Built by Tippel
Every system below was designed, built, and shipped end-to-end by one engineer — from compliance-grade document AI to a live SaaS product. Read them as answers to one question: what does production-ready AI look like in practice?
GDPR Risk Analysis System
An AI system that analyzes contracts and processing agreements for data-protection risks — deployable fully on-premise, so sensitive documents never leave the company.
Details
Challenge
Reviewing contracts and processing agreements for GDPR risk is slow, expensive expert work — and most companies do it too rarely.
Solution
An AI system that analyzes documents, surfaces data-protection risks with references to the relevant articles, and guides remediation workflows — deployable fully on-premise so the sensitive documents never leave the company.
Why it matters
Exactly the class of AI German companies want: sensitive data, clear ROI, and architecture that satisfies the Datenschutzbeauftragter.
AI Phone Agent
A voice agent that answers calls, understands requests in German, books appointments, and hands over to a human when it should — currently in development.
Details
Challenge
Businesses miss calls and lose customers; call centers are expensive and hard to staff.
Solution
A voice agent that answers calls, understands requests in German, books appointments, and hands over to a human when it should — currently in development.
PromptPageAI — AI Website Generation
A production SaaS that generates complete, hosted business websites from a prompt — live with paying users, built and operated end-to-end.
Details
Challenge
Small businesses need professional websites but can't afford agencies or the time to build them.
Solution
A production SaaS that generates complete, hosted business websites from a prompt — domain, SSL, SEO basics, and GDPR-compliant hosting included, delivered in 24 hours.
Why it matters
Proof of end-to-end shipping ability: LLM pipeline, payments, hosting infrastructure, and operations, run as a real product with paying users.
KQL Generator for Microsoft Sentinel
A multi-agent system that generates, validates, and refines detection queries for Microsoft Sentinel from natural-language intent.
Details
Challenge
Security analysts spend significant time hand-writing and debugging KQL queries for threat hunting and detection engineering in Microsoft Sentinel.
Solution
A multi-agent system that generates, validates, and refines KQL from natural-language intent — one agent drafts, one checks schema correctness against the workspace, one refines based on result quality.
Why it matters
Agentic AI applied to a domain where correctness is non-negotiable — with validation loops instead of blind generation.
3D Medical Imaging AI — Colon Cancer Classification
Custom deep learning models for 3D biomedical image classification, trained on high-performance computing infrastructure.
Details
Challenge
Classifying 3D biomedical image data requires custom architectures and serious compute — off-the-shelf models don't transfer.
Solution
Custom deep learning models for volumetric data with fully automated GPU/HPC training pipelines — from data preprocessing to reproducible experiment tracking.
Why it matters
Demonstrates the deep end of the stack: not just calling LLM APIs, but designing and training neural networks on high-performance computing infrastructure.
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