Data, AI and platformBI, AI and ML for the business
Athena: from a question to a query and a notebook
One question in plain language and the work is done: Athena saves the query in the Lab, writes the notebook with the ranking and the conclusion, and explains from the data what happened. Everything stays in the project, can be checked, and is one keystroke away.
Illustrative scenario: it describes a typical case, not a customer project.
A chart answers a question. The real work comes after.
The marketing lead wants to know which campaign to cut. An assistant that draws a chart helps, but then someone has to write the right query, save it, put it in a document with a conclusion and send it to whoever decides.
Athena does that work inside the project: it saves the query where the team will find it, writes the notebook and answers from the data, showing what it ran. Whoever receives the result can open it, check it and change it.
- Who it's for
- Marketing and e-commerce leads, management accounting and anyone with a question about the data; the data team that prepares the sources and datasets Athena starts from.
In Muvia, step by step
Real product screens, recorded on a project with sample data.
The video · One question in plain language and the work is done: Athena saves the query in the Lab, writes the notebook with the ranking and the conclusion, and explains from the data what happened. Everything stays in the project, can be checked, and is one keystroke away.
What you get
- From question to document
- The answer does not stay in a chat: it becomes a saved query and a notebook you can send to whoever decides.
- You see what it did
- Athena shows the query it ran and the objects it created: the answer can be checked, and what is not needed can be undone.
- Explanations from the data
- An incident is explained by reading the project's own series, not with a generic answer.
- Work that stays in the project
- What Athena creates is found with ⌘K, and the team picks it up like any other query or notebook.
For the technical team
How it is built in Muvia
- 1
Prepare sources and datasets
The team connects the sources and saves the campaign balance as a dataset with stable fields. A certified dataset is the one Athena prefers when it chooses where to start.
- 2
Ask for the work, not just the chart
For example: save a query ranking campaigns from best to worst by extra margin, create a notebook with the ranking and a conclusion, tell me which one to cut. Athena saves the query in the Lab and writes the notebook.
- 3
Check and undo
Every object it creates appears as a card in the conversation, with a link to open it and a button to undo it. The query is a query like any other: you read it, edit it and use it in dashboards.
- 4
Have it explain an incident
Asked why orders collapsed one evening while visits were rising, Athena reads checkout errors and conversion every 15 minutes, says when the problem started and ended, and shows the query it ran.
- 5
Find it all with ⌘K
Queries, datasets and notebooks created by Athena are project objects: a keystroke and a word find them, next to the ones the team made. Each AI capability is a module the company chooses to switch on.
- The data it needs
- Daily spend by campaign and channel, from a Google Sheets spreadsheet
- Online store orders and order lines from the PostgreSQL database
- Sessions, conversion and checkout errors every 15 minutes, read from a REST API
- The campaign balance: spend, extra revenue and extra margin, as a project dataset
More cases in this line
All use cases- Data science on company dataYour data scientist trains wherever they like, in their own Jupyter with their own tools. Muvia gives them governed data to start from, runs the model on a schedule next to the data, and puts the scores into dashboards, alarms and reports.
- Data from business systems, checked before useFiles, databases and online services connect from one catalogue, and each becomes a table with no schema to design. Before the data reaches a report, written rules keep the bad rows out and set them aside in quarantine with the reason.
- Tables that update themselvesEvery hour a file of orders arrives from the website, the app and the marketplace. Muvia loads it, cleans it, checks it and updates the business-ready tables in cascade: when someone opens the dashboard, the latest file is already in.
Bring us a question you can't answer today.
We start from a real question your business has and walk the path from source to dashboard with your systems, not a demo dataset.