Shanc vs an LLM with no layer
The model is capable. On its own, it has none of your context.
Point an agent at your warehouse and it guesses what your data means — and the more fluent it sounds, the easier the guesses are to miss. Shanc gives it a layer to reason over, so it works from your real definitions instead of filling gaps.
Side by side
What's different
It reasons over facts, not guesses
An ungrounded agent fills the gaps in your data with plausible inventions. Shanc gives it your real schema, definitions and joins, so the answer comes from what's true rather than what's likely.
More prompting doesn't fix it
Explaining context to the agent every session produces more confident wrong answers, not fewer. Shanc holds the context as a maintained layer, so it's there once and stays right.
Someone checks the query
On its own, nothing catches a hallucinated column or a wrong join — you find out when the number is already in a decision. Shanc validates before the query runs.
It doesn't vanish when the chat ends
An agent's context dies with the session. Shanc's layer is shared and persistent, so what one person's agent knows, everyone's does.