The appearance of the first chat-LLMs in 2022 marked a new stage in the development of artificial intelligence. However, immediately after the widespread adoption of these models, a legitimate question arose: how secure is it to transfer company-related data and queries to the cloud? Most cloud-based language models stored communication history and uploaded documents on their servers and for companies dealing with sensitive information, this became a serious barrier to AI adoption.
One of the most sustainable and logical solutions to this problem has been the deployment of Open Source LLM locally, within the corporate IT infrastructure. Unlike cloud services, local models work without an Internet connection, do not transfer data to external servers and give companies full control over information
The best open model [Open Source LLM] is currently comparable in performance to closed models [such as ChatGPT, Claude], but with a lag of about one year (TIME, “The Gap Between Open and Closed AI Models Might Be Shrinking. Here’s Why That Matters,” 5 November 2024).
– Ben Cottier, lead researcher at Epoch AI, a nonprofit research organization, 2024
Major technology companies have started to make their LLMs available for local use. Meta’s open source LLaMA series and the rapidly growing DeepSeek project from China were examples of the move to open architecture. Alongside them, Mistral and Falcon have also released powerful models free from the constraints of proprietary platforms. These initiatives have not just accelerated the development of global AI, but have also given privacy-conscious companies real alternatives for independence, flexibility and security compliance.
In a corporate environment, especially in the construction industry, data protection is not just a matter of convenience, but of regulatory compliance. Working with tender documents, estimates, drawings and confidential correspondence requires strict controls. And this is where local LLM provides the necessary assurance that data stays inside the company’s perimeter.
Key Benefits of Local Open Source LLM:
- Complete control over data. All information remains inside the company, which eliminates unauthorized access and data leakage.
- Standalone operation. No dependence on the Internet connection, which is especially important for work in isolated IT infrastructures. This also ensures uninterrupted operation in the face of sanctions or blocked cloud services.
- Application flexibility. The model can be used for text generation, data analysis, program code writing, design support and business process management.
- Adaptation to corporate objectives. LLM can be trained on internal documents, which allows you to take into account the specifics of the company’s work and its industry features. The local LLM can be connected to CRM, ERP or BI platforms, allowing you to automate the analysis of customer requests, report generation or even trend forecasting.
Deploying DeepSeek’s free and open source model -R1-7B on a server, for access by an entire team of users, at a cost of $1000 per month can potentially cost less than annual fees for cloud APIs, such as ChatGPT or Claude and allows companies to take full control of their data, eliminates its transfer to the internet and helps comply with regulatory requirements such as GDPR
In other industries, local LLMs are already changing their approach to automation. In support services, they respond to frequent customer requests, reducing the workload of operators. In HR departments, they analyze resumes and select relevant candidates. In e-commerce, they generate personalized offers without revealing user data.
A similar effect is expected in the construction industry. Thanks to the integration of LLM with project data and standards, it is possible to accelerate the preparation of documentation, automate the preparation of estimates and predictive cost analysis. The use of LLM in conjunction with structured tables and dataframes is becoming a particularly promising area.
