Developer Setup & API Usage

Python SDK (Notebook)

import querio

# Load shared logic and governed context
ctx = querio.context()

# Ask a question against existing logic
result = querio.ask("How did churn change last quarter?", context=ctx)
result.sql          # Inspect generated SQL
result.table()      # Render results as a table

# Publish analysis as a board
board = querio.board([churn_rate, churned.by("plan"), churned.by("signup_month")])
board.refresh()

# Publish board to multiple surfaces
querio.publish(board, to=["slack", "api", "iframe"])

Reactive Notebooks

Querio notebooks are .py files with reactive cells — downstream cells recompute automatically when upstream data changes, similar to a spreadsheet but for SQL/Python analytics.

# Reactive cells
users = sql("select * from users")
churned = users.filter(last_active < cutoff)
# Downstream updates automatically
churn_rate = churned.count() / users.count()

Key Integration Options

iFrame: Embed any board or Explore surface into internal or customer-facing tools.
REST API: Programmatically query boards or trigger refreshes.
MCP (Model Context Protocol): Connect Querio as a tool/context source for external AI agents.
Slack: Publish board snapshots and receive scheduled refreshes in channels.

Context Layer

Define Skills, Rules, Metrics, and a data Catalog that govern how AI interprets queries — versioned by default and self-healing over time.