AI Factory Use Cases#
AI Factory is designed to help you build Sovereign AI solutions — where your data, models, and AI-driven applications operate under your governance, in your infrastructure, with your trusted content.
These use cases highlight how AI Factory components work together to support real-world, production-ready Sovereign AI applications — across industries and solution patterns.
For industry-specific solution examples, see: Industry Solutions with AI Factory .
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Retrieval-Augmented Generation (RAG)#
RAG is a foundational pattern for Sovereign AI — it lets you deliver accurate, grounded responses by combining LLMs with your enterprise data.
What#
Use AI models that retrieve trusted content from your Knowledge Bases, rather than relying only on opaque base models.
Make responses auditable and explainable — key for Sovereign AI.
Why#
Reduce hallucinations.
Maintain compliance and traceability.
Ensure AI answers reflect your latest business knowledge.
How#
Build Create a Knowledge Base in Gen AI Builder from your Data Sources.
Implement tuned Create a Retriever in Gen AI Builder for semantic search.
Power Create an Assistant in Gen AI Builder and apps with RAG pipelines.
Learn More#
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Document Intelligence Pipelines#
Automate the transformation of documents into structured, queryable knowledge that remains inside your governance boundary.
What#
Apply OCR, parsing, summarization, and metadata extraction.
Store results in Create a Knowledge Base in Gen AI Builder or Vector Engine.
Why#
Keep sensitive documents in your infrastructure — no third-party API calls required.
Build explainable pipelines for compliance-heavy domains.
How#
Use Pipelines and data preparation .
Leverage built-in Pipelines reference - Preparers (OCR, parse PDF, parse HTML).
Index results for search or RAG.
Learn More#
Pipeline Getting Started
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Conversational Assistants and Chatbots#
Build Assistants that run within your infrastructure, use your models, and rely on your Knowledge Bases — with complete visibility and control.
What#
AI Assistants powered by Model Serving and your RAG pipeline.
Defined and governed with Create a Ruleset in Gen AI Builder .
Why#
Avoid relying on public API services for LLM responses.
Control tone, style, compliance — essential for Sovereign AI.
How#
Build Create an Assistant in Gen AI Builder in Agent Studio.
Govern behavior with Rulesets (optional) .
Add Create a Tool in Gen AI Builder to safely integrate internal systems.
Learn More#
Agent Studio in Hybrid Manager
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Semantic Search Across Enterprise Content#
Enable natural language search on your content — while ensuring embeddings and search data remain inside your control.
What#
Vector-based search on Knowledge Bases or Vector Engine.
Supports text, hybrid structured/unstructured data, images.
Why#
Keep embeddings in your databases or object storage — not in third-party vector DBs.
Maintain auditability and security.
How#
Use Create a Retriever in Gen AI Builder to expose search to Assistants or apps.
Use Vector Engine (PGvector) for in-database search.
Learn More#
Vector Engine Hub
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Real-Time Model Inference APIs#
Deploy and serve models inside your Kubernetes clusters, under your governance — not through opaque third-party APIs.
What#
Serve LLMs, embeddings, vision models via KServe-based Model Serving.
Models pulled from your Model Library Reference Manual , deployed via How-To Create an InferenceService for NVIDIA NIM Container .
Why#
Full control of model stack and model versions.
Run inference in GPU-accelerated, air-gapped environments if needed.
How#
Manage models in Model Library Reference Manual .
Deploy with Model Serving Reference Manual .
Secure endpoints with Hybrid Manager controls.
Learn More#
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Automated AI-Powered ETL Pipelines#
Keep AI data pipelines inside your controlled environment — with no data leakage.
What#
Use Create a Structure in Gen AI Builder and Pipelines to run:
AI categorization
Document summarization
Metadata extraction
Data cleansing
Why#
Avoid sending sensitive data to cloud APIs.
Build reusable, explainable ETL components.
How#
Run as Tools in Create an Assistant in Gen AI Builder or standalone Pipelines.
Monitor and govern pipeline execution via Hybrid Manager.
Learn More#
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Summary#
Sovereign AI use cases are about keeping data, models, and AI applications under your governance:
Build RAG pipelines grounded in your enterprise data
Automate document intelligence workflows
Deploy conversational Assistants in your infrastructure
Serve AI models via GPU-backed Model Serving
Run AI-powered ETL pipelines with no data leaving your environment
Power enterprise semantic search with full control of embeddings
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Next steps:
Follow a Learning Paths
Get started with Gen AI How-To Guides
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AI Factory gives you the foundation to build trusted, governed, explainable Sovereign AI — on your terms.
→ Start today with Getting Started - AI Factory 101 , or deploy your first Create an Assistant in Gen AI Builder .