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Christoph Lengowski · AI Product Owner & Senior IT Consultant

Making AI initiatives ready for decisions.

From the first use case to a safe release, I connect product ownership, requirements engineering, and AI evaluation – creating testable requirements, robust prototypes, and clear go/no-go decisions.

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Use Cases & AI RequirementsEvaluation & GuardrailsPrototyping & Delivery
8QA team members led
E2EIndependently delivered Playwright test automation
5+Years of delivery ownership
Christoph Lengowski

Enterprise AI with Product Focus

From an ambiguous AI idea to an assessable product concept – grounded in business value, clear requirements, and technical feasibility.

AI Evaluation & Quality-by-Design

Defining quality goals, evaluation criteria, and guardrails for AI systems. Risks, failure modes, and probabilistic outcomes become measurable before rollout.

AI Guardrails & Delivery Readiness

Designing traceable evaluation, approval, and feedback loops for AI products. Independently delivered Playwright and TypeScript test automation plus CI/CD experience support reproducible evaluations and safer delivery processes.

AI Requirements Engineering

Translating vague business ideas into prioritized use cases, data and context requirements, suitable RAG, LLM, or agentic architectures, and clear KPIs for probabilistic outcomes.

Rapid AI Prototyping & PoCs

Focused prototypes using local LLMs, RAG, and workflow automation validate value, data access, and technical risk before teams invest in scaling and integration.

AI Product Ownership

Prioritizing AI initiatives by business value, risk, and feasibility. Aligning domain teams, engineering, and stakeholders around measurable outcomes, clear decisions, and realistic delivery steps.

RAG & Knowledge Products

Design and practical evaluation of RAG and knowledge-management products. Personal PoCs using Docker, Open WebUI, Ollama, and LightRAG support informed decisions on retrieval, data quality, and system boundaries.

Certifications

Verified qualifications across product ownership, requirements engineering, AI quality, test management, and structured delivery.

ISTQB logo
2026

Certified Tester GenAI

Certible
View certificate
  • AI-assisted testing: understanding practical uses of generative AI in software testing
  • Prompting and test design: deriving test scenarios, test data, and test ideas with GenAI support
  • Risk awareness: critically evaluating AI outputs, quality safeguards, and responsible tool usage
ISTQB logo
2026

ISTQB Advanced Level Test Management (CTAL-TM)

ISTQB
View certificate
  • Strategic test planning: defining and steering test strategies plus risk management for complex systems
  • Team leadership and governance: leading test teams, monitoring KPIs, and improving test processes
  • Commercial focus: estimating effort and controlling budgets to maximize QA return on investment
ISTQB logo
2025

ISTQB Foundation Level

ISTQB
View certificate
  • Standardized methodology: strong command of the fundamental test process and internationally recognized terminology
  • Holistic test design: applying black-box and white-box test design techniques to find defects effectively
  • Quality mindset: ensuring high software quality through early testing activities across the SDLC
Professional Scrum Master I badge
2021

Professional Scrum Master I

Scrum.org
View certificate

Proof ID: 703391

  • Servant leadership: facilitating Scrum events and removing impediments to maximize team productivity
  • Agile transformation: reinforcing transparency, inspection, and adaptation across the organization
  • Coaching: helping the team self-organize and live the Scrum values and principles
Professional Scrum Product Owner I badge
2024

Professional Scrum Product Owner I

Scrum.org
View certificate

Proof ID: 991278

  • Business value maximization: prioritizing the product backlog strategically to optimize value
  • Stakeholder management: bridging business requirements and technical implementation effectively
  • Product vision: shaping clear product goals and measurable acceptance criteria
IREB CPRE Foundation Level badge
2021

IREB CPRE Foundation Level

IREB

Proof ID: 21-CPREFL-197026-20

  • Precise requirements analysis: eliciting, documenting, and validating functional and quality requirements professionally
  • Conflict management: moderating between stakeholder interests to avoid misaligned implementation
  • Specification excellence: producing clear, testable requirements for smoother delivery
PRINCE2 wordmark
2021

PRINCE2 Foundation

PRINCE2 / PeopleCert

Proof ID: GR656228305CL

  • Structured project management: understanding process-oriented methods for controlled project delivery
  • Business case focus: continuously validating the business justification throughout the project lifecycle
  • Roles and responsibilities: defining clear structures and escalation paths for efficient project execution
Profile

About Me

I combine product ownership, requirements engineering, QA leadership, and hands-on AI delivery to make robust product decisions.

