Products
SovAIHub products include open-source implementation kits, architecture packs, and deployment references for offline runtime, local LLM RAG, internal artifact supply chains, controlled agents, and production AI governance.
Categories
SovAIHub resources are organized around practical outcomes: understand the architecture, prepare the implementation, operate the system, and govern the AI supply chain.
Architecture diagrams, checklists, article companions, starter templates, and public learning assets.
Implementation packs with code, setup guides, deployment files, prompts, and operating instructions.
Advanced patterns for air-gapped AI, local LLMs, private RAG, internal artifact hubs, monitoring, and governance workflows.
Fixed-scope reviews and implementation support for teams that need help moving from architecture to production.
Flagship series
A phased set of working reference implementations. Start with offline runtime, add local LLM RAG, then introduce an internal artifact hub for offline builds.
A laptop-ready offline runtime demo for sovereign AI patterns: local documents, approved tools, audit logs, and no runtime internet dependency.
A private RAG workflow using Ollama locally, grounded prompts, cited answers, and audit logging without an external LLM API.
A controlled internal artifact hub pattern for local registries, Python wheelhouses, prompt and tool approvals, checksums, and offline builds.
Roadmap
The product line is designed as a phased architecture journey, not a set of disconnected demos.
Prepare connected. Run disconnected.
Retrieve local evidence and generate with a local model.
Build new apps from approved internal artifacts.
Generate new RAG, ML, and agent apps from governed templates.
Add RBAC, scanning, signing, observability, and promotion workflows.
Comparison
Use the comparison below to pick the right implementation kit for your current objective.
Use this if you want to prove offline runtime, approved tools, local documents, and audit logging first.
Use this if you want a local LLM RAG demo using Ollama, grounded prompts, and cited answers.
Use this if you want an internal artifact hub for base images, Python wheels, prompts, tools, approvals, and offline builds.
Catalog
Explore architecture packs, implementation kits, and deployment resources for private and governed AI systems.
Open-source laptop-ready starter kit for demonstrating an air-gap-ready sovereign AI runtime with local documents, approved tools, offline Docker bootstrap, and audit logs.
Open-source local LLM RAG starter that runs private retrieval, grounded prompting, Ollama inference, citations, and audit logging without an external LLM API.
Open-source reference implementation for a controlled internal AI artifact supply chain with local registry, wheelhouse, prompt/tool manifests, approvals, and offline builds.
Sovereign AI, RAG, agent, and governance architecture templates for planning private AI systems.
A deployable private ChatGPT-like solution pattern for company documents, retrieval, citations, and monitoring.
Kubernetes and OpenShift manifests, routes, PVC patterns, secrets layout, and deployment guidance for AI systems.
Similarity-based and LLM-based answer validation patterns for grounded enterprise RAG systems.
How these kits fit together
The individual kits are useful on their own, but the stronger story is how they combine into a sovereign AI delivery model.
Package the application while connected, then run with local documents, approved tools, and audit logs after internet is disconnected.
Retrieve local evidence, build a grounded prompt, generate with a local Ollama model, return citations, and log the response.
Use an internal registry and local wheelhouse so new applications can be built from approved artifacts instead of direct internet pulls.
Offers
For teams that need hands-on help, SovAIHub offers focused support around architecture review, private RAG implementation, air-gap readiness, deployment planning, and AI governance.
Review a team's RAG, agent, cloud, data, artifact supply chain, or monitoring architecture and provide improvement recommendations.
Help build a controlled document assistant with ingestion, retrieval, local or cloud model integration, citations, and deployment guidance.
Assess whether a team can run and build AI workloads without direct internet access using internal images, packages, models, and tools.
A practical validation layer using retrieval confidence, source checks, response review, and LLM-based revalidation patterns.
Contact
Use the contact page to request a private RAG kit, architecture review, air-gap readiness review, OpenShift deployment pack, or AI governance implementation plan.
Share your use case, data environment, deployment constraints, and governance needs to identify a practical implementation path.