Big language models like GPT-4 have already become a powerful tool for business. But working through public APIs is always a risk: data is outsourced, flexibility is limited, and costs can quickly escalate.
But there is a solution — build your LLM model from scratch. This gives you complete control, security, and customization for your needs. In this guide, we’ll show you exactly how to do it, without water and complicated words.
What is a Private LLM?
A private LLM (Large Language Model) is an artificial intelligence-based system that a company deploys and uses inside its infrastructure: on its servers or in a private cloud. Such models are used in chatbots, search, feedback analysis, and other tasks involving natural language interaction.
Unlike public solutions like ChatGPT, Google Gemini, or Claude, this model only runs for your business and does not share data with external services. This is especially important if you work with personal, commercially sensitive, or highly regulated data — for example, in the financial, medical, or legal sectors.
The main advantage of a private LLM is full control over the data, security, and logic of the model. You can customize the system to your industry, retrofit it on internal documents, and build it into your products — from chatbots to analytics platforms.
Where are Private LLMs Applied?
Private language models are more and more common in industries where security, accuracy, and data control are particularly important:
Financial Technology (Fintech)
Private LLMs are used to process applications, analyze transactions, generate financial analytics, and support customers in chat rooms. Such models allow for secure processing of personal and payment data while complying with regulatory requirements (e.g., GDPR, PCI DSS).
Medicine and Health Care
In this area, LLMs help physicians and staff quickly analyze medical records, generate reports, verify appointments, and even predict risks. All while keeping all data in a closed loop, critical for compliance with HIPAA and other medical standards.
Internal Corporate Chatbots and Assistants
The best part of LLMs is that you can train a private language model on your company’s internal docs, guidelines, and knowledge base. A smart assistant that gives clear, personalized answers to your team can help get things done faster and take pressure off your support staff.
When Does a Business Need Its LLM?
Sometimes companies create their language model not because it is fashionable, but because there is no other way. They have to comply with laws, protect data, and take into account the specifics of the business. That’s why it can be really important.
To Comply With Regulatory Requirements (GDPR, HIPAA, etc.)
Companies that handle personal data are required to comply strictly with data privacy regulations. The use of public LLMs (such as ChatGPT or other cloud APIs) may violate GDPR, HIPAA, and other laws if data is transferred to external servers.
Protection of Intellectual Property and Inside Information
If your company works with know-how, patent documentation, strategic plans, or R&D data, any leaks can cause serious damage. Dealing with a public model that logs or can use your data for further learning is a risk.
Working with Local or Weakly Structured Data
Many companies keep unique internal knowledge bases, from technical documentation to corporate guidelines. To effectively use them in AI, the model needs to be further trained or customized to the company’s specifics. Public models do not allow for this. A proprietary LLM can be trained on your data, including local files, knowledge bases, tickets, CRM, and more.
Support for Highly Specialized or Non-Standard Tasks
Off-the-shelf LLMs are good at handling general issues, but often not tailored to the terminology and structure of specific industries — be it law, construction, oil and gas, or pharmaceuticals.
Choosing the Right Approach: Build an LLM from Scratch or Use a Proprietary Model?
When a business decides to create its own LLM, the next step is to choose the right model. There are two main directions: use open-source solutions (open-source models that can be customized), or choose a proprietary model — an off-the-shelf system from a large technology company, such as OpenAI, Anthropic, or Google.
Both options can form the basis of a private LLM, but they differ greatly in the degree of control, cost, customization options, and infrastructure requirements. Below, we’ll look at the differences between them and how to choose an approach depending on the business objectives.
Popular Open-Source Frameworks
Here are the most actively developed and used open-source models:
- LLaMA (from Meta): a powerful and compact architecture that is well-suited for fine-tuning in private environments. LLaMA 2 is limitedly licensed, while LLaMA 3 is already open source.
- Mistral: fast and efficient models with high accuracy on a small number of parameters (e.g., 7B). They work especially well in generation and dialogue tasks.
- Falcon (from TII): a family of models focused on performance and energy efficiency, suitable for deployment in enterprise environments.
- GPT-NeoX / GPT-J / GPT-2 / GPT-3-like: community-developed models with full openness and deep customization.
Comparison of Approaches: Open-Source vs. Proprietary
To choose the right path for private LLM implementation, there is value in understanding how open-source and proprietary models differ in key ways, from flexibility and cost to security and compliance. Below is a visual comparison of the two approaches:
| Criteria | Open-Source LLM | Proprietary LLM (GPT-4, Claude, Gemini, etc.) |
| Flexibility | Extremely high — model architecture can be modified and fine-tuned | Limited — API does not allow changes to internal logic |
| Data Control | Full control: data never leaves the infrastructure | Data is processed on the provider’s side |
| Costs | High initial costs (hardware, training, maintenance), but more cost-effective at scale | Low entry cost, pay-as-you-go or subscription-based |
| Security | Maximum when deployed locally | Requires trust in the external provider |
| Updates & Maintenance | Requires an in-house team or a technical partner | Handled by the provider — updates, security, and support included |
| Regulatory Compliance | Easier to ensure compliance (e.g., GDPR, HIPAA, NDA, etc.) | Harder to fully comply due to external data transfer |
Key Steps to Build a Private LLM: From Data to Learning Model
Building your own language model takes both a clear strategy and a step-by-step approach. It all starts with getting your data in order, choosing the right infrastructure, and then training the model so it actually understands and solves real business challenges.