As a Senior IT Consultant, I work across business teams, product ownership, requirements engineering, and quality assurance. In public-sector delivery, I structure backlogs, epics, user stories, and acceptance criteria, support domain decisions, and translate them into test concepts, quality gates, and traceable delivery steps.

Since January 2026, I have been building websites and web applications with Codex and Claude Code, increasingly using agentic development at work as well. In February, I founded my startup NutriKompass and developed the entire application myself. This adds hands-on software development and ownership of a product to my consulting experience.

I also maintain a private Personal Context Hub that structures professional experience, projects, decisions, and learning topics as a versioned Markdown knowledge base. It is practical context-engineering work: preparing information for consistent reuse across portfolio work, applications, strategic decisions, and collaboration with AI systems.

Agents & AI Systems

I consider value, system boundaries, evaluation, and operations together: with clear input paths, guardrails, traceability, and verifiable quality criteria.

  • I translate domain logic, roles, and decision points into usable AI interactions instead of generic chat flows.
  • I design guardrails, validation, data flows, and traceability into the system from the start.
  • I build agents as embedded workflow components, not as isolated gimmicks.
  • I structure and version context as a reusable knowledge base instead of losing information across isolated chats and documents.
Experience
5+ Years Product Delivery
Role
Senior IT Consultant · Product · QA · AI
Foundation
M.Sc. + PSPO/IREB/ISTQB
Own product
Founder & developer · NutriKompass
Christoph Lengowski

Quick profile

  • AI product ownership across business, domain teams, and engineering
  • IREB-based AI requirements engineering for clear use cases and measurable outcomes
  • Hands-on development of RAG, LLM, and workflow prototypes
  • Evaluation, guardrails, and quality-by-design as a differentiator
  • Focus on AI products that remain reliable from PoC to operating reality

What teams value

  • I bring structure into ambiguous problem spaces and surface risks early.
  • I treat AI quality as part of product goals, requirements, and architecture – not as a late control step.
  • I prototype early so product decisions are based on evidence rather than AI promises.

Experience

Consulting as a foundation. Building products as the next step. Since 2026, both paths run in parallel.

Consulting & QA

12/2023 - Present

AI Delivery / Quality Assurance Lead / IT Consultant

Materna Information & Communications SE

Key Impact

Prototyped practical AI assistance and knowledge workflows, evaluated boundaries and guardrails, and owned robust QA and delivery structures.

AI delivery and QA ownership in a complex public-sector project: spanning Copilot Studio and knowledge-workflow PoCs, AI enablement, test governance, release readiness, and team leadership.

  • Prototyped a Copilot Studio agent with SharePoint knowledge sources for QA, requirements, and documentation workflows
  • Supported AI enablement through internal training, workshops, and practical use-case consulting
  • Led a QA team of up to 8 testers
  • Defined a risk-based test strategy and durable test concepts
  • Structured automatable test cases in Jira/Xray and Cucumber for Playwright handoff
  • Worked with developers on Playwright implementation and Jenkins-based CI/CD integration
  • Stakeholder management and quality governance at project leadership level
12/2021 - 12/2023

IT Consultant

Materna Information & Communications SE

Key Impact

Built durable requirements and delivery structures for real-world digitalization projects and supported implementation through close functional alignment.

Consulting and hands-on work in digitalization projects with a clear focus on requirements engineering. Ownership for requirements intake, functional alignment, backlog structuring, and prioritization as the bridge between client, business stakeholders, and development.

  • Requirements intake and functional analysis translated into actionable backlog items
  • Alignment with clients, business stakeholders, and developers across scope, requirements, and priorities
  • Ownership for structuring, maintaining, and prioritizing the product backlog
  • Creation of functional specifications and implementation-ready delivery artifacts
  • Occasional functional testing and review work from a requirements perspective
Q4 2021 - 12/2021

IT Consulting Trainee

Materna Information & Communications SE

Key Impact

Built the foundation for public-sector project work through training, certifications, and a structured understanding of project delivery.

Structured trainee program for entering project delivery environments with a focus on methodological foundations, delivery contexts, and IT consulting practice.

