Version françaiseBook a demo

Platform

One platform for the whole data & AI lifecycle

From the first connected source to the model served in production, every building block shares the same identities, the same permissions, the same catalog and the same audit trail. Your teams and your AI agents work in it together, on your infrastructure.

Book a demoInstallation guide

Capabilities

Nine capabilities, one console

Each one stands on its own, and they all talk to each other: a connection feeds the pipeline, the pipeline writes the table, the table feeds the model.

Connectors

Databases, files, S3, SFTP, REST APIs, Hadoop and SAS, connected through a reference to an encrypted secret.

Lakehouse and SQL

Iceberg tables on your S3, federated SQL with Trino, catalog and lineage.

Notebooks

Jupyter and VS Code in the browser, with a coding assistant wired to your own model.

Machine learning

Experiments, model registry and serving, with drift monitoring.

Generative AI

An LLM gateway and RAG over your documents, with the models you choose.

Governance

Per-project permissions, audit of humans and agents, encrypted secrets, costs and quotas.

AI agents

An MCP server so your agents create, run and schedule work, under your rules.

Architecture

How the building blocks fit together

A control plane, an execution layer, your own resources: everything runs in your cluster. Your teams come in through SSO, your agents through MCP, and both go through the same API.

Your teams · SSO

◆ Your AI agents · MCP

graal · control plane

  • Console
  • REST API and MCP
  • Identity · Keycloak
  • Metadata · PostgreSQL

graal · execution

  • Jobs and workflows
  • Notebooks
  • Distributed Spark
  • Model serving

Your resources

  • S3 storage
  • LDAP / AD directory
  • Image registry
  • Observability

Your Kubernetes cluster · on-premises, private cloud or qualified hosting

  • One identity

    Your directory and SSO for people, service accounts for agents and applications.

  • One permission model

    Roles per project and per resource, applied the same way in the console, the API, SQL and MCP.

  • One catalog

    The tables written by pipelines and jobs are the ones read by SQL, notebooks and models.

  • One audit trail

    Every action, human or agent, is traced with its author, its resource and its result.

Built on open standards

  • Kubernetes
  • Helm
  • Apache Iceberg
  • Trino
  • Apache Spark
  • Jupyter
  • MLflow
  • Keycloak
  • PostgreSQL
  • S3
  • MCP

Openness

Nothing locks you in

What you build in graal stays readable, exportable and drivable by your own tools.

Exported code

Pipelines export to Pandas or PySpark and run outside graal. Jobs and pipelines are versioned in Git.

Open formats

Parquet and Apache Iceberg on your S3 storage, standard SQL with Trino: your data can be read without graal.

Public APIs

A REST API described in OpenAPI, the MLflow API for model tracking, and MCP for agents.

Your automation tools

A Terraform provider and a command-line interface, generated from the same contract as the API.

The foundation

What holds it all together

AI agents

One service account per project, human approval for sensitive actions, no destructive tool enabled by default, and every call visible live.

Explore AI agents

Governance

Permissions per project and per resource, audit of humans and agents, encrypted secrets, costs and quotas.

See governance

Deployment

One Helm chart, on-premises, in a private cloud or with a qualified hosting provider, highly available and all the way to offline installation.

See deployment options

Frequently asked questions

Do we have to adopt everything at once?

No. Many start with jobs and orchestration, or with the lakehouse, then expand. Each capability works on its own and joins the others without migration.

Where does processing run?

In your Kubernetes or OpenShift cluster, on-premises, in a private cloud or with the hosting provider of your choice. Data stays in your storage.

Can we keep our current tools?

Yes. Your Python and Spark scripts, dbt projects and MLflow clients work as they are, and your Iceberg tables stay readable by any compatible engine.

Which model drives the agents and generative AI?

The one you choose: a model hosted on your premises and served by graal’s LLM gateway, or the provider of your choice.

Let's see graal on your data

An end-to-end demonstration, from source to served model, driven by your teams and by an agent.

Book a demoDiscover the pilot