Mistral / Mixtral, Gemma (Google), Qwen, DeepSeek, Claude (Anthropic).
LLM fine-tuning services
We provide comprehensive LLM fine-tuning services to enable businesses to derive greater benefits from their generative AI solutions. Customization of pre-trained LLMs is done via your own proprietary datasets and business-specific industry expertise and operations. Our experts take care of all the necessary technical processes related to fine-tuning and deployment of the AI solution that meets your requirements.
In case you want to develop an intelligent assistant or try fine-tuning LLM services, we will help you strike the balance between model efficiency, infrastructure expenses, and data security.
30+
years on the market
1000+
completed projects
180+
cloud experts
20+
long-term clients
800+
IT experts on board
commitment to our work
Benefits of LLM fine-tuning
Domain-specific accuracy & reduced hallucinations
Training on your own databases, documentation, and customer service ensures domain understanding.
Drastic cost reduction
Big base models have high inference cost per token if fed a large context window or multi-step prompts. The concept of fine-tuning allows quick adaptation so that small but efficient models will outperform huge frontier models.
Data sovereignty & compliance
Use our fine-tuned models in your private cloud or local infrastructure to take advantage of the benefits while ensuring that all your IP and corporate data remains in your domain and is not used to train third parties.
Strategic advantages for enterprise AI
Although traditional foundations yield generalized performance, tailored systems are created specifically to cater to your specific needs. Customization brings about an improved link between the out-of-the-box AI system and the expertise in your field, allowing for better accuracy and control over the results.
Our LLM fine-tuning services
We study your goals, technical environment, dataset preparation, evaluation metrics, and performance requirements in order to find out which solution – fine-tuning, Retrieval-Augmented Generation (RAG), or both combined – would be right for you.
Dataset curation, cleaning & annotation
Data of good quality is the key factor for successful fine-tuning. Extraction, deduplication, and formatting of raw data from your company into instruction-following, dialogue, and task-oriented datasets with full privacy masking.
Model fine-tuning & hyperparameter optimization
With the help of SFT, our team will tune pre-trained models to fit your requirements. In addition, systematic hyperparameter optimization (learning rate schedules, batch size, and weight decay) is applied for maximum efficiency.
Human feedback & alignment (RLHF / DPO)
In order to ensure safety, compliance, and alignment with the tasks, we utilize Alignment techniques like DPO or RLHF in order to clean up the predictions of the model and get rid of any unwanted actions or biases.
Enterprise integration & MLOps deployment
Your machine learning model integrates effortlessly into any business setting, from enterprise software applications and ERP systems to CRMs and even APIs. Our company builds MLOps pipelines that enable us to track model performance, automate model retraining, and manage model versions.
End-to-end solutions for custom model optimization
We will integrate your model that is already optimized into the current corporate environment, including enterprise software solutions, ERPs, CRMs, and API gateways. We will create ML operations pipelines to ensure proper customization and continuous model monitoring and retraining.
Our LLM fine-tuning capabilities
Task-specific model adaptation
Modifying models for specialized applications such as code generation, legal document analysis, medical diagnosis assistance, customer support routing, and financial prediction.
Multi-modal fine-tuning
Going beyond text data to train the model and then applying it to text, image, audio, and video content.
Quantization & model compression
Employing the methods of 4-bit and 8-bit quantization (GPTQ, AWQ), or others, to perform compression of fine-tuned models for deploying them on resource-scarce edge devices.
Retrieval-augmented fine-tuning (RAFT)
In addition to using the strength of fine-tuning and RAG pipelines together, training the model on how to ingest and reference the retrieved domain documentation.
Advanced technical expertise for complex enterprise needs
Our engineering group utilizes state-of-the-art machine learning technology along with extensive software architectural experience to solve demanding technical needs.
Ready to put your LLM fine-tuning plans into practice?
Tell us about your AI use case, training data, or model performance challenges. SaM Solutions will help you select the right foundation model, define a fine-tuning strategy, and implement a tailored LLM solution that meets your business needs.
LLM fine-tuning techniques we use
Supervised fine-tuning (SFT)
Acquisition of elementary domain knowledge and task skills through the analysis of prompts and responses.
LoRA (low-rank adaptation)
Parameter-efficient approach by freezing the base model parameters while adding trainable rank-decomposition matrices, resulting in a significantly smaller GPU memory footprint during training.
QLoRA (quantized LoRA)
Quantization along with the LoRA technique to perform fine-tuning for performance at lower costs while retaining the integrity of the model.
DPO (direct preference optimization)
Streamlined approach to alignment, which allows for parameter optimization based on preference datasets without needing complicated reward models.
