SovAIHub
GatedPrototypeintermediate program

Private RAG Engineering

Build and evaluate a private, grounded, permission-aware RAG system with citations, access controls, observability, and evidence.

Discuss a pilot cohortBrowse shared modules
Version
0.1.0
Modules
5
Delivery
Pilot planning
Availability
Curriculum and lab under development
Content review
2026-08-03
Automated lab
2026-08-03

Program fit

Built for teams responsible for implementation decisions.

A practical engineering program for grounded, permission-aware retrieval systems that remain inside an approved boundary.

Intended roles

  • AI engineers
  • Solution architects
  • Application engineers

Prerequisites

  • Working knowledge of Python or a comparable application stack
  • Basic understanding of embeddings, retrieval, and model inference
  • Access to an approved local or customer-hosted lab environment

Capabilities

What the program is designed to develop.

CAP-01

Control document ingestion and lineage

CAP-02

Implement permission-aware retrieval

CAP-03

Test citations and groundedness

CAP-04

Handle no-answer and adversarial cases

CAP-05

Generate a local evidence report

Shared module sequence

One maintained registry, assembled for this outcome.

Program pages resolve module titles and descriptions directly from the Academy registry. Updates remain controlled in one source rather than copied across pages.

  1. 1
    SAI-100Content reviewed

    Sovereign AI Foundations

    Establish the vocabulary, deployment boundaries, shared responsibilities, and control objectives needed to reason about sovereign AI systems.

    Open documentation
  2. 2
    SAI-230Prototype

    Private RAG and Permission-Aware Knowledge

    Build grounded retrieval with controlled ingestion, citations, access enforcement, evaluation, lineage, and safe no-answer behavior.

    Open documentation
  3. 3
    SAI-240Draft

    Identity, AI Gateways, and Egress Control

    Propagate identity and enforce inspection, DLP, routing, endpoint, and response policies at controlled AI boundaries.

    Open documentation
  4. 4
    SAI-250Prototype

    Governance, Assurance, and Evidence

    Turn policies and control objectives into verifiable runtime, release, decision, and audit evidence.

    Open documentation
  5. 5
    SAI-260Draft

    Observability, Reliability, and FinOps

    Measure workload health, behavior, evaluation quality, capacity, cost, incidents, and operational evidence.

    Open documentation

Practical labs

Observable work inside an approved environment.

Exercises use local, customer-hosted, or explicitly approved private infrastructure and are designed to produce repeatable validation and evidence.

LAB-230-01Prototype

Permission-Aware Private RAG Lab

Run a synthetic local retrieval system that enforces document permissions, citations, deletion, no-answer behavior, and injection controls.

Lab outputs

  • Working private retrieval service
  • Permission and citation test results
  • Versioned evidence bundle
Last content review: 2026-08-03Last automated lab validation: 2026-08-03Independent reproduction: Not yet completed
Open commands and acceptance gates

Lab boundary principles

  • No customer documents or prompts are sent to SovAIHub by default
  • Prerequisites and supported configurations are explicit
  • Validation is automated or clearly repeatable
  • The environment can be reset and the exercise repeated
  • Limitations and unsupported configurations are stated honestly

Capstone and assessment

Finish with implementation artifacts, not attendance alone.

Capstone outputs

  • Working customer-hosted or local RAG deployment
  • Controlled document ingestion
  • Retrieval and citation evaluation
  • Permission tests
  • Hallucination and grounding checks
  • Audit and evidence report

Assessment method

Practical capstone assessed through repeatable retrieval, permission, citation, no-answer, and evidence checks.

Delivery modes

  • Pilot planning
  • Customer-hosted prototype lab
  • Enterprise interest registration

Credential guardrail: Initial delivery may use “program completed,” “assessed completion,” or “capstone passed.” It does not award a professional certification.

Connected implementation assets

Build on SovAIHub kits and solution patterns.

Program pages resolve kit details from the existing SovAIHub product registry, keeping product information maintained in one place.

Local LLM RAG Kit

SovAI Air-Gap AI Starter v0.2 with Ollama

Open-source local LLM RAG starter that runs private retrieval, grounded prompting, Ollama inference, citations, and audit logging without an external LLM API.

View kit

RAG Kit

SovAI Private RAG Kit

A deployable private ChatGPT-like solution pattern for company documents, retrieval, citations, and monitoring.

View kit

Governance Module

SovAI Hallucination Guard

Similarity-based and LLM-based answer validation patterns for grounded enterprise RAG systems.

View kit

Related solution

Private RAG for Enterprise Documents

Related solution

Sovereign EvalOps

Current limitations

What this program does not claim yet.

Honest release boundary

  • The reproducible delivery pack and independent lab validation are not complete yet.
  • Participants must use synthetic or explicitly approved data in Academy exercises.
  • Runtime adapters will declare tested versions separately from the stable curriculum.

Enterprise pilot planning

Discuss Private RAG Engineering for your team.

Share role mix, environment, technical constraints, data boundary, and desired implementation outputs. Do not submit sensitive architecture details through the public form.

No public checkout, account, or sensitive architecture upload required

Register enterprise interestReview AI readiness