Build domain-specific language models with parameter-efficient techniques, distributed training infrastructure, and continuous learning pipelines that preserve foundational knowledge while adapting to your enterprise data.
Most enterprises struggle with LLM fine-tuning because generic approaches cause catastrophic forgetting, require massive GPU clusters, and create models that degrade over time. Our engineering team implements PEFT methods (LoRA, QLoRA), gradient checkpointing, and custom training loops that reduce computational requirements by 90% while maintaining model performance.
Leaders trusting our AI solutions:
90%
Reduction in computational requirements with PEFT methods
10,000x
Fewer trainable parameters using LoRA vs full fine-tuning
99.8%
Model accuracy retention after parameter-efficient training
Catastrophic forgetting destroying foundational model capabilities
Standard fine-tuning overwrites critical pre-trained knowledge, causing models to lose general language understanding and reasoning abilities. Enterprises invest months in training only to discover their models can no longer perform basic tasks they handled before fine-tuning.
Prohibitive GPU infrastructure costs for full model training
Full fine-tuning requires massive GPU clusters costing $300k+ annually per model. A single A100 costs $27k/year, and most enterprises need 8-16 GPUs minimum. These infrastructure demands make custom LLMs financially unfeasible for most organizations.
Model drift and performance degradation over time
Fine-tuned models become stale as business data evolves. Without continuous learning pipelines, model accuracy degrades by 15-30% within 6 months. Re-training from scratch is too expensive, creating a cycle of declining performance.
Inability to handle domain-specific terminology and context
Generic LLMs struggle with industry jargon, internal processes, and company-specific knowledge. Models hallucinate incorrect information about proprietary systems, products, or procedures, creating compliance risks and operational errors.
Memory limitations preventing training on large enterprise datasets
Enterprise datasets often exceed GPU memory capacity. Standard training methods require storing model weights, gradients, and optimizer states simultaneously, making it impossible to train on comprehensive company data without massive infrastructure.
Lack of version control and experiment tracking for model iterations
Teams lose track of hyperparameter configurations, training data versions, and model performance metrics. Without proper MLOps, enterprises can’t reproduce successful models or understand why certain versions perform better than others.
Data quality and preprocessing bottlenecks
Raw enterprise data requires extensive cleaning, tokenization, and formatting before training. Poor data quality leads to biased models, while manual preprocessing takes months and introduces inconsistencies that affect model performance.
Deployment complexity and inference optimization challenges
Moving fine-tuned models from training to production involves complex containerization, API development, and performance optimization. Models that work in research environments often fail to meet latency requirements in real-world applications.
What we engineer for enterprise use cases
Parameter-efficient fine-tuning (PEFT) implementation
Custom LoRA, QLoRA, and AdaLoRA implementations that reduce trainable parameters by 10,000x while maintaining model performance. Minimize GPU memory requirements and training costs without sacrificing accuracy on domain-specific tasks.
Distributed training infrastructure
Multi-GPU training pipelines with gradient accumulation, mixed precision, and model parallelism. Scale training across clusters with automatic fault tolerance, checkpointing, and dynamic resource allocation for enterprise-grade reliability.
Catastrophic forgetting prevention systems
Advanced regularization techniques including Elastic Weight Consolidation (EWC), rehearsal methods, and knowledge distillation pipelines. Preserve foundational model capabilities while adapting to new domains and tasks.
Domain-specific tokenizers, data cleaning algorithms, and preprocessing workflows that handle enterprise data formats. Transform unstructured company data into training-ready datasets with automated quality validation and bias detection.
MLOps infrastructure with comprehensive experiment logging, hyperparameter tracking, and model artifact management. Compare training runs, reproduce results, and maintain audit trails for compliance and optimization.
Model quantization, pruning, and TensorRT optimization for production deployment. Container orchestration with auto-scaling, load balancing, and sub-100ms inference latency for real-time enterprise applications.
Real-time model performance tracking with drift detection, automated retraining triggers, and incremental learning systems. Maintain model accuracy over time without full retraining cycles or service interruptions.
Custom attention mechanisms, specialized embedding layers, and task-specific model architectures. Optimize model design for industry requirements including financial compliance, healthcare privacy, or legal document analysis.
How to start
Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks
Challenge briefing
Tech assessment
Discovery phase
Proof of concept
MVP in production
Why Xenoss is trusted to build enterprise-grade LLM fine-tuning systems
We solve the hardest technical challenges that prevent enterprises from successfully deploying custom LLMs in production.
Pioneered parameter-efficient fine-tuning methods that eliminate catastrophic forgetting
Developed proprietary regularization techniques that maintain 99.8% of foundational knowledge while adapting to domain-specific tasks. Our EWC-enhanced training prevents the model degradation that destroys $300k+ investments in traditional fine-tuning approaches.
Engineered custom training pipelines that fine-tune 70B parameter models on 4 GPUs instead of 32. Our memory-efficient architectures eliminate the $500k+ hardware requirements that make LLM customization financially impossible for most enterprises.
Solved model drift and performance degradation in production environments
Created incremental learning algorithms that adapt to new data without full retraining cycles. Our drift detection systems automatically trigger model updates, preventing the 30% accuracy loss that kills enterprise LLM projects within 6 months.
Built comprehensive platforms that track hyperparameter configurations, dataset versions, and model artifacts across thousands of training experiments. Eliminates the chaos that causes teams to lose successful configurations and waste months reproducing results.
Created intelligent preprocessing workflows that transform messy enterprise documents into training-ready datasets. Our quality validation algorithms detect bias, inconsistencies, and formatting issues that cause fine-tuned models to fail in production.
Developed TensorRT optimization pipelines that achieve sub-100ms inference on fine-tuned models. Our containerized deployment systems auto-scale to handle enterprise traffic loads while maintaining consistent performance and cost efficiency.
Built GDPR-compliant training workflows with complete data lineage tracking. Our explainable fine-tuning systems provide decision transparency required for regulatory compliance in banking, healthcare, and legal applications.
Deployed fine-tuned models handling real-time document analysis, customer support automation, and regulatory compliance checking. Our systems maintain 99.99% uptime while processing enterprise workloads that smaller providers can’t handle.
Featured projects
Build your own custom, domain-specific LLM without catastrophic forgetting
Talk to our ML engineers about deploying parameter-efficient fine-tuning systems with LoRA implementations, distributed training infrastructure, continuous learning pipelines, and enterprise MLOps integration that preserves foundational knowledge while reducing GPU costs by 90%.
Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. I particularly appreciate their ability to hire extreme fast and to generate great product ideas and improvements.
Oli Marlow Thomas,
CEO and founder, AdLib
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