What it is
You start from an already trained model and adjust it with a set of your own examples. The model "learns" your style and applies it without you repeating it in every prompt.
Why it matters to an agency
It helps when you need very consistent output (brand tone, fixed format). For knowledge that changes, RAG is almost always the better choice, since it is cheaper and more flexible.
Example
Fine-tuning a model so it always answers in the voice and report format of your agency.
By the numbers
OpenAI accepts fine-tuning from 10 examples, but a clear improvement shows up with 50 to 100 quality examples. Unlike RAG, the knowledge stays frozen in the model: to update it you have to retrain, while with RAG you just reindex the new document. A typical training dataset weighs less than 10 MB: that is 100 example conversations, not gigabytes.