Large Language Model (LLM) Development Malta

We evaluate your LLM use cases, data assets, accuracy requirements, and deployment constraints. This includes assessing data quality, identifying domain-specific terminology, and establishing performance baselines against generic models.

Based on assessment findings, we select foundation models, define fine-tuning strategies, design RAG architectures, and plan evaluation frameworks. We balance performance targets against cost constraints and deployment requirements.

Technical architecture design covers model selection rationale, fine-tuning pipeline specifications, RAG retrieval and chunking strategies, embedding model selection, vector database configuration, and inference infrastructure planning.

We prepare training datasets, execute fine-tuning runs with hyperparameter optimisation, build RAG pipelines with document processing and embedding, and develop prompt libraries tested against your specific use cases.

Comprehensive evaluation using domain-specific test sets, adversarial prompts, factual accuracy benchmarks, and bias assessments. We validate RAG retrieval quality, citation accuracy, and edge case handling before production deployment.

Production deployment with optimised inference pipelines, caching strategies, load balancing, and monitoring. We integrate LLM capabilities into your applications via well-designed APIs with proper authentication and rate limiting.

Ongoing monitoring of model accuracy, response quality, latency, and costs. We implement feedback loops for continuous improvement, manage model updates, and expand capabilities based on emerging use cases and evolving foundation models.