You’ve created your account. Now what? This guide walks you through the platform end to end, entirely in the web app — no code editor, API, or SDK required. By the end you’ll have connected data, created a project, run your first concept, explored the results, and built a dashboard on top of them.
This is the app walkthrough. Once you’re comfortable clicking around and want to learn the Vadalog language itself, continue with Your First Project and the Vadalog Reference.

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Step 1 — Get your bearings

After signing in and completing the “Complete your profile” screen, you land in your workspace. The home screen centers on the AI Assistant, and the left sidebar is how you move around:
Sidebar itemWhere it takes youWhat it’s for
Home/The AI Assistant — ask questions or have it build things for you.
Projects/projectsCreate and open projects (where your work lives).
Data/datasourcesConnect data sources and upload files.
Dashboards/dashboardsBuild visualizations over your project output.
Apps/appsPublished apps built on your dashboards.
Settings/settingsProfile, API keys, models, and preferences.
A Getting Started checklist lives at the bottom of the sidebar and tracks your progress through the first few tasks (connect data, create a project, create a dashboard, and more). It’s a handy companion to this guide.
Prometheux workspace home

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Step 2 — Connect your data

Open Data (/datasources) from the sidebar. This page has two tabs: Sources (database connections) and Files (uploads).
1

Open the connection dialog

Click Add Data Source. The “Add your data source” dialog opens with a searchable, categorized list of connectors — Relational Databases (PostgreSQL, MySQL, SQL Server, Oracle, …), Cloud Services (Snowflake, Databricks, BigQuery, Redshift, …), NoSQL (MongoDB, Neo4j), Vector Stores, and File Formats (CSV, JSON, Parquet, Excel, …).
2

Pick a connector and fill in the details

Select your source type and complete the connection form. Fields vary by type — for example, Snowflake asks for Username, Password, Account URL, Database, and an optional Warehouse; PostgreSQL asks for Host, Port, Database, Username, and Password.
3

No data handy? Use a demo source

On a fresh workspace, expand “Available px-datasources — no setup needed” and pick a read-only Prometheux dataset. It opens the connection form pre-filled — just review and connect, so you can explore the platform before wiring up your own systems.
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Or upload a file

Switch to the Files tab, click Upload, and choose a CSV, Excel, or other supported file. Each uploaded file gets a Connect button that registers it as a data source. (Excel workbooks let you pick which sheets to connect.)
Once connected, your source appears in the Sources list with its record and column (feature) counts. Use Preview to inspect the data in a paginated, searchable table, and Run to use it inside a project.
Add data source dialog

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Step 3 — Create a project

A project is your workspace for turning data into answers — it holds your concepts, ontology, and dashboards. Open Projects (/projects). On a new account you’ll see “It looks like you don’t have any projects yet.”
1

Start a new project

Click New Project (top-right). In the dialog, stay on the Create tab and fill in:
  • Name — e.g. Company Ownership
  • Description (optional)
  • Context (optional) — a few notes about the project’s goal; the AI Assistant reads this on every turn.
  • Scope — leave as User Scope.
2

Create it

Click Create. You’re taken straight into the project editor, opened on the Lineage view.
In a hurry? If a Templates tab is available, hover a template and click Use → to spin up a ready-made project (ontology, concepts, and sample data included) that you can explore and adapt.
Create a new project

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Step 4 — Define your first concept

Inside the project editor, the Explorer panel on the left lists your concepts — the logic that transforms data into results.
1

Add a concept

Click New cell in the Explorer header. A new concept cell appears.
2

Choose how to express it

Pick a concept type: Logic (Vadalog, the default), SQL, Cypher, or Python. Write your logic in the editor.
3

Let the assistant write it for you (optional)

Type a plain-English description of what you want, then click the ✨ Generate Vadalog from natural language button. Prometheux drafts the Vadalog for you; edit it as needed.
4

Save

Save with the Save cell button or ⌘S / Ctrl+S. An unsaved cell shows a Modified badge.
New to Vadalog? The Your First Project tutorial teaches the language step by step, and the Vadalog Reference is the full guide.
Defining a concept

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Step 5 — Run it and explore the results

With a concept saved, run it from the cell toolbar:
  • Run cell executes just the current concept.
  • Run workflow runs the concept together with everything it depends on.
Press Shift+Enter as a shortcut. While it runs, the button becomes a Stop control and the results panel shows “Running query…”. When it finishes, the Output tab opens automatically with your results. In the Output table you can:
  • Page through rows (Prev / Next, with a Rows size selector).
  • Search across columns and apply per-column filters (contains, equals, <, >, …).
Switch to the Lineage view (open the Explorer rail) to see your concepts as a dependency graph — click any node to jump into its editor.
Exploring concept results and lineage

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Step 6 — Build a dashboard

Now turn your results into something shareable.
1

Create a dashboard

Open Dashboards (/dashboards) and click New Dashboard.
2

Point it at your project

In the config panel, choose your Project and an Input concept (drives run) — the concept whose run produces the data your widgets display. Set a Title and any header details.
3

Lay out pages, sections, and widgets

Click Add page for a tab, Add section within it (choose Single, Two columns, or Four columns), then Add widget. Pick a widget type — Table, Line chart, Pie chart, or Progress bar — and select the Concept it visualizes. For charts, map the X axis / Y axis (or label / value) columns.
4

Save and run

Click Save, then open the dashboard and click RUN. Prometheux executes the input concept and populates every widget with live data.
Building a dashboard

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What’s next

You’ve gone from an empty account to a running project and a live dashboard. From here:

Learn Vadalog

Write your own reasoning rules with the step-by-step language tutorial.

Connect more data

Wire up Snowflake, Databricks, SQL databases, and files.

Use the AI Assistant

Have the assistant generate and debug concepts for you.

Automate & integrate

Drive the platform from the REST API, Python SDK, or MCP.