VaultRAG – Enterprise Private Knowledge Agent
Local-First RAG for Secure, Air-Gapped Internal Intelligence

VaultRAG is an open-source, local-first Retrieval-Augmented Generation (RAG) agent designed for secure querying of enterprise internal knowledge bases. Built with LlamaIndex, Chroma vector store, and Ollama for completely offline, private execution — ideal for regulated industries and organizations with strict data governance requirements.

Open-Source Integration Highlights

• LlamaIndex for advanced RAG orchestration and hierarchical data indexing
• Ollama for local LLM serving (Llama-3/Mistral) ensuring 100% data privacy
• ChromaDB as the high-performance local vector store for document embeddings
• Hugging Face Transformers for local embedding generation via Sentence-BERT
• PyMuPDF & Unstructured.io for robust parsing of complex PDFs and wikis
• Giskard for automated vulnerability scanning and hallucination detection
• Docker for simplified, air-gapped containerization and deployment

Executive Summary: VaultRAG Enterprise Intelligence

Vision: Making enterprise knowledge accessible without compromising security by enabling natural-language interaction with proprietary documents entirely on-prem or on-device.

1. The Strategic Imperative

Traditional search fails on unstructured internal content, while cloud-based RAG solutions raise significant privacy concerns. VaultRAG bridges this gap, providing a secure alternative to public LLMs for organizations with strict data sovereignty mandates.

2. The Solution: Trustworthy Private RAG

VaultRAG delivers accurate answers with built-in hallucination detection, source attribution, and query moderation. Its open-source design ensures full auditability and easy customization for specific enterprise workflows.

Quantifiable Operational Impact

🛡️ Zero Data Exfiltration: Total isolation from public networks and external APIs.
🔍 High-Fidelity Retrieval: Precise unbundling of complex PDFs, wikis, and SOPs.
⚖️ Audit-Ready Design: Open-source stack allows for complete transparent security reviews.
✅ Trustworthy Output: Source-cited responses that eliminate LLM "black-box" risks.

Strategic Imperative: Mastering Private Intelligence

Enterprise intelligence is often trapped in unstructured silos—SOPs, wikis, and policy manuals—creating a "Knowledge Tax" on employee productivity. VaultRAG bridges the gap between massive data volumes and secure, actionable insights without the risk of public LLM exposure.

1. Strategic Value Proposition

Strategic Pillar	Business Impact	Quantifiable Outcome
Productivity Gains	Empowers employees to self-serve information from scattered internal sources.	70–90% Time Reduction
Operational Efficiency	Reduces internal support ticket volume by automating knowledge retrieval.	Significant Ticket Deflection
Data Sovereignty	Enforces governance controls by eliminating data leakage risk.	Zero Data Leakage

2. Regulatory Strategy: The Local-First Privacy Model

VaultRAG is architected to exceed the security requirements of regulated industries through a strict isolation model:

🔹 Air-Gapped Ingestion: Document uploads are never transmitted; all indexing and generation occur locally on the device.
🔹 Configurable Guardrails: Built-in moderation for sensitive topics to ensure answers remain professional and on-topic.
🔹 Transparent Governance: Open-source code enables security teams to audit, review, and harden the implementation.
🔹 Future-Proof Compliance: Scalable model supports future redaction and access logging without altering the core privacy architecture.

Strategic Outcome

By democratizing institutional knowledge through Context Grounding, VaultRAG transforms a compliance burden into a competitive advantage, ensuring accurate information is available to those who need it, exactly when they need it.

Target User Personas: Solving for Data Sovereignty

VaultRAG is engineered for stakeholders in highly regulated sectors where institutional knowledge is vast but inaccessible due to strict air-gapped security requirements.

Knowledge Worker

Information Specialist

Goals: Fast answers from internal docs without manual system-hopping.

Pain Points: Fragmented internal wikis; wasting hours hunting for current SOPs.

VaultRAG Benefit: Conversational access to all siloed PDFs and local wikis in one UI.

Compliance Officer

Governance Lead

Goals: Ensure responses are auditable and grounded in official policy.

