Skip to content

Pillar 01 · Platform

From infrastructure to business interfaces.

Five layers to build, deploy and operate critical AI rituals in production — inside your infrastructure, under your rules.

Engines change. Control remains.

AI engines are replaceable by design; the control system — steering, evidence, expert validation, memory — endures. That is what makes the architecture reversible.

Architecture de la Plateforme Nexa Forward : quatre couches, de la solution métier au déploiement cloud.

Chapter 1

Infrastructure

NEXA deploys where your data is allowed to live. Not the other way round.

  • On-premise deployment
  • Multi-cloud compatibility
  • Integration with existing infrastructure
  • Security and environment isolation
  • Data residency configurable per project

Product visual coming soon

Chapter 2

Nexa Code

The development teams’ environment, where quality rules and production constraints are set at design time.

  • Reusable libraries and components
  • Faster development of a new ritual
  • A common core to industrialise instead of rewriting
  • Custom features and rituals

Product visual coming soon

Chapter 3

Nexa Pipeline

The CI/CD chain that tests AI features across several environments before production. Checks are blocking.

  • Multi-environment management
  • Automated promotion to production
  • Deployment and updates
  • Pre-production checks: dry-run, column contract, integrity hash
  • Reproducible, reliable executions

Product visual coming soon

Chapter 4

Nexa Studio

Where business experiences are created. One core, a very wide range of interfaces.

  • Dashboards and data visualisation
  • Frames and business interfaces
  • Governed conversational agents
  • Search engines
  • Document generation
  • Interfaces for experts to control, validate and use results
Creation journey: from business need to generated console
From a business need expressed in natural language to a generated console.

Chapter 5

Connectors and interoperability

A core that only sees its own executions governs nothing. The SDK and connectors plug in the rest of your IT estate.

  • Data sources and warehouses
  • Enterprise applications
  • AI models and providers
  • Catalogue of available connectors
  • Instrumentation SDK for in-house algorithms
Source catalogue: 33 available connectors with AI & ML, database, cloud storage and MCP server filters
Source catalogue: databases, applications, MCP servers.

See the core at work.

A demo on your own documents, in your context — not on a demo dataset.

Ready to industrialize your decisions?

The AI that holds up in audit.