Frontier models need to do everything for everyone. It's expected (though not often done) that smaller models fine-tuned on specific tasks can approach frontier performance on a specific area. [0]

Post-training/fine-tuning is not trivial and having it as a service might make it more accessible.

[0] https://surgehq.ai/blog/training-on-complexconstraints

If you want an LLM to have knowledge about something, the knowledge has to either exist in its weights or be provided to it in its context. Because context is expensive and limited, and models tend to get dumber the more their context is filled, there is usually more that you'd need to put into context than can reasonably fit in it in order for the model to answer questions about your data. So your options are basically

1.) stuff it into context

2.) figure out a way to determine what to put into context based off of what is being asked of the model (RAG)

3.) change the weights of the model to have knowledge of your data baked into it (fine tuning)