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Enterprise leaders face significant pressure to demonstrate a return on investment from artificial intelligence. Yet, up to 87% of AI initiatives fail to transition from proof-of-concept to successful production implementations [1]. One significant factor that’s not discussed enough? Inadequate evaluation clarity and methodology. Many organisations still rely on informal, ad-hoc assessments – or “spot check […]

In today’s data-driven world, artificial intelligence (AI) is rapidly transforming how organisations operate. AI has revealed its potential to revolutionise industries, from automating repetitive tasks to uncovering actionable insights. However, for AI to deliver on its promise, one critical foundation must be in place: good data. Data is the fuel for AI, enabling algorithms to

This blog post examines how Deepseek’s distillation process, alongside Malted AI’s task-specific approach, highlights the potential for developing efficient, small language models that focus on accuracy, specialised knowledge, and reduced computational overhead, offering a more tailored solution for enterprise AI applications. There has been significant hype around Deepseek building a performant model for only $6M,

This blog post explores how knowledge distillation, combined with synthetic data, enables the development of small, efficient AI models that retain the capabilities of larger ones, addressing data scarcity, reducing resource requirements, and delivering practical and secure solutions for enterprise applications. Access to quality data remains a persistent challenge for organisations striving to build effective,

This blog will explore the key differences between small language models (SLMs) and large language models (LLMs), focusing on how they’re built, their trade-offs in efficiency and resource consumption, the situations where one might be more appropriate than the other and what happens when models are combined. In recent years, advancements in natural language processing