Pain Points: AI "hallucinations" in critical policy interpretations; lack of source links.

VaultRAG Benefit: Verbatim citations and local grounding ensure 100% auditability.

Security Admin

IT/Infrastructure

Goals: Deploy AI tools without risking data exfiltration or cloud breaches.

Pain Points: Cloud-only RAG platforms violating data sovereignty laws.

VaultRAG Benefit: 100% local execution; no internet dependency or API leakage.

04b. Requirements & User Stories (MoSCoW Prioritization) Click to Expand

ID	User Story	Priority	Linked Component	Acceptance Criteria
VR-01	As a user, I want to upload and query docs conversationally.	Must	LlamaIndex Orchestrator	Support for PDF/MD; <5s response time.
VR-02	As a user, I want direct source citations for every answer.	Must	Citation Engine	Verbatim snippets with filename/page ref.
VR-03	As a Security Admin, I want 100% local, air-gapped execution.	Must	Ollama + ChromaDB	Zero outbound network calls detected.
VR-04	As a user, I want to ask follow-up questions with context.	Should	Buffer Window Memory	Retention of last 5 conversational turns.
VR-05	As an Analyst, I want to export threads for documentation.	Could	Markdown Exporter	Clean file export with citations included.

04c. User Journey Map: The Private Insight Lifecycle Click to Expand

Stage	Actions / Touchpoints	Legacy Pains	Autonomous Resolution	Metrics Impact
1. Ingestion	Upload document collection (PDFs, Markdown) locally.	Data security fears; manual tagging required.	Local Indexing with automated embedding generation.	0 Cloud Calls
2. Query	User asks natural-language question.	Keyword search fails on context; slow retrieval.	Vector Retrieval fetches semantic context in <1s.	-90% Search Time
3. Verification	Agent generates answer with source links.	Blind trust in AI; "Black-box" answers.	Grounded Citations link every claim to a file.	100% Traceable

Design & Architecture: The Sovereign Knowledge Stack

VaultRAG is architected following the TOGAF ADM framework, ensuring that the transition from business vision to technical execution is governed by a strict "Privacy-by-Design" mandate.

Phase A/B: Vision & Business (Secure Knowledge Access)

Capability Map: Democratizing Institutional Intelligence

[Architectural Diagram: Vision to Business Capability Map]

VaultRAG's primary objective is to create a secure, trustworthy RAG system that brings natural-language access to enterprise knowledge. Key capabilities include grounded generation and hallucination guardrails, measured by user trust scores and citation relevance.

Phase C: Information Systems (Chroma Vector Fabric)

Data Architecture: Local Persistence Layer

[Architectural Diagram: ChromaDB & Memory State Flow]

The Information architecture leverages a local Chroma Vector Store containing document chunks and enriched metadata. Conversation state is maintained in-memory for session persistence, with all data exclusively persisted on the user's local device.

Phase D: Technology (Ollama & LlamaIndex Stack)

Technical Stack: Air-Gapped Intelligence

Indexing & Retrieval	LlamaIndex with local embeddings
Vector Store	Chroma (Persistent local database)
LLM Backend	Ollama (Llama 3.1 or Mistral default)
Guardrails	LlamaGuard / Custom self-checking prompts
Frontend	Streamlit (Document upload & chat interface)

Output Format: Markdown responses with high-fidelity inline source citations.

05d. Technical Rollout Roadmap

The VaultRAG roadmap sequences prioritized user stories into an iterative release cycle, focusing on Privacy-First Foundations in the MVP before scaling into Enterprise Governance and multi-system integration. This strategy establishes immediate trust via local grounding before expanding the agentic surface area.

Implementation Phases & PI Mapping Click to Expand

Phase	Focus	Deliverables	Target Metrics	Status
1: MVP	Foundational RAG	PDF/MD Ingestion; Semantic Search; Streamlit UI	<5s Latency	Current State
2: Expansion	Advanced Ingestion	Office Docs & Confluence Support; Conversation Export	80% Coverage	Backlog
3: Hardening	Governance Layer	PII Redaction; Topic Filtering; LlamaGuard Integration	Zero Leakage	Target State
4: Scale	Deployment Ops	Docker/K8s Packaging; Multi-User Session Mgmt	99.9% Uptime	Future Vision

Current State: Grounded MVP

Baseline functionality focused on high-fidelity document parsing and verified local response generation via Streamlit.

