Optical Character Recognition Experts in Germany

• Generative AI products for corporate clients, owned from strategy through specification to production.
• Central strategy, local configuration: multi-tenant AI assistant for occupational pension schemes (bAV), delivered as an interactive avatar with text and voice path. Three tenants run on one codebase, each with its own conversation guide, while the knowledge base, guardrails and escalation paths stay central
• Versioned, AI-ready knowledge base composed into a tenant-agnostic voice context and tenant-specific text prompts — the configuration layer that keeps local adaptation from forking the product
• Conversational design: answer limits, scope and off-topic handling, anti-hallucination rules, escalation and lead handover to human advisors
• Five eval suites as a quality gate before any prompt or model change (anti-hallucination, LLM-as-judge failure modes, multi-turn consistency, voice KPIs, action vocabulary with confusion matrix); user test with 10 testers (Hamburg, 07/2026) drove the rework from alpha to beta
• Coordinated external developers, compliance and client stakeholders; GDPR-compliant EU stack, IDD-compliant, EU AI Act classification documented
• Second product line: white-label social media generator for consultancy chilli mind (CH/DE) — one codebase, per-client branding and configuration
• Results: 239+ deployments and a pilot with corporate customers · 108+ deployments for the white-label product · repeatable pattern for multi-tenant AI products in a regulated environment

• I support medium-sized clients with their funding needs.
• I evaluate operational business processes and translate complex legal and regulatory requirements into business requirements, target models and actionable roadmap epics.
• I use Generative AI tools and automation in a targeted way to efficiently create requirements documentation, process analyses, evaluations, meeting preparation materials, customer analyses and decision papers.

AI-supported document processing and inventory management integration

Design and development of an AI-supported application for the automated processing of delivery and invoice documents, connected to SelectLine ERP. Documents are analyzed using AI, matched with orders and line items, and prepared for posting goods receipts.

IMPACT:

• Automated extraction of structured order, delivery and invoice data from PDF and image documents
• Automatic and manual mapping of documents to orders and order line items
• Integration with SelectLine ERP for order import, status synchronization and goods receipt postings
• Traceable processing through separate analysis, mapping and posting processes as well as technical logging
• Development of a containerized end-to-end architecture with AI analysis, workflow automation and relational data storage

KEYWORDS: AI, document analysis, OpenAI, n8n, SelectLine ERP, FastAPI, Python, JavaScript, MariaDB, Docker, REST API, PDF, OCR, mapping, inventory management, goods receipt, workflow automation

The program supports the comprehensive realignment of the German Consumer Panel Services business. It combines a significant panel boost with the reprocessing of historical data and the integration of new receipt data. By significantly expanding and stabilizing the panel with the involvement of external partners, the aim is to improve the validity of the data base and create a reliable foundation for methodology, weighting and customer reporting. At the same time, historically grown processes for data delivery, OCR, matching, item QC, methodology and reporting are being harmonized, further developed technologically and reorganized. The goal is a scalable end-to-end landscape with higher data quality, clear responsibilities, reliable governance and sustainably manageable operational processes.

• Overall management of the restatement program, including the integrated roadmap as well as milestones, dependencies, risks and management decisions.
• Coordination of the panel boost and alignment of the required data deliveries, quality requirements and prerequisites for methodology, weighting and reporting.
• Alignment of business, product, data science, technology, operations and external partners around a shared target picture, aligned priorities and an integrated approach.
• Design of the organizational change triggered by the fundamental realignment of the data base, methodology and management logic, which has a lasting impact on established decision-making and collaboration patterns.
• Establishment and further development of governance, reporting and escalation structures as well as program-wide monitoring and operational processes for reliable management and sustainable handover.
• Management of critical data, technology and provider dependencies, including reprocessing, OCR transition and the timely synchronization of delivery, testing, methodology and reporting.
• Orchestration of international collaboration with teams and stakeholders in Germany, the United Kingdom, Portugal and Romania, as well as with external suppliers in Germany and Austria.

Impact Areas and Expertise: Program & Delivery Leadership, Business & Technology Alignment, Organization & Transformation, Governance & Sustainable Operations, Strategy & Target, Methodic Leadership, Transformation & Change Leadership, Executive Advisory

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• Objective/Motivation: PAYONE urgently needs a 360° view of its customers. So far, PAYONE has had no company-wide master data strategy. It is not possible to identify customers across all relevant systems.

The organization is to be enabled to identify customers across all relevant systems. Establishing the basis for master data management at PAYONE

• Challenge: Due to company acquisitions, the system landscape is very heterogeneous. The company is highly dynamic and burdened with many system harmonization and integration projects, meaning that resource bottlenecks and changes in project priorities repeatedly create an almost impossible task.

