAI. These technologies are reshaping how organizations approach natural language processing, task automation, and data analysis.

Data from July 10, 2025, 5-minute read. By: Skyone

The adoption of Large Language Models (LLMs) is advancing significantly, driven by cutting-edge models such as LLaMA 3 (Meta) , Claude 3 (Anthropic) , Mixtral (Mistral) , and the constant updates from OpenAI . These technologies are reshaping how organizations approach natural language processing, task automation, and data analysis.

In parallel, there is growing interest in private LLMs , which aim to guarantee confidentiality, compliance, and control over the data used in these models. In this article, we will explain what LLMs are, their applications in the corporate context, and how solutions like Skyone Studio enable the secure and strategic use of these technologies.

What are LLMs (Large Language Models)?

LLMs are AI models trained on massive volumes of text. From this base, they learn to identify patterns in human language and to generate coherent content, answer questions, summarize, translate, and even program.

Technical basis: LLMs operate based on tokens , minimal units of language that represent words or parts of words. Behind these models are architectures such as Transformer , responsible for significant advances in contextual understanding capabilities.

A typical paragraph consumes about 100 tokens; an article with 1,500 words, approximately 2,000 tokens.

The performance of an LLM depends on factors such as:

Volume and diversity of the training corpus
Parameterization capability (number of parameters )
Inference efficiency (time and cost to generate answers)
Fine-tuning and RLHF (Reinforcement Learning with Human Feedback)
techniques

Why are LLMs in the spotlight?

In recent years, we have seen three trends converge to drive LLMs:

Technological advancements in foundational models:
The opening of models like LLaMA, Falcon, Mistral, and Gemini allowed for customization and use in private environments. At the same time, closed models like GPT-4 and Claude evolved in reasoning, memory, and security.
Growth of Generative AI in Enterprises:
Companies are adopting LLMs for customer service automation, sales co-pilots, document analysis, content generation, and technical support.
Concerns about privacy and data sovereignty
are leading to the emergence of private LLMs , implemented locally or in controlled cloud environments, ensuring that sensitive corporate data is not exposed in public models.

Private LLMs and their challenges

Private LLMs allow companies to leverage the capabilities of generative models with internal data while maintaining confidentiality. However, their adoption requires:

Organization and structuring of data (in data lakes, lakehouses, or data warehouses)
Layers of security and access control
Integration infrastructure with legacy systems
Ability to monitor and audit the outputs generated

It's an ecosystem that goes beyond the model itself; it requires a solid database, interoperability between systems, and integration with operations.

How to implement LLMs safely and scalably: the Skyone Studio case study

Skyone Studio is a complete product that enables the application of enterprise AI agents based on a robust, secure, and integrated architecture.

Main components:

iPaaS (Integration Platform as a Service) : enables the integration of over 400 systems and APIs with low code usage, creating automated workflows to connect CRMs, ERPs, legacy platforms, and cloud systems.
Lakehouse : a modern data structure that combines the scalability of data lakes with the reliability of data warehouses, prepared to support advanced analytics and LLM inference.
AI Agents : creation of multiple intelligent agents with support for LLMs, real-time inference, and integration with channels such as WhatsApp, Google Chat, and interactive dashboards.

Skyone Studio AI agent's ability to automate integrations is directly driven by LLMs. The LLM is the engine that allows the Studio to understand integration needs, translate requests into natural language, and execute the necessary actions to connect the systems. The Studio's "no-code" proposition is amplified precisely by this intelligence: the model understands what needs to be done and automates the process in a contextual and secure way.

Data Publishing and Conversation : generating insights and visualizations that can be activated by natural commands in conversational interfaces.

Use cases:

Companies like Panasonic and Pague Menos use Skyone Studio to reduce operational costs, accelerate decision-making, and automate high-volume processes.
One example of the impact: a 40% reduction in data processing time , with the proper structuring of the information base for use in generative AI.

Conclusion

LLMs are undoubtedly one of the main drivers of current digital transformation. But for their corporate use to be successful, more than just adopting a language model is needed: it's necessary to build an ecosystem of data, integration, and governance .

Solutions like Skyone Studio deliver this foundation: system integration, data lakehouse, intelligent automation, and full support for creating LLM-based agents.

Companies that structure this environment now will be ready to lead the next generation of artificial intelligence in business.