diff --git a/md-docs/user_guide/project.md b/md-docs/user_guide/project.md
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+# Project
+
+A Project is the secondary organizational entity in ML cube Platform.
+A Project groups together a set of artificial intelligence algorithms that share a common goal measured by a set of KPIs.
+For this reason, it is composed of several [Tasks] that collaborate to reach the Project's goal.
+
+Users in the [Company] can access to one or more Projects according to their [roles].
+
+## Creation
+
+When a Project is created, the [User] specifies its *name*, *description*, and selects the *default storage policy*.
+
+*Storage Policy* defines the default behavior the Platform follows to access data that are shared with it.
+Indeed, data shared with ML cube Platform can either be duplicated and stored in ML cube private cloud storage or stay only on customer's cloud and accessed as a remote data source.
+
+## Demo Projects
+
+To better explore ML cube Platform modules and features, it is possible to create *Demo Projects* that are not taken into account by subscription quotas.
+ML cube Platform provides different Demo Projects that cover all the possible use cases (regression, classification, text data, image data, RAG, object detection and so on).
+To create a Demo Project, you need to check the "Demo Project" checkbox and select the one you prefer.
+
+## KPI Monitoring
+
+A Key Performance Indicator is a measure of performance over time for a specific objective. 
+While artificial intelligence algorithms try to minimize their loss function, artificial intelligence based solutions and applications look at KPIs.
+Therefore, it is essential to monitor Project's KPIs along with algorithm performance to have a complete view of the current situation.
+
+ML cube Platform offers the possibility to upload Project's KPIs to monitor them via drift detection algorithms.
+That enables the detection of potentially dangerous trends in what really matters from the business point of view.
+*KPI Monitoring* page in the Project sidebar shows the registered KPIs, their trends and drift events ML cube Platform detected during time.
+
+## Integrations
+
+ML cube Platform is part of the artificial intelligence and cloud ecosystem and provides connectors to interact with Cloud Providers and MLOps solutions.
+The *Integrations* page allows to create and manage credentials that will be used by the Project's [Tasks].
+
+For instance, if data are stored in a Google Cloud Storage bucket, adding the Google Cloud Platform credentials with the right permissions, allows ML cube Platform to read data from it.
+
+Another example is to trigger a Sage Maker pipeline to retrain your artificial intelligence model with a dataset provided by ML cube Platform.
+In this case, you can create Amazon Web Services credentials with permission to create an event on Amazon Event Bridge.
+See the [Integrations] page for more information about credentials setup, data sources and retraining triggers.
+
+## Jobs Monitoring
+
+Operations like sharing data to ML cube Platform, submitting the creation of a retraining dataset or reports like RAG evaluation, trigger the execution of asynchronous pipelines in ML cube Platform cloud infrastructure.
+Each pipeline is associated with an identifier named *job id* that can be used to monitor its execution status.
+This monitoring can be done both from Web App in the *Job Status* page and, with specific SDKs method allowing automation.
+A job failure can be either due to bad requests or internal errors, you can check the error message information via the same page.
+
+[Company]: company.md
+[Tasks]: task.md
+[User]: user.md
+[roles]: rbac.md
+[Integrations]: integrations/index.md
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diff --git a/mkdocs.yml b/mkdocs.yml
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+++ b/mkdocs.yml
@@ -107,6 +107,7 @@ nav:
   - User Guide:
       - user_guide/index.md
       - user_guide/company.md
+      - user_guide/project.md
 
       - Modules:
           - user_guide/modules/index.md