AI
Use models, organizational knowledge, documents, voice, and agents through governed interfaces.
AI is the product domain for building and using governed AI experiences. It provides stable application interfaces while keeping model runtimes, storage engines, and workflow components behind platform boundaries.
Choose an outcome
Access models
Discover and use platform-hosted or connected models.
Build with the BSQAI API
Follow an application journey from authentication to operation.
Ground responses with RAG
Ingest organizational knowledge and retrieve relevant context.
Connect tools with MCP
Make Model Context Protocol servers available to an organization.
Process documents
Convert documents for extraction and retrieval workflows.
Capability map
Model access and routing
Model as a Service covers model discovery, registration, cloud-model connections, and the hand-off into inference. Applications integrate through the BSQAI API instead of depending directly on internal gateways or serving engines.
Routing policies can select providers and models, apply fallback behavior, and retain telemetry without changing the application-facing contract.
Conversational AI and knowledge
Conversational experiences combine model access, conversation state, organizational knowledge, and permission-aware retrieval. The Portal provides the user-facing experience; RAG explains ingestion, chunking, embeddings, vector search, and grounded responses.
Use the AI and knowledge guided path when the goal is to help users work with approved models and enterprise content.
Documents, voice, and multimodal input
Document processing converts files into structured content for extraction, classification, and retrieval workflows. Speech services add real-time or batch transcription and supported text-to-speech paths. These capabilities share authentication, workflow, storage, and observability services with other AI experiences.
Use Docling for document conversion details and RAG for the complete ingestion and retrieval flow.
Agents and tool use
Agentic applications add orchestration, governed tools, connectors, and secure execution to model interactions. BullSequana AI exposes approved tool interfaces and carries the calling identity's tenant and team boundary into those integrations.
MCP Servers is the tool-integration surface. A Platform Admin makes a Model Context Protocol server available to an organization and chooses which of its tools may be called. Each member links their own credential for the system behind it, and every call passes through the MCP Gateway.
Start with the application-building path, then use How MCP works on the platform for the request flow and the MCP Lifecycle Operator reference for managed workload reconciliation.
Model lifecycle
Training, fine-tuning, evaluation, promotion, registration, and serving form one lifecycle. Data workspaces and MLflow retain development and experiment context; AI exposes approved models through stable interfaces; Foundation runs the serving infrastructure.
The Data and ML guided path connects those responsibilities.
Typical application flow
- Identify whether the application needs direct generation, organizational knowledge, document processing, or tools.
- Choose a model and confirm the tenant and team access boundary.
- Authenticate to the BSQAI API and implement the smallest end-to-end request.
- Add retrieval, documents, voice, or tools only when the use case requires them.
- Deploy the application with health checks, resource limits, telemetry, and a rollback path.