A Modern Alternative to Traditional Documentation Systems

For many years, organizations have relied on large documentation platforms to manage internal knowledge. Tools such as Confluence became the default location for everything from architecture documentation and operational runbooks to meeting notes and project knowledge.

However, as documentation grows, organizations often encounter the same challenges: information becomes fragmented, documents become difficult to locate, and valuable knowledge ends up scattered across multiple systems.

At the same time, advances in artificial intelligence have opened a new possibility: AI-assisted knowledge discovery.

Rather than storing everything in a single platform, many teams are now exploring how multiple specialized systems can be connected and made searchable through AI.

This approach creates a modular knowledge architecture where documentation, code, and files remain in their natural environments while AI provides a unified search interface.

The Core Idea

Instead of relying on one monolithic documentation system, knowledge can be distributed across several tools that each serve a specific purpose.

For example:

• a knowledge base for structured documentation
• a code repository for technical material
• a collaboration platform for files and documents

An AI assistant can then search across all of these sources using Retrieval Augmented Generation (RAG) and present relevant answers to users.

Conceptually, the architecture looks like this:

Users interact with an AI assistant, which retrieves information from multiple systems and generates responses based on internal documentation.

Typical components might include:

• BookStack for structured documentation (European alternative: XWiki, developed by a French company; strong enterprise wiki option)
• GitHub for code and technical documentation (European/self-hosted alternative: Gitea or GitLab CE; using GitHub is typically not a major concern if repositories are public documentation or contain non-sensitive material)
• Nextcloud for file storage and collaboration (European project headquartered in Germany, making it a strong option for organizations concerned about data sovereignty)
• AI models and vector search to enable semantic search across documentation

Together, these systems form a flexible knowledge ecosystem.

Structured Knowledge: The Documentation Layer

Structured documentation works best in a dedicated knowledge platform.

Tools such as BookStack provide a clear hierarchy for organizing knowledge:

Shelf → Book → Chapter → Page

For example:

Shelf: AI Strategy
Book: AI Governance
Chapters: Risk, Compliance, Model Lifecycle

Shelf: Application Management
Book: Robotics
Book: Incident Management
Book: Operational Runbooks

This structure makes it easy to maintain documentation such as:

• architecture overviews
• governance frameworks
• operational procedures
• onboarding documentation
• internal standards

BookStack is lightweight and easy to maintain. However, if an organization prefers a clearly European vendor ecosystem, XWiki is a strong alternative with enterprise-grade capabilities.

Technical Documentation and Code

Technical documentation often evolves alongside the codebase.

For this reason, many teams keep technical documentation directly in their code repositories.

A typical repository structure might look like:

/docs
architecture.md
ai-agent-architecture.md
sdlc-process.md

/src
/scripts
/config

This approach, often referred to as “documentation as code”, offers several benefits:

• version control
• transparent change history
• collaboration via pull requests
• easy automation through CI/CD pipelines

Many organizations use GitHub for this purpose (European/self-hosted alternatives include Gitea or GitLab CE; GitHub is often considered acceptable if repositories do not contain sensitive operational data and the organization values its ecosystem and developer tooling).

Files and Collaboration

Not all knowledge belongs in documentation pages.

Presentations, diagrams, spreadsheets, datasets, and reports still play an important role in everyday work.

A file collaboration platform such as Nextcloud works well for storing and sharing this type of content.

Typical folders might include:

AI
presentations
diagrams
datasets

Projects
project documents
meeting notes

Nextcloud is particularly interesting because it is an open-source platform developed in Europe and can be self-hosted, allowing organizations to maintain full control over their data.

For document editing within Nextcloud, tools such as Collabora Online (European alternative: ONLYOFFICE, developed by a company based in Latvia; Collabora itself is UK-based but widely used in sovereignty-focused deployments) can be integrated.

The AI Layer: Retrieval Augmented Generation

The key component that ties everything together is the AI search layer.

Using Retrieval Augmented Generation (RAG), an AI assistant can index content from multiple sources such as:

• documentation pages
• repository documentation
• file storage systems

When a user asks a question, the process typically works as follows:

1. The question is converted into a vector representation.
2. A vector database retrieves relevant documents.
3. These documents are provided as context to a language model.
4. The AI generates a response based on the retrieved information.

This approach allows the AI to answer questions using internal knowledge rather than general internet content.

A typical vector database might be Qdrant (European company based in Germany) or Weaviate (European alternative founded in the Netherlands).

Language Models

The language model itself can be chosen depending on organizational requirements.

Some organizations use OpenAI models (European alternatives include Mistral AI, based in France, or open models such as Llama; OpenAI is often acceptable for experimentation or non-sensitive use cases due to its maturity and ecosystem).

In environments with stronger sovereignty requirements, models can also be self-hosted.

Identity and Access

In larger environments, it is common to integrate identity and authentication services.

Typical solutions include:

• Keycloak (open-source identity platform originally developed in Europe)
• ZITADEL (Swiss identity platform with strong focus on modern identity architecture)

These systems support standard protocols such as OIDC and SAML, enabling secure integration across all components in the architecture.

The Result: A Unified Knowledge Interface

The result of this architecture is a knowledge environment where AI acts as the primary interface.

Instead of manually navigating different systems, users can simply ask questions.

The AI assistant retrieves relevant information from documentation, repositories, and file systems and presents the answer in context.

This approach offers several advantages:

• modular architecture
• flexibility in tool selection
• strong data ownership
• easier knowledge discovery
• reduced dependency on single-vendor platforms

Knowledge Management in the Age of AI

We are still early in the transition toward AI-assisted knowledge systems.

However, a clear pattern is emerging.

Modern knowledge architectures increasingly combine:

Knowledge Bases
+
Documentation as Code
+
AI-Powered Search

Traditional documentation platforms are not disappearing, but they are being complemented by a new layer: AI that helps people find and understand knowledge across systems.

In the long run, the way we interact with documentation may change significantly.

Instead of browsing through pages and folders, we will increasingly ask questions and let AI guide us to the answers.