What LLMs Are Actually Good For in Production

Every client conversation these days touches on AI. The honest answer is that large language models are excellent at some tasks and a poor fit for others.

They excel at unstructured text tasks: summarization, classification, drafting, and natural-language interfaces over structured data. They're far less reliable for tasks requiring precise, deterministic logic or up-to-the-second accuracy.

Our approach is to treat LLMs as one component in a larger system, paired with retrieval, validation layers, and clear fallback behavior, rather than as a drop-in replacement for well-tested business logic.

Used this way, we've shipped AI features — from support ticket triage to document summarization — that hold up in production, with the guardrails to fail gracefully when the model gets something wrong.