Building IT operations documentation and making it usable with AI
Capture operational knowledge in versioned Markdown or a wiki, so your team can use AI to search, answer questions and analyse incidents.
- Public sector
- Critical infrastructure (KRITIS)
You make operational knowledge usable with AI by first documenting it thoroughly and storing it in a versioned Markdown or wiki structure. An AI-assisted search builds on this, answering questions with source references, finding runbooks and helping with incident analysis. FlixConsulting develops the documentation with your team, sets up the search and tests its value in a pilot.
Where organisations stand
- Operational knowledge sits in people's heads, tickets, file shares and old wiki pages. It is often unclear which version is current.
- Up-to-date runbooks are missing when incidents occur. The team asks the few people who know a system in detail.
- Management wants to use AI in operations. It is still open which content an AI system may read, where it runs and who is accountable for its answers.
How we work
- 01
Analysis
We record existing documentation, wikis, tickets and runbooks, assess how current they are and where gaps exist, and clarify the protection requirements of the content.
- 02
Target architecture
We define a Markdown or wiki structure with templates for systems, runbooks and incidents, together with versioning, access rights and where the AI search runs.
- 03
Delivery
We develop the documentation with your team, starting with a clearly defined area, and set up the AI search. A pilot tests it against test questions defined in advance.
- 04
Handover to operations
We embed documentation maintenance in change and incident processes, hand over templates and rules and train your teams.
What changes afterwards
- Operational knowledge is versioned in a consistent structure and no longer depends on individual people.
- Your team answers operational questions with AI and checks every answer against the cited source documents.
- Maintenance is a fixed part of change and incident management. This keeps the documentation current for people and AI.
Questions about this use case
Why a versioned Markdown or wiki structure?
Markdown is plain text with simple structure. People read it directly, and an AI system processes it without complex conversion. In a versioned repository, every change is traceable and can be reviewed before approval. An existing wiki such as Confluence can remain the source if structure and access rights are suitable.
How does your team check whether the AI's answers are correct?
Every answer cites the documents it is based on. Your team checks it there before acting. In the pilot, the team also evaluates a fixed list of test questions from day-to-day operations. If an answer is missing or wrong, this may point to a gap in the documentation. Your team then closes it.
Let us talk about your project.
No obligation, confidential and with a concrete next step.