Human-in-the-Loop Expert in Germany

• Designed and developed the central company platform LINK: tasks, CMS, shop, academy, image database, fleet records, and AI assistant in a shared data model on PostgreSQL.
• Supported the transformation of established standalone solutions into a cloud architecture; analyzed the existing data landscape and migrated it step by step.
• Integrated internal and external data sources via APIs and webhooks: Microsoft 365, shop with accounting integration, and manufacturer data.
• Implemented an AI assistant for knowledge access and document analysis, including an approval process before sending.
• Migrated and rebuilt a 49-page website with SEO in mind; launched data-driven tools (Drone Finder, Permit Finder) and lead forms.
• Served as the link between management, operations, and IT: analyzed processes on-site, translated requirements into technical solutions, and trained a team with little digital experience using real work.

Through Sourcegarden GmbH, ongoing engagement. Transitioning software development to agentic development in phases, from setup and pilots through transformation and handover.

"Reasoning Models: From LLM Foundations to NOC Automation."

Following the Vaillant engagement, established independent practice focused on senior SAP advisory and building ASTRA — a methodology, not a tool. ASTRA is independent and unbiased: the same four pillars serve CIO / Business Leadership, SI / SAP Practice, and Programme Director / Principal with equal weight. No audience is primary.

ASTRA serves three audiences with equal weight — CIO / Business Leadership · SI / SAP Practice · Programme Director / Principal. ASTRA is independent and unbiased — it does not sit inside any SI practice and does not serve any single audience as primary customer. Each audience receives a role-calibrated output from the same analysis.

One methodology, two outcomes:

OUTCOME 1 — SAP TRANSFORMATION GOVERNANCE:

Any active SAP programme: ECC→S/4HANA Greenfield, Brownfield, Global Rollout, AMS. The four pillars answer the four questions that determine whether a transformation succeeds — for any of the three audiences.

• Are business requirements clean — or is the SI building what SAP standard already covers? (Functional Auditor → all three audiences)
• Is the custom object inventory healthy — or accumulating debt that blocks upgrades? (RICEFW Auditor → Director and CIO)
• Is the SteerCo reporting accurate — or is leadership seeing a Watermelon? (Truth Engine → CIO and Director)
• Are people ready for the system they are receiving? (Human Signal → CIO and Director)

OUTCOME 2 — AUTONOMOUS ENTERPRISE READINESS:

Any programme deploying Joule, pursuing RISE, or planning an AE migration. The same four pillars answer the questions SAP does not — again, calibrated per audience.

• Which custom objects block the 224 Joule AI agents? (RICEFW Auditor → SI gets remediation paths, Director gets verdicts, CIO gets financial impact)
• Is the Fiori deployment complete enough to activate Joule? (Truth Engine → all three audiences)
• Is the workforce trained for AI-native working? (Human Signal → CIO and Director)

Four pillars (each serves all three audiences):

• Functional Auditor: Clean Core Score 0–100. Section A (CIO): KG fidelity risk in €. Section B (Director): SI challenge questions, man-day saving. Section C (SI): Language Translation Ledger, SAP alternatives.
• RICEFW Auditor: Joule API Pathway Audit, Clean Core Level A–D, Technical Debt Index 1–10. Section A: financial impact of blocked AI agents. Section B: RETIRE/MIGRATE/REDESIGN/KEEP verdicts. Section C: BTP remediation paths.
• Truth Engine: Watermelon Detection, Fiori Deployment check, Programme Truth Score 0–100. Section A: 3 SteerCo questions. Section B: 48-hour action list. Section C: vendor friction map.
• Human Signal: three-stage Fiori adoption model, HITL Policy readiness, Human Signal Score 0–100. Section A: GUI training weight, AE adoption financial risk. Section B: go-live recommendation, stakeholder resistance map.

On-Demand SAP Advisory:

• SAP Programme Management — end-to-end governance, factory management, multi-stream delivery, vendor oversight, SteerCo reporting for Greenfield, Brownfield, Rollout and AMS programmes.
• Delivery Management — RICEFW oversight, Data Migration governance, Release & Cutover management, Hypercare, SLA/KPI management.
• Due Diligence & Landscape Assessment — SAP landscape review, AS-IS/TO-BE roadmap, RFP structuring, SI selection advisory.
• IT Strategy & CIO Advisory — transformation roadmap, Clean Core strategy, Autonomous Enterprise readiness planning.
• Programme Recovery — distressed programme assessment, rapid governance setup, escalation resolution.