RLHF (reinforcement learning from human feedback)
Aligning outputs of the AI model with intricate human values, safety criteria, and quality metrics.
PEFT (parameter-efficient fine-tuning)
Choosing from options like layer freezing, prefix tuning, and adapters to speed up the process of convergence and decrease computational complexity.
Technologies we use
Base models
Fine-tuning frameworks
Data preparation & MLOps
Model serving & inference
Cloud & hardware infrastructure
Why choose SaM Solutions
SaM Solutions is an IT company that has more than 30 years of experience in the software development field. Thus, our approach to Generative AI engineering is:
Deep software engineering heritage
Unlike small-scale independent companies focusing on AI, we know how to incorporate ML algorithms into existing software solutions (legacy software, clouds, and enterprise platforms).
Data security
Being a proven LLM fine-tuning company, we apply security regulations (GDPR, HIPAA, SOC 2) to ensure compliance with regulations.
Cost-aware AI engineering
Computational efficiency matters to us. Using the size of the model and quantization, we reduce your cloud compute cost while maintaining high quality.
End-to-end MLOps capabilities
Right from ingestion of data sets to deployment pipelines, automation and real-time performance monitoring, we handle every aspect of your AI solution.
Proven enterprise delivery
Backed by major multinational companies around the world, SaM Solutions provides production-grade software solutions for sustained success.
Proven engineering excellence and enterprise rigor
With more than three decades of experience in software development, SaM Solutions comes up with mature delivery frameworks for all of your machine learning endeavors. We specialize in creating highly private, secure, and economical AI systems that belong solely to you.
Unlock the full potential of LLM fine-tuning
Discover how our LLM fine-tuning services can improve model accuracy, adapt AI
to your business needs, and streamline operations with tailored language models.
Industries we support
Case studies
LLM-powered content translation for Umbraco CMS
To improve knowledge accessibility across international teams, SaM Solutions built an internal translation system for 1300+ news and article entries stored in Umbraco.
- 1300+ internal articles auto-translated from Russian to English
- 92% reduction in manual translation effort
- Average processing time: 30 seconds per article
- Secure on-premise deployment using the Gemma model
Context-aware chatbot for internal request automation
Pre-release AI chatbot designed to process internal service requests and deliver relevant information on demand.
- Built-in support for natural language queries and smart ticket creation
- Powered by a custom Model Context Protocol (MCP) for real-time data access
- Accelerates internal request handling and self-service efficiency
Airline scheduling system
An AI chatbot developed to simplify error handling in a regulated scheduling environment. By leveraging RAG and regulatory documents, it explains errors in detail and suggests resolutions for non-technical users. Integrated tools can also fetch additional context, such as flight delays, when needed.
- AI chatbot integrated with the scheduling system
- Contextual answers grounded in regulatory documentation
- Built with RAG and tool support for richer insights
Do you want to enhance your business results?
Multilingual AI translator
AI translation system built with modern LLMs (Gemma, Llama, Mistral, Phi, Qwen). Beyond translation, it delivers synonyms, keyword suggestions, explanations, and quick comments to support both understanding and content adaptation.
- High-quality translations with keyword optimization
- Synonym generation and contextual explanations
- Fast comments and clarifications for users
Multimodal AI Assistant
Local AI assistant designed without reliance on paid APIs. It delivers answers, generates visuals, and provides real-time information in one flexible solution.
- Local deployment with no dependency on paid APIs
- Image generation via ComfyUI integration
- Real-time info retrieval (weather, web search)
FAQ
What is LLM fine-tuning?
LLM fine-tuning is the specialized engineering process of taking a foundational Large Language Model — such as Llama 3 or Mistral — and further training it on a curated dataset specific to your business. While broad pre-training equips generic base models with a vast array of general knowledge, fine-tuning adapts their underlying weights to speak your company’s internal language. Through this targeted training, the model masters niche terminology, strictly adheres to required operational workflows, and consistently reflects your brand’s unique voice and formatting expectations with high precision.
What are the applications of fine-tuning the model?
What kind of data is needed to fine-tune an LLM?
How do you ensure data security during LLM fine-tuning?
Contact us
Prefer a more personal approach? Email us — we’ll get back to you shortly. Share your ideas or requirements, and we’ll help you refine them.
What happens next?
1
Shortly after receiving your request, one of our experts will contact you to discuss and clarify your business needs.
2
If needed, we’ll sign an NDA to ensure maximum confidentiality.
3
Your dedicated Account Manager will prepare a detailed project proposal, which may cover cost estimates, timelines, team CVs, and other relevant details.
4
Once approved, your project team can begin work within ten business days.