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Introduction to Snowvault​

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1. Overview​

What Is Snowvault?​

Snowvault is a purpose-built, AI-native knowledge platform designed to securely store, structure, and retrieve both structured data and unstructured content using semantic search and large language models.

It functions as a central, governed knowledge vault that sits behind AI agents, applications, and user interfaces. Rather than embedding intelligence directly into documents or tools, Snowvault provides a single authoritative backend from which AI systems can retrieve contextually relevant information in a controlled and auditable way.

Snowvault separates three concerns that are commonly conflated in AI systems:

  • knowledge storage and ownership
  • semantic structure and governance
  • AI reasoning and response generation

This separation allows multiple agents, products, and interfaces to operate over the same trusted content without duplication, leakage, or loss of control.

At a conceptual level, Snowvault answers a fundamental question for organisations adopting AI:

Where should trusted knowledge live, and how should it be accessed safely?

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2. Problem Statement​

Why Snowvault Exists​

Modern organisations generate vast volumes of information: documents, policies, procedures, emails, webpages, workflows, and operational data. This information is typically scattered across systems and was never designed to be consumed safely or correctly by AI.

When AI systems are layered directly on top of this content using naïve retrieval approaches, predictable failures occur:

  • keyword or similarity search ignores intent and abstraction
  • sensitive information is unintentionally exposed
  • context is lost between documents and sections
  • multiple agents duplicate access logic and permissions
  • no audit trail exists explaining why an answer was produced

Snowvault exists to solve these problems by introducing explicit semantic structure and governance between content and AI systems.

It ensures that knowledge is retrieved based on meaning, scope, authority, and permission — not merely on textual similarity — enabling organisations to deploy AI with confidence rather than risk.

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3. Core Capabilities​

Central Knowledge Vault​

Snowvault ingests documents, webpages, policies, workflows, reference data, and other business content into a governed store. Both structured and unstructured sources are supported.

All content is explicitly associated with a tenant and namespace, ensuring isolation, ownership, and controlled exposure by default.

Vector-Based Semantic Retrieval​

Content is embedded and indexed so users and AI agents can ask natural-language questions and receive context-aware answers rather than keyword matches.

Retrieval is driven by semantic relevance but constrained by declared metadata, permissions, and binding rules — ensuring answers are meaningful and safe.

AI Agents on Top of Data​

Snowvault supports multiple domain-specific agents (for example: support, legal, operations, marketing).

Each agent queries the same backend, but requests are routed using an agent reference that determines tenant, namespace, authority rules, and response mode.

Multi-Tenant by Design​

Tenant isolation is enforced at every layer. Explicit tenant references, namespaces, and access controls ensure content is never shared, inferred, or leaked across organisational boundaries.

API-First Architecture​

Snowvault exposes a single, stable API that can serve many downstream consumers, including internal tools, websites, and plugins such as WordPress or custom web agents.

Applications consume Snowvault as a service without embedding knowledge logic locally.

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4. Core Concepts​

Tenants​

Tenants represent the logical owners of data — such as a company, brand, or client. Every piece of content in Snowvault is explicitly bound to a tenant.

Namespaces​

Namespaces provide separation within a tenant. Typical examples include learning content, authoritative vault content, and curated public content.

Namespaces allow organisations to control where knowledge may be exposed and how it may be used.

Agents​

Agents represent who is asking the question. An agent reference determines how a request is routed and which tenant, namespace, and response rules apply.

Metadata-Rich Ingestion​

Content is enriched at ingestion time with metadata such as tags, object references, workflow context, and permissions.

This metadata materially improves retrieval accuracy, governance, and explainability.

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5. Typical Use Cases​

  • Public-facing chatbots over curated, approved content
  • Internal knowledge assistants for policies and procedures
  • Support deflection and assisted drafting
  • Workflow and procedural guidance
  • Secure AI search over sensitive or regulated documents

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6. Why Snowvault Is Different​

Snowvault is designed for controlled exposure.

It explicitly separates public, internal, and sensitive knowledge while allowing a single backend to serve many agents, each with different roles and permissions.

Rather than replacing existing systems, Snowvault sits behind them — providing governance, traceability, and auditability for AI-driven retrieval.

Snowvault treats knowledge as a managed, governed asset, not as an emergent by-product of prompts or models.