Due to BaFin findings, the project has a central task and role. The first focus is the migration of all customers, including their AML/KYC data, from the master data backend systems to Salesforce. This is intended to resolve one of the largest findings and establish the corresponding ODD/EDD processes.

In addition, customer data must be harmonized in Salesforce. Previous migrations resulted in duplicate customer records in some cases. The aim is therefore to maintain only one customer in Salesforce and, using the relevant information from the backend systems, also be able to identify which products the customer uses and in which processing systems the customer obtains PAYONE services.

Project: ONE Customer

Budget: €1.5 million

Team: 10/30 employees (full-time/part-time); 4 vendors/providers

Integration: 8 (subsystems/interfaces)

Applications: Salesforce; SAP S4/HANA; in-house developments

Tools: MS Office; Jira, Confluence, SharePoint

Methods: Hands-on; Agile (SAFe); Prince2

• Led a 16-person finance and accounting organization during a restructuring phase; strengthened closing discipline, financial control, cash transparency and accountability.
• Supported cost programs with approximately €2 Mio. in annual savings; established regular KPI reviews for P&L, working capital, POC and cash topics.
• Responsible for monthly, quarterly and annual closing under HGB/IFRS, management/group reporting, audit, tax and GoBD compliance.
• Finance automation with clear business benefits: 13-week liquidity planning, cash flow reporting, P&L versus plan, variance analysis and bank reconciliation; reduced manual work by 40–60 %.
• Improved accounts payable processes through OCR-supported invoice processing and three-way matching with exception handling.
• Sparring partner to management on restructuring, liquidity, working capital, risks and process simplification.

• Interim PO for a DiGA-listed digital therapy platform, ensuring compliant product development in a highly regulated SaMD environment (BfArM, BSI) while maintaining delivery pace.
• Challenge: Regulatory complexity combined with hands-on delivery responsibility and only one week of onboarding time.
• Impact: Introduced AI-assisted product development workflows with clear quality standards and reinstated regular short release cycles for faster, more precise delivery. Currently optimizing product development workflows and reducing AI-generated bloat in tickets and documentation.
• Introduced an OCR-based workflow in Langdock that automates therapy plan creation from prescription images.

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

• ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.

• Self-employed · Berlin, Germany
• Co-founded the company and own the entire technical side: product architecture, backend, frontend, infrastructure and operations.
• Designed and built the Automated Booking System (ABS) as well as the core platform and payment logic.
• Development of AI-powered SaaS products, from architecture through backend and frontend to production operation.
• Technical consulting on product architecture, data protection and permissible automation.
• Building, running and monetising my own API products for AI agents, each available as a REST interface and as an MCP server.
• Full ownership of architecture, infrastructure, billing, legal texts and go-to-market.

Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.

• Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
• Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
• Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
• Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.

Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.

• Designed and developed AI-powered document processing solutions using Python, OCR, NLP, and Large Language Models (LLMs) to automate extraction, validation, and classification of financial documents, reducing processing time by 75%.
• Built intelligent multi-stage workflow automation pipelines integrating AI services, machine learning models, and enterprise systems to streamline financial operations and improve data quality.
• Developed reusable AI-driven transformation frameworks capable of processing structured and unstructured document formats (XML, CSV, JSON, TXT, DAT) and normalizing them into unified business schemas.
• Designed and developed Python-based REST APIs and backend services supporting enterprise finance applications and high-volume data processing workloads.
• Built scalable data synchronization pipelines between Oracle CFIN and SQL databases, incorporating machine learning models for cash-flow forecasting and AP/AR anomaly detection.
• Architected and deployed Apache Airflow workflows to orchestrate AI-powered data pipelines, automating end-to-end processing from document ingestion through financial system integration.
• Led the migration of critical enterprise integrations from MuleSoft to Python-based services, improving maintainability, performance, and operational flexibility while preserving complete data integrity.
• Managed the full API lifecycle including solution design, implementation, documentation, deployment, monitoring, and production support for mission-critical financial systems.
• Collaborated directly with finance stakeholders to identify business challenges, define solution requirements, and deliver measurable operational improvements through automation and AI-driven workflows.
• Worked closely with cross-functional engineering and business teams to rapidly iterate on features, improve processes, and drive successful adoption of AI-enabled solutions.
• Provided technical leadership through architecture reviews, technology decisions, code reviews, and engineering best practices across integration and automation initiatives.
• Mentored developers, established coding standards, and contributed to improving software quality, maintainability, and delivery effectiveness across projects.
• Provided production support during critical month-end and quarter-close financial processes, performing root-cause analysis and implementing rapid fixes to ensure system reliability and data accuracy.

• Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
• Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
• Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
• Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.

Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.