Operating through senior professional networks across EMEA.

AI Assistance and Target Vision for Partially Autonomous Energy Portfolio Management

Building an AI control layer directly on the up-to-date daily live portfolio of a large energy provider — not as an isolated pilot, but as an operational extension of the existing DB1 and portfolio management. The goal is the gradual development from assistance through monitoring/alerting to analysis agents with Human-in-the-Loop approvals, supplemented by a role-specific System of Engagement alongside the BI System of Record. At the same time, the business case, target vision and management pitch compared with static monthly reporting are being developed.

• AI control layer: Design and build on the existing live portfolio data product (several million contracts) — development stages assistance → monitoring/alerting → agents with Human-in-the-Loop approvals.
• LLM-supported data analysis: Semantic queries, SQL/tool integration and additional RAG components based on portfolio, plan-versus-actual and data quality data (Azure OpenAI, Snowflake), with drill-downs to individual contract level.
• Analysis agents: Multi-stage agents for variance and driver analyses of churn, price adjustments, volumes and procurement costs.
• Views concept: Role-specific interfaces for business units, management and C-level as a System of Engagement alongside the BI System of Record.
• Business case & pitch: Target vision and cost-benefit argumentation compared with static monthly reporting.
• LLM setup (privacy & security): Coordination with IT Security and Data Protection — EU region, data separation and approval processes.
• Agent architecture: Multi-stage agent pipelines (analysis → validation → summary) with documented data sources, tool calls, review steps and source references for each statement.
• Data foundation: Built on the up-to-date daily DB1 data product (Snowflake, dbt) — portfolio, plan-versus-actual and data quality metrics as the common basis for all AI analyses.
• Guardrails & evaluation: Evaluation and approval processes for LLM responses relating to management-relevant statements — test sets, metrics and human review.
• Prototyping & validation: Iterative validation of agent responses with the business unit — test question catalogue, feedback loops and response quality for each release.
• Roadmap & development stages: Detailed stages from assistance → monitoring/alerting → partially autonomous management, including transition criteria and governance for each stage.
• Integration: Integration into the existing BI and data landscape — BI remains the System of Record, while the AI layer provides interactive drill-down paths as the System of Engagement.
• Enablement: Enablement of business users — prompting guides, training and an operating model for ongoing use.
• Management: Coordination of business units, Data Engineering, IT Security and Data Protection.
• Change Management: Communication and expectation management with business units and management throughout the development stages.

Results:

• Built on an existing up-to-date daily data product with several million contracts
• Established an LLM setup coordinated with Data Protection and IT Security in the EU region, including data separation and approvals
• Defined three development stages through to partially autonomous management
• Designed role-specific views for business units, management and C-level
• Developed the business case and management pitch for the development stages
• Established an iterative response-quality validation process with the business unit
• Designed the operating model for assistance operations and initiated validation

Stack: Azure OpenAI, Azure AI Foundry, Snowflake, dbt, React, TypeScript, Entra ID, RAG, Agentic AI, analysis agents, Human-in-the-Loop, Prompt Engineering, LLM Evaluation, LLMOps, GDPR / EU region, Azure DevOps, Python, SQL, Change Management

SEO Content Marketing Management, strategic consulting for SEO content projects, human in the loop editorial work

Tools:

• gsc, ga4, sistrix, semrush, helium10, chatGPT, gemini, perplexity

Results:

• improved content visibility in search engines and AI search

• Own the 12–18 month roadmap and key outcomes for Elli's enterprise customer platform, covering onboarding, pricing, billing, analytics and broader platform modernization; redesigned the Fleet onboarding funnel to double conversion, supporting a projected €20.7M revenue uplift by 2028.
• Lead the broader Energy Intelligence product and directly own its AI/ML, asset and portfolio-optimization capabilities, including MLOps and safe strategy deployment, strategy lifecycle management and backtesting; delegated data and V2G integration roadmap ownership to a new PO as the platform scope expanded.
• Built a Human-in-the-loop GenAI/RAG support workflow, increasing L1 resolution by 24%, routing accuracy to 91%, and reducing L2 workload by 30%.
• Introduced standardized data contracts and a self-service Python toolkit for traders and Data Scientists, increasing platform adoption by 15% and reducing support effort by 50%.
• Built and scaled a real-time orchestration product from 32 to 3,000+ endpoints across four markets, growing recurring revenue from €1.4k to €56.3k MRR.
• Developed product and AI capability across the organization, training 20 PMs on RAG, agents and prototyping; mentoring a junior PM and coaching an Enterprise Platform Tech Lead toward Product Management ownership.

