Resources

Everything you need to evaluate graal

The technical documentation, the demo scenario and guides to choose, buy and deploy a data & AI platform on your infrastructure.

Documentation

The technical documentation

In French and English, from the first command to day-to-day operations.

Quickstart

A first workload, in a few commands.

Read

Installation

The Helm chart, its values and its prerequisites.

Read

Architecture

Components, flows and permissions.

Read

Concepts and vocabulary

Organizations, projects, jobs, runs, workspaces.

Read

AI agents (MCP)

Connecting an agent, its permissions, its tools.

Read

Security and governance

Identity, roles, secrets and audit.

Read

Roles and permissions

Who can do what, on which resource.

Read

REST API

The OpenAPI contract and its authentication.

Read

Demo

See graal in fourteen minutes

The scenario

Seven steps, from a low-code pipeline to an AI agent scheduling a job, on éCO2mix open data.

See the scenario

A demo on your use case

The same journey, adapted to your industry and your questions.

Book a demo

Guides

Choose, buy, deploy

How to choose a data & AI platform

Five selection criteria and a factual table, dated and sourced.

Read the guide

Buying graal in the French public sector

Procedure exemption, innovative procurement: the thresholds and their texts.

Read the guide

The 8-week pilot

Milestones, deliverables, success criteria and next steps.

Read the guide

What “sovereign” means

A four-point definition, and why the question matters now.

Read the guide

Tracing and documenting for compliance

NIS2, DORA, Data Act, AI Act and the SREN law.

Read the guide

Deploying graal

On-premises, in a private cloud or with a qualified hosting provider.

Read the guide

Upcoming guides

What we are writing next

In preparation

Moving off a Hadoop estate

What carries over as is, what gets rewritten, and in which order.

In preparation

Kubernetes for data teams

What a privilege-free installation changes, for the platform team and for data scientists alike.

In preparation

Catalog and governance

When a data team needs them, and where to start.

A question left unanswered?

Write to us: a precise answer about your context beats a generic guide.