PDF & Markdown Ingestion
Semantic Search Vectorization
Basic Grounding Guardrails

Future State: Enterprise Sovereignty

Targeting complex enterprise environments with advanced data protection and distributed infrastructure.

Office Docs & Confluence Connectors
PII Redaction & Topic Filtering
Docker/K8s Orchestration

Multi-Agent Reasoning Chain: The "Deterministic Swarm"

VaultRAG replaces the "Black Box" LLM approach with a Multi-Agent Orchestration layer. By decomposing RAG into specialized, auditable agents, we ensure that retrieval, reasoning, and safety are handled by independent workers with clear separation of concerns.

1. The Autonomous Workforce (Agent Personas)

Every query undergoes a rigorous lifecycle managed by a swarm of specialized local agents. No single LLM has the authority to generate a response unchecked.

Agent Role	Reasoning Engine	Domain Responsibility
Query Orchestrator	Stateless Control Plane	Enforces the "No Source, No Answer" policy and failure routing.
Retrieval Agent	LlamaIndex + Chroma	Semantic lookup and metadata filtering (date, security level).
Context Validator	Grounding Logic	The Hallucination Kill-Switch; decides if context is sufficient to answer.
Hallucination Guard	Llama-3.1 (Low Temp)	Performs sentence-level grounding checks against retrieved chunks.
Citation Agent	Attribution Engine	Maps answer sentences to verbatim document sources and page refs.

2. Trust Boundaries & Governance

To satisfy Enterprise Architecture (EA) reviews, the system enforces hard trust boundaries between agents:

🔒 Input Boundary: Moderation Agent blocks off-scope or speculative queries before they hit the vector store.
🔍 Logic Boundary: Answer Generation is 100% constrained to context; external inference is architecturally disabled.
🛡️ Verification Boundary: Every response must pass the Audit & Policy Agent log before reaching the UI.

3. The "Reasoning Trace" (Transparent Auditing)

VaultRAG provides full transparency by capturing the "Internal Monologue" of the agent swarm for audit forensics:

[Moderation_Agent]: Query "SOP for Remote Work" - ALLOWED. Intent identified as internal policy search.
[Retrieval_Agent]: Fetched 3 chunks from 'HR_Policy_2024.pdf' (SimScore: 0.91).
[Context_Validator]: SUFFICIENT. Chunks contain explicit remote eligibility criteria.
[Hallucination_Guard]: PASS. Generated claim "Eligibility starts after 90 days" matches source page 12.
[Citation_Agent]: Attached source: HR_Policy_2024.pdf [p. 12].

View Decision Matrix & Trust Protocols

When agents disagree (e.g., Retrieval score is high but Validation fails), the system applies deterministic resolution protocols:

Scenario	Resolution Logic
Conflicting Sources	Audit Agent prioritizes "Date Created" metadata; flags discrepancy to User.
Low Grounding Score	Generation Agent is blocked. System returns "Insufficient trusted information found."

This Composable Architecture allows enterprise security teams to swap Llama-3.1 for more specialized local models or custom legal/HR policies without refactoring the core RAG pipeline.

Architectural Dividend

By decomposing RAG into policy-enforced agents, VaultRAG ensures that no response is generated without verified claims. It effectively transforms RAG from a "probability engine" into a "deterministic intelligence utility."

The VaultRAG Intelligence Platform: Sovereign Knowledge Fabric

VaultRAG is architected as a shared, governed intelligence substrate. It standardizes how agents retrieve knowledge, invoke models, and enforce trust, ensuring that enterprise concerns like data sovereignty and auditability are handled at the platform level rather than embedded in individual agent logic.

1. Platform Responsibilities (EA Canonical View)

By decoupling agents from the underlying infrastructure, the platform provides non-functional guarantees that agents cannot bypass.

