I bridge the disciplines of Mechanical Engineering, Software Engineering, and Artificial Intelligence to transform complex engineering processes into robust, scalable software systems.

My work focuses on a simple but challenging principle:

Technical knowledge should not remain locked away in Excel spreadsheets, PDF files, or isolated tools. It should be structured, traceable, reusable, and accessible in a smart way.

I design and develop solutions that connect engineering methodology, mathematical models, technical data, software architecture, automation, and AI.

Transforming established engineering processes into structured digital workflows.

• Engineering calculation systems
• Digitalization of technical standards
• Engineering data platforms
• Calculation and validation engines
• Automated engineering workflows
• Traceable and reproducible results

Designing software architectures for technically complex applications where correctness, maintainability, data integrity, and extensibility matter.

• API-driven architectures
• Database-centric engineering applications
• Modular software systems
• Multi-user applications
• Validation and business-rule engines
• Integration between engineering systems

Connecting engineering data and software workflows with simulation environments.

• Material data pipelines
• CAE/FEA data structuring
• Engineering model automation
• ANSYS integration
• Automated data exchange
• Simulation-oriented workflows

Applying AI where it provides measurable value rather than using it simply as an interface.

Areas of interest include:

• Local and on-premise LLMs
• AI-assisted engineering data extraction
• Technical document processing
• Intelligent tool selection
• AI-driven workflow orchestration
• Retrieval and structured knowledge systems
• OpenAI-compatible AI architectures

A centralized platform for managing, validating, analyzing, and distributing engineering material data.

The system combines structured engineering databases, AI-assisted document processing, calculation logic, visualization, compliance-oriented validation, and CAE integration.

• AI-assisted technical datasheet extraction
• Structured material-data management
• Engineering data validation
• Stress-strain curve generation
• S-N curve generation
• FKM-oriented validation and auditing
• Engineering data traceability
• ANSYS export workflows
• Docker-based on-premise deployment

The architectural objective is to establish a reliable engineering data foundation that can serve as a common interface between material information, calculations, simulation, and downstream engineering applications.

A database-driven application for structured product cost and manufacturing cost analysis.

The project addresses the limitations of fragmented spreadsheet-based cost models by introducing a centralized software environment for:

• Product cost structures
• Manufacturing cost analysis
• Structured technical data
• Multi-user workflows
• Automated calculations
• Data consistency and traceability

The broader objective is to turn cost engineering from a collection of individual spreadsheets into a maintainable digital process.

An interactive calculation platform for mechanical strength verification based on the FKM Guideline, with a focus on shaft components and structural shoulders.

The project explores how complex engineering methodology can be transformed into a software architecture that provides:

• Structured engineering inputs
• Automated calculations
• Validation logic
• Reproducible results
• Transparent calculation workflows
• Digital engineering documentation

The fundamental challenge is translating engineering knowledge into deterministic, testable, and maintainable software logic.

I am particularly interested in the boundary between engineering methodology and software architecture.

A typical transformation looks like this:

Engineering Knowledge
        │
        ▼
Standards / Guidelines / Models
        │
        ▼
Mathematical & Physical Logic
        │
        ▼
Validation & Domain Rules
        │
        ▼
Software Architecture
        │
        ▼
APIs / Databases / User Interfaces
        │
        ▼
Automation
        │
        ▼
AI-Assisted Workflows

The objective is not simply to reproduce an existing spreadsheet in a browser.

It is to extract the underlying engineering knowledge, formalize it, validate it, and build a software system around it.

I am particularly interested in AI-driven software architectures where the user describes an objective rather than operating a collection of individual tools.

Instead of:

User
 ↓
Find the correct application
 ↓
Find the correct function
 ↓
Enter parameters
 ↓
Run calculation
 ↓
Interpret results

the long-term vision is:

User describes the objective
          ↓
AI understands the context
          ↓
AI identifies the required capabilities
          ↓
AI selects the appropriate tools / plugins
          ↓
Tools execute the workflow
          ↓
AI validates and interprets the result
          ↓
User receives an actionable answer

This creates a fundamentally different interaction model for engineering and industrial software:

Intent → Reasoning → Tools → Validation → Result

Python · FastAPI · Flask · SQLAlchemy · Uvicorn

PostgreSQL · SQLite · Structured Engineering Data

JavaScript · HTML5 · CSS3 · Bootstrap

Local LLMs · Ollama · OpenRouter · OpenAI-compatible APIs

Docker · Git · CI/CD · On-Premise Deployment

Mechanical Engineering · Structural Mechanics · FKM · CAE · FEA · Engineering Data Management

Modern engineering software rarely exists in isolation.

I am interested in connecting engineering applications with existing corporate and industrial ecosystems, including:

• ANSYS
• SAP
• PTC Creo
• Engineering databases
• Manufacturing systems
• Internal APIs
• Document and knowledge repositories
• Local AI infrastructure

The goal is to create interoperable engineering systems rather than isolated applications.

Digital Engineering
Engineering Automation
Engineering Data Management
CAE / FEA Integration
Calculation Software
Industrial Software Architecture
AI-Assisted Engineering
Local / On-Premise AI
Intelligent Workflow Automation
Enterprise System Integration

My current work revolves around one central question:

How can complex engineering knowledge become software that is easier to use, easier to maintain, and capable of intelligently assisting the engineer?

This includes exploring AI-powered engineering tools, modular plugin architectures, local AI systems, intelligent workflow orchestration, and domain-specific engineering applications.

I am interested in collaborating on technically challenging projects at the intersection of:

Mechanical Engineering × Software × Data × AI

Particularly:

• Engineering software
• Industrial digitalization
• CAE / FEA automation
• Engineering databases
• Technical data platforms
• AI-assisted engineering
• Intelligent workflow systems
• API and enterprise integration
• Local / on-premise AI

• 💼 LinkedIn:
• 📧 Email: [email protected]