import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
AI analytics data teams trust
Modern reactive notebooks where data teams work, build context, and empower everyone to use AI for analytics
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
AI analytics data teams trust
Modern reactive notebooks where data teams work, build context, and empower everyone to use AI for analytics
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
AI analytics data teams trust
Modern reactive notebooks where data teams work, build context, and empower everyone to use AI for analytics
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
For AI analyics to work everything should be code
Querio is built around a simple idea: analytics should be treated like software; written in real code with context saved as you work. All work runs as SQL and Python inside reactive notebooks. Useful logic is stored as versioned context. AI agents provide self-serve by using notebooks and the built up context.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
For AI analyics to work everything should be code
Querio is built around a simple idea: analytics should be treated like software; written in real code with context saved as you work. All work runs as SQL and Python inside reactive notebooks. Useful logic is stored as versioned context. AI agents provide self-serve by using notebooks and the built up context.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
For AI analyics to work everything should be code
Querio is built around a simple idea: analytics should be treated like software; written in real code with context saved as you work. All work runs as SQL and Python inside reactive notebooks. Useful logic is stored as versioned context. AI agents provide self-serve by using notebooks and the built up context.
All analysis runs in our in-house reactive notebooks. A tool loved by data teams and AI agents.
Hover
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Imagine Your Team Had Access to You, 24/7
Explore is where your team asks questions you don't need to do yourself. AI agents answer questions using our notebook and the context your team has already defined. Every answer is just SQL or Python written in a notebook.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Imagine Your Team Had Access to You, 24/7
Explore is where your team asks questions you don't need to do yourself. AI agents answer questions using our notebook and the context your team has already defined. Every answer is just SQL or Python written in a notebook.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Imagine Your Team Had Access to You, 24/7
Explore is where your team asks questions you don't need to do yourself. AI agents answer questions using our notebook and the context your team has already defined. Every answer is just SQL or Python written in a notebook.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
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. Querio Notebooks are AI native, have modern reactivity, and are flexible for all analytics work. It powers everything you see.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
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. Querio Notebooks are AI native, have modern reactivity, and are flexible for all analytics work. It powers everything you see.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
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. Querio Notebooks are AI native, have modern reactivity, and are flexible for all analytics work. It powers everything you see.
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.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Easily create and share beautiful boards
After analyzing data in a Notebook or asking questions in Explore, teams can publish results to Boards. Boards make it easy to collect insights, design them for beautiful reports, and they refresh automatically so storytelling is frictionless.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Easily create and share beautiful boards
After analyzing data in a Notebook or asking questions in Explore, teams can publish results to Boards. Boards make it easy to collect insights, design them for beautiful reports, and they refresh automatically so storytelling is frictionless.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Easily create and share beautiful boards
After analyzing data in a Notebook or asking questions in Explore, teams can publish results to Boards. Boards make it easy to collect insights, design them for beautiful reports, and they 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.
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.
Live data
Boards stay up to date by automatically re-running the same cells. Schedules are easy to setup.
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.
Verified boards
Boards can be approved. This makes it clear what is data team reviewed vs a one-off report.
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. This context supports every query, whether written by AI or a human, so you can save time and trust querio in the hands of your team.
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. This context supports every query, whether written by AI or a human, so you can save time and trust querio in the hands of your team.
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. This context supports every query, whether written by AI or a human, so you can save time and trust querio in the hands of your team.
Versioned by default
Every change creates history. You can see what changed, why it changed, and when.
Self-healing over time
When mistakes are fixed, future answers improve instead of resetting.
Flexible file system
Context can take many forms: text, SQL, Python, JSON, .md, or .py.
Hover
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
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
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
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Querio embeds where your people work
Once analytics context is set, Querio can be embedded anywhere. Whether it's internal tools, products, or MCPs, anyone can get value from your data. The same context is used anywhere so you can trust AI answers while you scale access.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Querio embeds where your people work
Once analytics context is set, Querio can be embedded anywhere. Whether it's internal tools, products, or MCPs, anyone can get value from your data. The same context is used anywhere so you can trust AI answers while you scale access.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Querio embeds where your people work
Once analytics context is set, Querio can be embedded anywhere. Whether it's internal tools, products, or MCPs, anyone can get value from your data. The same context is used anywhere so you can trust AI answers while you scale access.
Experience your users deserve
Beautiful and accurate insights from just a question
Beautiful and accurate insights from just a question
Centralized Maintenance
Use the same logic you define anywhere you put Querio
Use the same logic you define anywhere you put Querio
Simple to integrate
Whether iFrame, API, or MCP, it's easy to take Querio anywhere.
Whether iFrame, API, or MCP, it's easy to take Querio anywhere.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Querio is loved by data leaders, product teams, and founders
Teams adopt Querio because it makes the whole company better with data in a safe way. The best teams care about accuracy as much as speed, and want to be cutting edge with how they get there.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Querio is loved by data leaders, product teams, and founders
Teams adopt Querio because it makes the whole company better with data in a safe way. The best teams care about accuracy as much as speed, and want to be cutting edge with how they get there.
import querio
# load shared logic and context
ctx = querio.context()
# notebooks, agents, and access surfaces
app = querio.workspace(ctx)
Querio is loved by data leaders, product teams, and founders
Teams adopt Querio because it makes the whole company better with data in a safe way. The best teams care about accuracy as much as speed, and want to be cutting edge with how they get there.
$120K
saved annually on hiring needs
$120K
saved annually on hiring needs
$120K
saved annually on hiring needs
10h
saved per business employee
10h
saved per business employee
10h
saved per business employee
3w → 30m
new reporting time
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
$200K+
saved annually by replacing Looker and deferring data hires
20x
faster reporting cycles
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
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.
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.
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.
Make everyone a data person
Make everyone a data person