Explore the platform
Understand how AI, Data, and Foundation work together in BullSequana AI.
BullSequana AI separates customer-facing capabilities from the shared services that run them. Use this overview to find the right product domain before opening implementation detail.
Product domains
| Domain | Use it when you need to | Start with |
|---|---|---|
| AI | access models, ground responses in organizational knowledge, process documents, or build conversational and agentic applications | Explore AI |
| Data | connect, transform, govern, analyze, or operationalize enterprise data | Explore Data |
| Foundation | operate shared identity, inference, networking, observability, storage, databases, and delivery services | Explore Foundation |
These domains are responsibility boundaries, not isolated products. Most production solutions use all three.
Choose the right starting point
Start in AI when the desired outcome is an AI experience: model access, RAG, document processing, voice, or agents.
Start in Data when the work begins with sources, pipelines, catalogs, notebooks, MLflow, streaming, or analytics.
Start in Foundation when the question concerns how the platform authenticates users, serves models, routes traffic, stores state, emits telemetry, or reconciles services.
If your goal is a task rather than a product-area explanation, use the guided paths. They connect the product domains in the order needed to reach an outcome.
From capability to implementation
The documentation uses three levels of detail:
- Guided paths describe an end-to-end customer outcome.
- Product pages explain the AI, Data, or Foundation responsibility involved.
- Reference pages document APIs, components, configuration, and operational behavior.
Guided paths are the primary entry point for customer work. Product pages add capability context, while component pages are reserved for deployment, architecture, and troubleshooting detail.
Common cross-domain flows
| Outcome | AI contribution | Data contribution | Foundation contribution |
|---|---|---|---|
| Grounded assistant | model access, RAG, conversational experience | governed source data and ingestion | identity, inference, vector storage, observability |
| Document workflow | extraction, classification, retrieval | pipeline execution and artifact handling | compute, storage, workflow services |
| Predictive application | inference and application API | workspace, experiments, pipelines, model registry | serving runtime, security, telemetry |
| Governed analytics | natural-language or embedded AI experience | catalog, processing, streaming, BI | tenancy, databases, networking, audit |
For a concise definition of tenants, teams, resources, and responsibility boundaries, read Core concepts.