  Alignment of Large Language Models (LLMs) remains an unsolved problem. Human
preferences are highly distributed and can be captured at multiple levels of
abstraction, from the individual to diverse populations. Organisational
preferences, represented by standards and principles, are defined to mitigate
reputational risk or meet legislative obligations. In this paper, we present
ABC Align, a novel alignment methodology for LLMs that enables integration of
the standards and preferences of a large media organisation into the LLM
itself. We combine a set of data and methods that build on recent breakthroughs
in synthetic data generation, preference optimisation, and post-training model
quantisation. Our unified approach mitigates bias and improves accuracy, while
preserving reasoning capability, as measured against standard benchmarks.