Architectural Concern	Platform Managed	Agent Controlled
Retrieval & Embedding Lifecycle	✅	❌
Model Invocation & Temperature Caps	✅	❌
Guardrail & Trust Enforcement	✅	❌
Observability & Policy Audits	✅	❌

2. Logical Subsystems Deep-Dive

🛡️ Guardrail & Trust Engine

The core differentiator. This engine enforces context sufficiency and claim-to-source alignment. If the answer confidence score is below threshold, the platform triggers an automatic refusal, preventing hallucinations at the source.

📑 Citation & Attribution Platform

Enforces the "No Citation, No Answer" mandate. It handles sentence-level mapping and version attribution, ensuring every response is grounded in a specific, auditable document revision.

Agent-Facing API Contract

Agents interact with the substrate through deliberate architectural friction, ensuring governance remains centralized:


        platform.retrieve(query, policy_context)

        platform.generate(context, constraints)

        platform.validate(answer)

        platform.audit(event)

3. SAFe Alignment: Platform as an Enabler

SAFe Concept	VaultRAG Implementation	Value for the ART
Enabler Epics	Retrieval, Trust, and Observability Services	Foundational security for all agents.
Architectural Runway	Unified Model Abstraction Layer (Ollama)	Swap models safely without agent rewrites.
System Team	Owner of the Intelligence Platform Substrate	Ensures non-functional guarantees are met.

Intelligence Dividend

By abstracting retrieval and governance into a unified substrate, VaultRAG moves from a sample-based trust model to Total Population Certainty. This allows agentic applications to scale safely and compliantly, making "Model Decay" a detectable system event rather than a hidden financial risk.

Model Lifecycle (MLE): Governing Sovereign Intelligence

VaultRAG treats the model layer—encompassing LLMs, embeddings, and verifiers—as a governed enterprise asset. We apply a continuous, non-linear lifecycle (Plan to Retire) to ensure every model is validated for determinism, safety, and offline performance before platform integration.

1. The Multi-Model Governance Matrix

Model Type	Examples (Local-First)	Lifecycle Strategy
Generative Foundation	Llama-3.1, Mistral, Qwen	Version-pinned; strict temperature/context locking.
Embedding Layer	nomic-embed-text	Deterministic chunk overlap; drift detection enabled.
Trust Verifiers	Hallucination/Claim Checkers	Continuous evaluation against "Golden Query" datasets.

2. The Model Lifecycle Pipeline (EA Control Gates)

To prevent "Model Sprawl" and "Hidden Hallucinations," we enforce four non-negotiable EA control gates:

🛡️ License & Policy Gate

Verification of OSI-approved licenses and data residency compliance before model sourcing.

⚖️ Validation Gate

Pre-production testing for prompt compliance, latency, and memory footprint on local hardware.

📦 Packaging Gate

Wrapping models behind platform APIs to prevent direct agent invocation and ensure versioning.

📊 Observability Gate

Continuous monitoring of refusal rates, hallucination flags, and confidence trends at runtime.

3. Evolve & Retire: Controlled Model Succession

VaultRAG utilizes Shadow Mode and Canary Deployments to ensure zero breaking changes during model updates:

# Example: config/models/registry.yaml
generation:
default_model: mistral-7b@v0.2
allowed_models:
- mistral-7b@v0.2
- llama-3.1-8b (Shadow_Mode_Active)

SAFe Alignment: Model as Architectural Runway

SAFe Element	Model Lifecycle Integration
Enabler Features	Validation harnesses and "Golden Query" dataset curation.
System Demo	Live model swaps demonstrating zero impact on agent logic.
Inspect & Adapt	Drift reviews and audit log analysis to trigger model retirement.

Why This Lifecycle Matters

VaultRAG treats models as governed enterprise assets. This framework ensures that trust and auditability are maintained over the long term, transforming a chaotic open-source environment into a stable, CFO-ready intelligence utility.

Infrastructure Architecture: The Sovereign Landing Zone

VaultRAG is architected as an Air-Gapped Intelligence Landing Zone. Unlike traditional cloud RAG, the infrastructure is intentionally minimal and local-first, providing deterministic, auditable execution without relying on external SaaS providers or proprietary runtimes.

