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Work

I design and ship agentic AI systems for German industry — lead engines built on live CRM data, production logistics platforms, research-grade network models.

Case studies

In production Employer Python / Microsoft Dataverse / Embeddings / FastAPI

ICP Engine & Data Enrichment

Context A German architectural glazing manufacturer sells through three distinct sales channels, each with a different ideal customer. Profiling was manual, static, and disconnected from the CRM.

System The engine reads customer records from Dataverse, verifies each company's online identity, and enriches records with digital-footprint and advertising-behavior signals. From the enriched base, channel-specific ideal-customer profiles are compiled on demand using analyst-selected filters — a living profile instead of a quarterly slide.

Architecture Python service against the Dataverse API; embedding-based similarity for profile matching — an approach first proven in a prototype that identified architecture firms likely to specify a specific timber product line; enrichment adapters feed a common schema. Output profiles are consumed directly by the lead-generation crawler.

In production Employer Python / Agentic workflows / Dataverse

Lead Generation Crawler

Context A profile is only worth what it finds. The channel ICPs needed to become pipeline without a research team doing manual prospecting.

System Takes a filtered profile from the ICP engine, scans the open web for matching companies, scores candidates against the profile, and writes qualified leads into the CRM with the evidence that qualified them attached. CRM feedback on lead quality flows back to sharpen scoring.

Architecture Agentic scraping workflow with per-source adapters, scoring against profile embeddings, idempotent CRM writes.

In production Client work Django / PostgreSQL / Railway

Logistics Operations Platform

Context A German logistics SMB ran its order lifecycle on spreadsheets and email: no roles, no audit trail, documents assembled by hand.

System Complete order management — creation through fulfillment — with role-based permissions for staff, customers, and management, and automated PDF document generation for the paperwork logistics runs on.

Architecture Django + PostgreSQL, deployed on Railway. Built solo, end to end: data model, permission system, document pipeline, deployment.

In production B.Sc. thesis Python / R / qgraph / bootnet / glasso / Neo4j / Streamlit

Computational Belief Modeling

Context How is a belief network structured — and how does that structure shift across contexts like institutional trust? The thesis modeled belief–affect–behavior structure in COSMO COVID-19 survey data.

System A reproducible Python/R pipeline estimates Gaussian Graphical Models (EBICglasso) across six survey contexts, computes bootstrap edge stability and focal-belief embeddedness, and encodes every network instance into a Neo4j belief knowledge graph for structured cross-context comparison.

Architecture Analysis in Python and R (qgraph, bootnet, glasso); Cypher import pipeline for the knowledge graph; public Streamlit app for interactive exploration including a Conditional Mean Shift explorer.

Outcome Graded 1.0. Live demo public.

GitHub ↗ Live demo ↗

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