hello_world

Play

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.

hello_world

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.

Explore

Notebook

Context

Boards

Explore

Notebook

Context

Boards

Explore

Notebook

Context

Boards

//Our vision

▍

▍

annoying_adhoc_question

Play

# 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.

annoying_adhoc_question

# 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.

not_the_paper_kind

Play

# reactive cells
users = sql("select * from users")
churned = users.filter(last_active < cutoff)

# downstream updates automatically
churn_rate = churned.count() / users.count()

Every interface is just a Querio Notebook

Data teams and AI love notebooks, so do we. We built a notebook that covers the pitfalls of jupyter notebooks.

not_the_paper_kind

# reactive cells
users = sql("select * from users")
churned = users.filter(last_active < cutoff)

# downstream updates automatically
churn_rate = churned.count() / users.count()

Every interface is just a Querio Notebook

Data teams and AI love notebooks, so do we. We built a notebook that covers the pitfalls of jupyter notebooks.

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.

for_your_boss

Play

# 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.

for_your_boss

# 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.

Outputs from real analysis

Boards are collections of notebook cells. You choose what to publish and how it should look.

Outputs from real analysis

Boards are collections of notebook cells. You choose what to publish and how it should look.

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 a 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

yes_its_whitelabeled

Play

# 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.

yes_its_whitelabeled

# 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

Beautiful and accurate insights from just a question

Centralized Maintenance

Use the same logic you define anywhere you put Querio

Simple to integrate

Whether it's iFrame, API, or MCP, it's easy to take Querio anywhere.

we_help_you_win

Play

# 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.

we_help_you_win

# 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.

$120K

saved annually on hiring needs

$120K

saved annually on hiring needs

10h

saved per business employee

10h

saved per business employee

3w → 30m

new reporting time

3w → 30m

new reporting time

$200K+

saved annually by replacing Looker and deferring data hires

$200K+

saved annually by replacing Looker and deferring data hires

20x

faster reporting cycles

20x

faster reporting cycles

10h

saved on analysis per week

10h

saved on analysis per week

Hover the cards to see more

Tap the cards to see more

import querio

# load shared logic and context

ctx = querio.context()

# notebooks, agents, and access surfaces

app = querio.workspace(ctx)

We connect directly to your data with industry leading security.

We integrate directly with your existing data stack and give you advanced RBAC controls. We follow best practices for data handling, and guarantee it with 3rd party audits.

Learn more