• Design and implementation of AI automations focused on voicebots, LLM workflows, and integrations with existing business processes.
• Custom automations using n8n, REST APIs, and webhooks; integration of telephony, calendar, CRM, and data systems.
• Prompt engineering, quality assurance, and error handling, including structured outputs, guardrails, monitoring/logging, and human-in-the-loop.

• Analyze the root causes of complex functional, technical, and data-related errors in PowerCloud across process, booking, and system boundaries.
• Identify automation and straight-through processing opportunities at the interface between business teams, IT, and external service providers.

• Implemented customer-specific data, GTM, web, and automation systems from requirements gathering through controlled deployment.
• Built multi-tenant research and delivery platforms with TypeScript/Node.js, React, APIs, LLM integration, and automated checks.
• Connected HubSpot, Notion, Clay, Smartlead, n8n, and Make with traceable handoffs, deduplication, error paths, and approvals.

• Provided technical leadership for a 4-engineer team delivering 3 major client platforms in 12 months with microservices architecture and scalability solutions — 100% of scoped majors shipped ahead of schedule vs. planned milestones (baseline: prior releases often slipped 1–2 sprints).
• Ran AI model evaluation and model outputs evaluation on LLM/AI vendor APIs: safety, completeness, instruction adherence, and groundedness review before go-live; cut escaped bad outputs in AI-integrated release checklists from recurring UAT findings to near-zero on final promote.
• Drove API development and performance optimization for payment, exchange, and AI services; fail-closed error handling and payload validation reduced integration rework cycles by ~35% vs. the first AI integration pass.
• Applied software testing, testing frameworks, code quality assurance, and code refactoring with continuous integration gates; first-pass PR acceptance improved across the team and production hotfixes on AI adapters dropped noticeably after review standards landed.
• Owned DevOps practices: Docker, GitHub Actions, Jenkins-compatible pipelines, and version control workflows — cut deployment time ~50% vs. pre-automation baseline and stabilized releases across 3 client environments.
• Implemented verifier/oracle-style pass-fail checks in container sandboxes (Harbor/Terminal-Bench aligned); wrote technical documentation so failures cleared in one review cycle.
• Led cross-functional collaboration with product and client stakeholders; translated AI evaluation scores and risk findings into plain-language briefs for non-technical partners, unblocking go/no-go decisions without extra engineering meetings.
• Used agile methodologies for sprint planning and backlog ownership; mentored engineers so mid-level contributors owned AI adapter modules independently by mid-engagement.

• Claude Code subagents, MCP, Pydantic V2, pytest, bandit

• Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.

• Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.

Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.

• Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
• Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
• Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
• Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
• Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.

• Led the global HRIS transformation program (16 country units): program setup, PMO, phase planning, status reporting, and steering up to partner/SteerCo level
• Built an HR readiness assessment framework (6 dimensions, weighted scoring) as a decision basis for implementation and AI maturity — mandated by the board as the project outcome
• HR process architecture: analysis of 80+ level-3 processes and 87 ISO process documentations across 13 country clusters; gap analysis and harmonization roadmap
• Designed agentic AI HR processes (hiring, onboarding, probation period management) on SAP S/4HANA Private Cloud and ServiceNow — human-in-the-loop design, process screening for agentic potential, decision paper at partner level
• HRIS market analysis: vendor screening, criteria framework, and evaluation logic for system selection
• HR target operating model: organizational analysis, archetype framework (9 dimensions), governance design for a global HR function
• Self-assessment tools and workshop formats for 16 country HR teams incl. nomination and stakeholder management; SharePoint project hub and communication architecture
• AI-supported consulting delivery: my own Claude-based working model for analysis, document creation, and project management — one consultant delivering program output