Dataset Preparation
The first step is working with data. For the model to really understand the specifics of your business, it must learn from high-quality and clean material. This means that all documents, texts, and other sources must first be brought to a standardized format, eliminating duplicates and unnecessary information.
The data is then partitioned and transformed into a structure that the model can understand. If there is insufficient information, additional options are created, for example, through paraphrasing or automatic translation. All of this is done to ensure that the artificial intelligence “speaks” your language and understands the industry context.
The data is then divided into training, test, and validation data, so that the model doesn’t just memorize, but learns.
Setting up the Infrastructure
Training large language models requires powerful computing resources: modern graphics cards, cloud platforms, or in-house servers.
The option is selected depending on the level of security and availability requirements. If the data is particularly sensitive, for example, medical or legal data, the model can be trained and run inside a closed perimeter, without Internet access.
It is also important to set up a control system in advance — monitoring, logs, and backups, so that everything works in a stable and transparent way.
Model Training and Validation
The third step is the actual training and validation of the model. This process requires fine-tuning and constant quality control. Specialists select optimal parameters so that the model learns faster and does not lose accuracy.
At the same time, they evaluate how well it copes with the tasks at hand: how it responds, how meaningfully it constructs texts, and whether it makes mistakes. At this stage, it is important to stop training in time if the model has reached the desired level, in order to avoid “overtraining”.
Fine-Tuning on Internal Data
The final step is making the model truly yours. Even if it’s trained on general data, it won’t be all that helpful until it’s tuned to your company’s specific content — things like internal docs, customer scripts, knowledge bases, and emails.
This helps the model pick up on your tone, your terminology, and how your team actually communicates. You can also use real employee feedback to teach it what kind of answers work best.
Deployment and Integration
Once your model is trained and tailored to your business needs, the next big step is rolling it out the right way. How you deploy it plays a huge role in how stable, secure, and scalable the system will be as your usage grows.
Most companies go with cloud platforms like AWS, Google Cloud, or Azure — they make it easy to launch, add users, and push updates without getting bogged down in complex setup.
Integration via API and Business Applications
To enable the model to interact with other digital systems, it is necessary to provide it with accessible and reliable interfaces. The most universal option is REST API. With its help, LLM can be easily integrated into web applications, corporate portals, CRM systems, or chatbots.
If high responsiveness and minimal latency are a priority, gRPC is a better choice, especially when using microservice architectures or embedded in mobile applications.
This integration allows the model’s capabilities to be utilized across all channels and touchpoints with customers or employees, making it a full-fledged part of a company’s digital infrastructure.
SCAND Use Case: Smart Travel Assistant
One of the brightest examples of our practice is the Smart Travel Assistant project developed by the SCAND team. This is a smart mobile application in which a private LLM acts as a personal assistant for travelers: it helps plan routes, book tickets, find interesting places, and form personalized recommendations in real time.
We further trained the model on specialized travel data, integrated it with external services — such as maps, hotel booking platforms, and airline systems — and deployed the solution on cloud infrastructure for high availability and scalability.
This case study demonstrates how a private LLM can become the technology core of a large-scale custom product — reliable, secure, and fully customized for the industry.
Challenges and Considerations
Despite the high value of private LLMs, businesses face several important challenges when implementing them. To make the project successful, these aspects should be taken into account in advance.
High Computing Requirements
Training and deploying language models require significant resources: powerful GPUs, sophisticated architecture, and storage systems. It is important for a company to understand that LLM implementation is not just a simple model load, but a full-fledged infrastructure task that requires either investment in its own servers or the use of a load-optimized cloud.
Legal and Ethical Risks
Working with AI in business is increasingly regulated by law. If you are processing personal, medical, or financial data, it is important to anticipate compliance with standards such as GDPR, HIPAA, and PCI DSS.
Reputational risks should also be considered: the model should be designed to avoid generating discriminatory, misleading, or malicious responses. These issues are solved through restrictions, filters, and clear control over what data the AI is trained on.
Quality of Findings and Interpretability
Even a well-trained model can make mistakes, especially in new or unusual situations. The key challenge is to ensure that its answers are verifiable, its conclusions explainable, and that it communicates the boundaries of its competence to the user. Without this, the LLM may give the illusion of confidence when generating inaccurate or fictitious data.
Why Partner With an LLM Development Company
SCAND develops language models, and working with us brings many advantages to businesses, especially if you plan to implement AI-based solutions.
First of all, you immediately get access to full-cycle specialists: no need to build a team from scratch, rent expensive equipment, and spend months on experiments.
We already have proven approaches to developing and training LLMs for specific business tasks — from training data collection and transformer architecture design to fine-tuning and integration into your IT infrastructure.
Second, it’s risk mitigation. An experienced team can help avoid mistakes related to security, scaling, and regulatory compliance.
In addition, we know how to leverage ready-made developments: SCAND already has working solutions based on generative AI-chatbots for banks, intelligent travel assistants, and legal support systems adapted to the necessary laws and standards.
All of these products are built using natural language processing techniques, making them particularly useful for tasks where it is important to understand and process human language.
Want to implement AI that works for your business? We can help.