1. Infrastructure Layers & Logic

We utilize a tiered isolation model to decouple agent processes from hardware, ensuring that security controls are enforced at every level of the stack.

Principle	Rationale	EA Requirement
Local-First	Ensures data residency and total privacy.	Zero Data Exfiltration
Defense-in-Depth	Assume model compromise; isolate model runtimes.	Least Privilege Access
Deterministic Execution	Enterprise trust through repeatable results.	Auditable by Design

2. Network Architecture: Zero-Trust Isolation

VaultRAG must function with the network physically disconnected. We apply a Strict Deny-All default policy:

🛡️ Outbound: Explicitly DENY; zero calls to public LLM APIs or telemetry servers.
🛡️ Inbound: Localhost only; access restricted to authenticated local users.
🛡️ Inter-process: Communication occurs via Unix sockets or secure loopback interfaces.

3. Deployment Topologies (EA-Approved)

💻 Developer Workstation

Standard MVP/PoC setup using macOS/Linux with Python and Ollama daemon.

🏢 On-Prem Enterprise Node

Dedicated departmental hardware with GPU acceleration and centralized local logging.

🔒 Air-Gapped High Security

Strict OS hardening for defense/healthcare; no network; manual model ingestion only.

4. SRE & Resilience: "Fail Closed" Logic

Managed via strict Error Budgets and local observability signals to ensure reliability during critical operations:

Infrastructure Safety Protocol

🚀 Availability: Separate processes for Ollama and Chroma to prevent full-system cascades.
⚡ Resilience: If memory is low, system enters controlled degradation; fails to "No Answer" rather than hallucinating.
🛡️ Sandboxing: Read-only mounts for source documents; write-only append logs for audit trails.

Executive Summary: Hardware Sovereignty

VaultRAG’s infrastructure transforms a chaotic AI runtime into a Governed Intelligence Utility. By enforcing total isolation and local hardware dependency, it effectively mitigates the multi-million dollar risk of data exfiltration while providing a replayable, auditable path for every response generated.

Impact & Outcomes: The Privacy-First Transformation

VaultRAG shifts the enterprise from a "Search-and-Find" model to a "Total Population Certainty" model for internal knowledge. By enforcing local-first execution and deterministic guardrails, the platform delivers measurable gains in productivity while maintaining an airtight security posture.

1. Hard-Dollar Impact: The "Audit-Proof" Knowledge Base

Value Driver	Manual Baseline	VaultRAG Outcome	Strategic Impact
Information Retrieval	Manual keyword search in fragmented silos.	70–90% Reduction in Search Time	Immediate productivity boost across HR, Ops, and Legal.
Data Leakage Risk	SaaS RAG risks PII exfiltration to public LLMs.	Zero Data Exfiltration	Suitable for Air-Gapped and Classified environments.
AI Trust Factor	"Black-box" hallucinations erode confidence.	100% Grounded with Citations	Users trust refusals as much as answers.

2. Strategic Platform Outcomes

Architectural Runway

VaultRAG provides the foundation for multiple secure assistants (Legal, Engineering, SOPs) without re-solving trust or security for every new use case.

Differentiated Portfolio Signal

Moves beyond "LLM wrappers" by demonstrating a governed, auditable, and open-source-first approach to agentic AI.

3. Outcome-to-Architecture Traceability (EA View)

This matrix connects business outcomes directly to the underlying architectural enablers:

Desired Outcome	Architectural Enabler
Faster Knowledge Access	Hybrid Retrieval and Query Rewriting
Higher Trust & Reliability	Platform Guardrails and Hallucination Detection
Scalable AI Assistants	Platform Abstraction Layer

The Sovereign Insight Dividend

By transitioning from a probabilistic "Black-Box" to a Deterministic Intelligence Platform, VaultRAG ensures that every dollar spent on AI delivers traceable value without introducing new vectors for proprietary data leakage. The Year-End Knowledge Audit becomes a "Non-Event".

VaultRAG – Enterprise Private Knowledge Agent Local-First RAG for Secure, Air-Gapped Internal Intelligence