The Sovereign Knowledge Vault – Data Centralisation & Intelligence

Subject: Centralised Truth Management and Semantic Indexing Scope: Cross-Module Data Architecture Module: TTS Core Infrastructure

8.1 Technical Concept: The Single Source of Truth

The Sovereign Knowledge Vault is the foundational data layer that powers the intelligence of Warden. It is engineered to solve the problem of "Data Silos" by converting unstructured corporate knowledge into a structured, machine-readable Knowledge Graph. The site does not simply store files; it indexes the logic and intent behind them.

8.2 Semantic Indexing & Vectorisation

The site employs a high-performance Vector Database architecture to manage corporate intelligence:

• Deep Parsing: Upon upload, documents (PDFs, Word, Confluence exports) are parsed using OCR and structural analysis to maintain the context of tables, headers, and footnotes.
• Embedding Generation: Content is converted into high-dimensional mathematical vectors. This allows the site to perform Semantic Retrieval, finding information based on meaning rather than just matching keywords.
• Metadata Tagging: Every piece of information is tagged with a "Reliability Score" and an "Expiry Date," ensuring the AI prioritised the most recent and authoritative data for any given task.

8.3 Operational Management

1. Vault Ingestion: Users upload raw documentation into the vault. This includes Information Security Policies, Employee Handbooks, Technical Specifications, and previous RFP responses.
2. Conflict Resolution: If the site identifies contradictory information (e.g., two different versions of a "Password Policy"), it flags a Conflict Alert for the administrator to resolve.
3. Active Learning: As the business evolves and policies are updated, the site performs a "Delta Sync," only re-indexing the specific sections that have changed to maintain the integrity of the Knowledge Graph.

8.4 Security & Air-Gap Posture

The Sovereign Knowledge Vault is built with absolute isolation protocols.

• No External Training: No data stored in the Vault is ever used to train public AI models.
• Encrypted Retrieval: Data is encrypted at the storage level and is only decrypted in memory during an active RAG (Retrieval-Augmented Generation) cycle.
• Audit-Ready Logs: The site maintains a persistent log of every time a piece of data is "retrieved" by an AI agent, detailing the specific module and user that initiated the request.

Updated on: 29/01/2026