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Custom LLM fine-tuning that solves catastrophic forgetting and model drift in production

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.

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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

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Challenges Xenoss eliminates with custom LLM fine-tuning

 

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

Build custom LLM fine-tuning solutions from scratch or enhance your existing models

What we engineer for enterprise use cases

Custom LLM fine-tuning
Custom RAG pipelines

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.

Integration with any enterprise stack

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.

Semantic search with source grounding

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.

Fast, production-ready delivery

Custom data preprocessing and tokenization pipelines

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.

Low-code agent orchestration

Model versioning and experiment tracking platforms

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.

Observability & usage analytics

Inference optimization and deployment systems

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.

Multi-LLM flexibility

Continuous learning and model monitoring

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.

Human-AI collaboration by design

Domain-specific architecture customization

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

2 hours

Tech assessment

2-3 days

Discovery phase

1 week

Proof of concept

8-12 weeks

MVP in production

2-3 months

Deploy domain-specific LLMs without catastrophic forgetting or $300k+ GPU clusters.

Reduce training costs by 90% with PEFT methods, preserve foundational knowledge with custom regularization, and achieve production deployment in 8-12 weeks.

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Tech stack for LLM fine-tuning

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.

Built distributed training infrastructure that reduces GPU costs by 90%

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.

Developed enterprise-grade MLOps for LLM lifecycle management

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.

Engineered domain-specific tokenization and data preprocessing pipelines

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.

Mastered inference optimization for real-time enterprise applications

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.

Specialized in compliance-ready LLM training for regulated industries

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.

Delivered production LLM systems that process millions of enterprise documents daily

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.

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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%.

stars

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

Oli Marlow Thomas,

CEO and founder, AdLib

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