Bachelors & masters theses

Contribute to the advancement of the Versio.io platform with the findings from your bachelor's or master's thesis

At Versio.io, we tackle enterprise IT complexity with AI and automation. For your bachelor or master thesis, we provide a high-impact environment where research meets industrial reality. Leveraging our infrastructure and expert mentorship, you'll bridge the gap between academic rigour and innovation. Ready for a deep dive into the Digital Twin?

Topics overview

The Agentic CMDB: AI integration via Model Context Protocol (MCP) and CLI

Emerging protocols like MCP allow AI agents to interact directly with technical systems. This thesis aims to build a functional CLI-based bridge that empowers AI agents to query and validate the Versio.io inventory in real-time.

Thesis contents:

Research on the Model Context Protocol (MCP) for standardized data exchange with AI agents
Development of a CLI tool to bridge local developer environments with the Versio.io API
Prototyping an AI assistant that uses MCP to interact with the IT inventory

Automated derivation of regulatory compliance rules using AI

Modern regulatory frameworks like NIS2 or DORA pose significant challenges for manual implementation. This thesis explores how Artificial Intelligence can be used to automatically translate complex legal requirements into actionable technical rules within Versio.io.

Thesis contents:

Analysis of regulatory requirements (NIS2, DORA, BAIT) and their mapping to technical metrics
Development of an AI-based concept (LLM/RAG) to derive hardening rules from formal documents
Implementation of a prototype to automate rule updates in the Versio.io Governance Engine

Data-driven Software License Management based on Versio.io Inventory

Software license compliance is only as accurate as the underlying discovery data. This thesis explores how the granular, real-time inventory captured by Versio.io can be systematically transformed into an automated license management system to bridge the gap between actual installations and contract entitlements.

Thesis contents:

Mapping of Versio.io CI attributes and inventory classes to specific licensing metrics (e.g., CPU cores, installation paths, SaaS seats)
Development of an automated reconciliation logic that matches the live inventory state against a repository of license entitlements
Creation of a data-driven dashboard for identifying shelfware and optimization potentials based on historical inventory trends

Maximizing the operational value of Kubernetes & Container inventories

Pure discovery of containers is no longer enough for modern enterprise IT. This topic investigates how to enrich Kubernetes data with business context, security metrics, and cost transparency to provide a higher value for stakeholders.

Thesis contents:

Analysis of dependencies between microservices and their underlying infrastructure in Versio.io
Integration of security and cost aspects into the container inventory process
Design of automated change monitoring scenarios for dynamic cloud-native environments

AI-powered CMDB: Enhancing inventory data processing and insights within Versio.io

A modern CMDB contains vast amounts of complex, high-velocity data that can overwhelm manual analysis. This thesis focuses on integrating AI capabilities directly into the Versio.io platform to automate data processing, detect hidden patterns, and transform raw inventory logs into intelligent, actionable insights.

Thesis contents:

Development of AI-driven methods for automated data classification, normalization, and anomaly detection within the Versio.io data pool
Design of an integrated AI layer to identify complex relationships and predict impact patterns across different infrastructure layers
Prototyping a Natural Language Interface (NLI) to enable intuitive, conversational querying and analysis of the digital twin directly within the platform

Automated IT landscape scan and integration to inventory workflows

Complete visibility is the foundation of every CMDB, but hybrid environments make scanning a challenge. This topic focuses on designing intelligent, automated workflows to eliminate blind spots and ensure the highest data quality.

Thesis contents:

Analysis of scanning methods and identification of "blind spots" in hybrid infrastructures
Design of a flexible orchestration framework for managing inventory jobs and triggers
Development of automated data cleansing and quality assurance mechanisms

Multi-layer network topology inventory and visualization

Modern networks are composed of multiple layers, from physical hardware to virtual overlays. This thesis develops a concept to unify these levels into a single, dynamic network plan within the Versio.io ecosystem.

Thesis contents:

Modeling network components across physical, logical, and virtual layers in Versio.io
Development of logic for automated generation of dynamic network diagrams from scan data
Conception of an automated impact analysis based on the discovered network topology

Bidirectional integration between Versio.io and ServiceNow

Many enterprises use ServiceNow for ITSM but need the deep visibility of Versio.io. This research focuses on the conception and implementation of a bidirectional synchronization to ensure data consistency across both platforms.

Thesis contents:

Harmonization and mapping of Configuration Item (CI) classes between both systems
Development of a robust synchronization logic for data ownership and conflict resolution
Prototyping a use case where Versio.io compliance alerts trigger ServiceNow incidents

How you can contact us

We are happy to receive your applciation including your CV, certificates and references of your past projects. If you have any questions, Matthias Scholze is happy to speak to you: +49 (30) 22 19 86 51 or matthias.scholze@versio.io.