Getting started
AI in GoPie
When you ask GoPie a question, an agent answers it by querying your data — not by recalling facts from training. This page maps what the AI can do and how it stays trustworthy.
How the agent answers a question#
The agent works in a loop you can follow:
- Find the data. A schema search across your datasets — names, column descriptions, sample values — picks the tables that can answer the question.
- Write the SQL. The agent drafts a query against the schemas it found.
- Run and check. It executes the query and inspects the result before answering. If the query fails or the result looks wrong, it revises and tries again.
When a question names a value loosely — "revenue for electronis", say — the agent matches the term against real values in the column and filters on Electronics, the value that actually exists.
Ask the Orders dataset "Which product category brings in the most revenue?" and the answer comes back as prose plus the query that grouped revenue by category — with the executed results attached. The full mental model is in How the SQL agent works.
Where AI shows up#
| Surface | What the AI does |
|---|---|
| Chat with your data | Answers questions with executed SQL, charts and citations |
| SQL editor | Turns a plain-language prompt into a query you can edit and run |
| Dashboard generation | Plans metrics and builds a chart canvas from a goal you describe |
| Dataset upload | Drafts dataset and column descriptions you can review and edit |
| Reports | Suggests analysis questions grounded in your schema and generates a frozen, citable report |
Grounding and transparency#
Every chat answer ships with its evidence:
- Citations — the datasets the agent used.
- The SQL — every query the agent ran, open for inspection in the results panel.
- The results — the actual rows the answer is based on.
If a question can't be answered from the data, the agent says so instead of inventing an answer.
NoteYour data stays in your GoPie deployment. The model sees dataset schemas and the small result samples needed to answer a question — AI data privacy is the full account of what leaves your deployment and what never does.
Limits#
- Long-running questions time out. If a query runs too long, the agent stops and tells you. Narrow the question or filter to a smaller slice of the data.
- Very large results are truncated for the model. The agent reasons over a sample; the full results stay available in the results panel.
- Conversation memory is bounded. Long chats eventually drop their oldest turns. Start a new chat when you change topics.