Common Questions
Frequently asked questions about using BullSequana AI.
Platform Overview & Access
What is BullSequana AI?
BullSequana AI is a modular platform for running AI and data workloads in production. It combines a Kubernetes-based runtime foundation with user-facing and developer-facing services for model access, data handling, retrieval, automation, observability, and deployment.
What can I do with it?
Depending on your role, you can:
- use the
CoreAI Portal - access models through the stable
CoreAI API - upload and manage files
- build retrieval and document-based AI experiences
- deploy models and AI services
- run analytical and data-oriented use cases
How do I access the platform?
You access the platform through the URL provided for your deployment. Authentication is required and is normally handled through your organization’s identity provider via the platform login flow.
Do I need technical skills to use it?
Not always. Many day-to-day actions are available through the portal, especially for business users and operational users. More advanced work, such as API integrations, workflow design, or deploying new use cases, is meant for developers, AI engineers, or platform teams.
Data, Files, And Vector Search
Where is my data stored?
Different types of data are stored in different platform components:
MinIOfor files, objects, and artifactsPostgreSQLfor application and operational dataMilvusfor vectorized data used in semantic retrievalClickHousefor analytical workloads where ProAI is in use
Can I upload and manage files from the portal?
Yes. The portal is integrated with the same backend APIs used by the platform for file handling, so users can upload, browse, and manage files through the UI.
If I want files to be vectorized for retrieval or RAG, is a normal file upload enough?
Not by itself. Files that should enter vectorization and retrieval workflows must be uploaded through the backend API path that triggers the relevant processing workflows (/v1/files/upload). The portal already uses that same integrated API path where supported, so users in the portal are not bypassing the platform flow.
For developer integrations, do not call Docling directly from your application code. Use the CoreAI API as the platform integration boundary.
What is Milvus used for?
Milvus stores embeddings and supports similarity search. It is used when the platform needs semantic retrieval, vector search, or retrieval-augmented generation over indexed content.
Workflows & Automation
What is the difference between Argo Workflows and Temporal?
They serve different purposes:
Argo Workflowsis used for infrastructure-oriented, Kubernetes-native, and batch-style workflowsTemporalis the preferred workflow engine for developers building application-level and durable business workflows
In practice, platform users and operators may interact with Argo-based workflows, while developers are generally encouraged to build new product or application workflows with Temporal.
Can users create workflows in the platform?
Users and platform teams can work with infrastructure or processing workflows exposed through the platform, especially where Argo Workflows templates or operational flows already exist.
For new application logic, service workflows, or long-running business processes, developers should prefer Temporal.
Can I build workflows without writing code?
Some data movement and operational flows can be configured with platform tools such as Airbyte or existing templates. But fully custom workflows usually still require engineering work, especially when they involve application behavior, integrations, or custom business logic.
How do I monitor workflow progress?
That depends on the workflow type:
Argo Workflowsprovides execution status for Argo-based jobsTemporalprovides visibility for Temporal-managed workflows- platform observability tools such as logs, metrics, and traces help with deeper troubleshooting
AI Capabilities
What is KubeAI used for?
KubeAI is part of the inference layer of the platform. It helps deploy and expose models on Kubernetes so they can be served reliably in production.
What are LLMs used for in BullSequana AI?
LLMs are used for use cases such as conversational AI, summarization, question answering, extraction, agentic behavior, and other application features exposed through the portal or APIs.
What is RAG?
RAG, or Retrieval-Augmented Generation, combines model generation with retrieved content from indexed data sources. It is useful when answers should be grounded in documents, collections, or enterprise knowledge instead of relying only on the model’s pretrained knowledge.
Can I use my own documents for AI search or question answering?
Yes, but the documents need to go through the correct ingestion and indexing flow so they can be processed, embedded, and made available for retrieval. The portal can participate in that process through its backend integrations, and programmatic integrations can do the same through the platform APIs.
Security & Permissions
Is data secured on the platform?
The platform is designed with production security in mind, including controlled access, protected service exposure, secret handling, and platform-level security components. The exact security posture still depends on the deployment configuration and the customer environment.
Can access be restricted by user or team?
Yes. Access is controlled through the platform identity and authorization model, including roles, groups, and service-level permissions where applicable.
Support
Who should I contact if something is not working?
Start with your platform administrator or the support contact defined for your deployment. For operational issues, the relevant portal, workflow, API, or platform logs may also need to be checked by the platform team.