Giving an LLM access to company data does not mean it can answer business questions cheaply or correctly. Before it can produce a reliable answer, the model may need to inspect large schemas, choose among competing data sources, apply company-specific definitions, and reconcile results across systems. That work can drive token usage up quickly, while still producing answers that look technically sound but use the wrong business logic. This paper explains where those costs and accuracy failures originate, what a single business question can really cost, and what it takes to keep an AI data system reliable as your business and workflows grow. It also details how Supper approaches that challenge by resolving business context before the model has to rediscover it.
What’s inside
The hidden work behind a business question
The discovery and interpretation an AI system has to do before it can turn company data into an answer.
Why costs are hard to predict
How the amount of model work can change dramatically depending on the question, the data involved, and the path the system takes to resolve it.
Why technically correct is not always business-correct
How an answer can look sound while using the wrong definitions, assumptions, or context.
What reliability requires over time
The business logic, governance, and ongoing maintenance needed to keep an AI data system useful as the company changes.