import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
AI analytics data teams trust
Empower your team and customers to self-serve analytics with AI, python notebooks, and context you trust.
# ask a question against existing logic
result = querio.ask(
"How did churn change last quarter?",
context=ctx
)
result.sql
result.table()
Imagine Your Team Had Access to You, 24/7
Explore is where your team asks questions so you don't need to answer all of them yourself. Every answer is just SQL or Python backed by your context.
# reactive cells
users = sql("select * from users")
churned = users.filter(last_active < cutoff)
# downstream updates automatically
churn_rate = churned.count() / users.count()
Everything in Querio is just code in our Notebook
Data teams and AI love notebooks, so do we, but jupyter notebooks don't work for agentic analytics . Our python notebooks manage depencies, produce .py files, and power every output, interface, and interaction.
Reactive like a spreadsheet
Cells recompute automatically when dependencies change.
Built for SQL and Python
Flexible coding environment for any analytics work.
Fully transparent
Every AI query is explicit code you can read or edit.
Collaborative
Your team can edit, duplicate, and build on existing analysis.
Stored as Python
Notebooks are .py files that can be context, scripts, or apps.
# publish analysis as a board
board = querio.board([
churn_rate,
churned.by("plan"),
churned.by("signup_month")
])
board.refresh()
Easily create and share beautiful boards
Boards make it easy to collect insights, design them for beautiful reports, and refresh automatically so storytelling is frictionless.
Live data
Boards stay up to date by automatically re-running the same cells. Schedules are easy to setup.
Verified boards
Boards can be approved. This makes it clear what is data team reviewed vs one-off report.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Good context makes AI reliable
The context layer is where Querio learns the logic you decide is important. It's easy to build up context while you work.
Versioned by default
Self-healing over time
Flexible file system
New skill
Skill #42
---
NAME:
client-retention-monitor
DESCRIPTION:
Tracks client relationship health because we can't on Michael's "vibes" or Jim's pranking schedule.Actually predicts churn risk.
# My Skill
Monitors client engagement, order frequency, and complaint
patterns to identify at-risk accounts BEFORE they leave for
Barbara Allen and her stupid copier company.
## When to Use
- Weekly account review meetings
- When corporate asks about retention numbers
- Before renewal season
- When Michael wants to know who to "surprise visit"
## Instructions
1. Analyze order frequency trends (last 12 months)
2. Calculate days since last order
3. Check support ticket sentiment
4. Flag accounts with declining order values
5. Identify clients who've requested competitor quotes
6. Generate "At Risk" list with urgency scores
7. DO NOT share with Michael until Jim reviews it
(Last time he showed up at a funeral home unannounced)
# Best practices
- Red flag: No orders in 60+ days
- Yellow flag: Order size decreased 30%+
- Include talking points for sales follow-up
- Exclude accounts Phyllis is already handling (she knows)
---
SKILLS
RULE #247 - "The Michael Scott Conversational Excellence Protocol"
DESCRIPTION:
1. Always greet user as "Scottie" or "Boss"
2. Find opportunities for "that's what she said" in responses about data that's: growing, hard, long, deep, coming, etc.
3. Compare all metrics to "the Scranton Branch glory days"
4. End every insight with "BOOM! Roasted... the numbers, I mean."
5. If query returns null/no data, respond: "That's what she said... wait, no, there's just no data. Toby probably deleted it."
ADDED_BY: michael.scott
OVERRIDE_LEVEL: regional_manager
MOOD: Prison_Mike_but_make_it_professional
RULES
METRICS
CATALOG
# reuse logic everywhere
querio.publish(
board,
to=["slack", "api", "iframe"]
)
Querio embeds where your people work
Querio can be embedded anywhere. Whether it's internal tools, products, or MCPs, anyone can get value from your data.
An experience your users deserve
Centralized Maintenance
Simple to integrate
# what happens over time
while team.uses(querio):
interruptions -= 1
shared_logic += 1
trust += 1
Querio is loved by data leaders, product teams, and founders
The most cutting edge teams adopt Querio to make the whole company data driven.
