Product presentation

BullSequana AI is a sovereign AI platform designed to run production AI workloads on your terms. It is modular, open-source-aligned, and built for organizations that want control over their data, models, infrastructure, and deployment choices.

The platform is designed to run on BullSequana hardware, on existing GPU infrastructure, or in sovereign and private cloud environments. Its architecture lets teams adopt the platform progressively, from operational runtime services to AI application capabilities and data-centric extensions.

Platform Positioning

• Sovereign by design, with strong control over data, models, and infrastructure choices
• Open-source-aligned, with modular components that reduce lock-in
• Hardware-agnostic, able to run on BullSequana systems or existing GPU servers
• Built for production AI workloads, from GenAI inference and RAG to broader data and analytics operations

Platform Tiers

Runtime

The Runtime layer is the operational foundation of the platform. It provides networking, security, storage, inference, observability, and delivery automation.

CoreAI

CoreAI is the Core AI platform layer. It provides what teams need to serve and manage GenAI capabilities in production, including stable APIs, model lifecycle services, LLM access, retrieval tooling, and AI observability.

ProAI

ProAI extends the platform with enterprise data and analytics capabilities. It brings together ingestion, streaming, OLAP, and business intelligence services that help organizations connect AI systems with operational and analytical data.

Use Cases

Use-case solutions build on the lower layers to deliver business-facing AI applications. These solutions combine the Runtime foundation, CoreAI capabilities, and ProAI data services where needed.

Adoption Model

BullSequana AI supports progressive adoption:

• start with Runtime to establish the operational AI foundation
• add CoreAI to serve and manage GenAI capabilities in production
• extend with ProAI when data ingestion, streaming, analytics, and BI become part of the target architecture
• build Use Cases on top of the shared platform layers

Why This Architecture Matters

This layered structure helps organizations move from infrastructure control to AI enablement and then to data-aware enterprise AI systems, without forcing every capability into a single monolithic stack from day one.