  • Intensive onboarding into project contexts, delivery flows, and consulting practice
  • Comprehensive training in Scrum, requirements engineering, and project methodology
  • Completed key certifications as a methodological foundation
  • Fast transition from the trainee program into operational project work

Development & founding

Alongside consulting

02/2026 - Present

Founder & developer

NutriKompass

Key Impact

My own startup and independently developed application: NutriKompass.

In February 2026, I founded NutriKompass and developed the entire application myself. It brings together product vision, domain requirements and technical implementation.

  • End-to-end product ownership from idea to web application
  • Built with Next.js and TypeScript
  • Agentic development informed by the quality standards of my consulting work
01/2026 - Present

Agentic web development

Codex · Claude Code

Key Impact

Turning requirements into working web applications of my own.

Since January 2026, I have been building my own websites and web applications with Codex and Claude Code. I increasingly use this approach in my day-to-day professional work.

  • Independently built multiple websites with coding agents
  • Brought requirements engineering and QA experience directly into development
  • Iterative implementation with personal ownership of the result

How both sides come together

From requirements to application.

Three perspectives, one development process. Explore each step.

Understand

Clarity before the first commit.

I turn a product idea into concrete requirements, user flows and acceptance criteria. This is where my consulting experience comes in.

Output

Requirements · User flows · Acceptance criteria

Explore projects

Projects

Selected projects and PoCs showing my path from AI requirements and product concepts to evaluation, guardrails, and technical validation.

01Key Project

Modernization of a Public-Sector Administrative Procedure

Requirements engineering, test management, and independently delivered E2E test automation for a business-critical administrative procedure

AI Product Ownership with Hands-on PoC Practice

Business-oriented use-case prioritization, AI requirements engineering, evaluation, and guardrails – combined with delivery experience and practical RAG, LLM, and workflow validation.

Legacy -> Web

Migration from Oracle Forms/Reports to modern web architecture

MVP

Definition and prioritization of product-oriented scope

E2E

Test automation independently delivered with Playwright and TypeScript

Public Sector

Digital administration with high traceability requirements

Modernization of a business-critical administrative procedure by replacing an Oracle Forms and Oracle Reports landscape with a modern web architecture. In addition to requirements management, process analysis, and MVP shaping, I own test automation end to end – from concept and design to the independent implementation of the automated user journeys.

My role

Test Manager / Requirements Engineer / MVP Co-Lead / Test Automation

Tech Stack

React · Spring Boot · TypeScript · Playwright

Challenge

A business-critical legacy procedure has to move into a modern web architecture without losing domain-specific edge cases, regulatory requirements, traceability, or usability. At the same time, the MVP needs clear boundaries, testable requirements, and a durable quality strategy.

Solution

I connect requirements engineering, MVP shaping, and quality assurance – from user stories, acceptance criteria, and business validation rules to the API-first target architecture. I designed the complete test automation approach and independently implemented it with Playwright and TypeScript. The end-to-end tests are derived from business processes and risks and managed in Jira and Zephyr for traceability to the requirements.

Project context

  • Modernization of a business-critical administrative reimbursement procedure
  • Replacement of an Oracle Forms and Oracle Reports landscape with a modern web architecture
  • Public-sector environment with strong requirements around regulation, traceability, and accessibility

Project scope

  • Requirements management and derivation of durable requirements from legacy processes and domain logic
  • Prioritization of user stories, acceptance criteria, MVP scope, validation rules, and edge cases
  • Complete concept, design, and independent implementation of E2E test automation with Playwright and TypeScript
  • Derivation of end-to-end tests from business processes, risks, and acceptance criteria
  • Traceable organization and management of automated tests with Jira and Zephyr
  • Interface role between business stakeholders, engineering, architecture, and quality assurance

Impact

  • Analyzed existing business processes and derived durable requirements from legacy systems
  • Prioritized user stories, acceptance criteria, MVP scope, and domain-specific edge cases
  • Owned test automation from concept through independent implementation
  • Designed end-to-end tests with Playwright and TypeScript from business processes, risks, and acceptance criteria
  • Integrated automated tests into the delivery process with traceability in Jira and Zephyr
  • Aligned accessibility and regulatory requirements with business, engineering, and architecture
System Architecture

System Flow

Legacy Domain Processes
Requirements & MVP Scope
React / Spring Boot
OpenAPI Interfaces
Playwright E2E Tests
Jira / Zephyr

Core Components

  • React frontend and Spring Boot backend
  • OpenAPI-based interface contracts
  • Playwright and TypeScript test automation with traceability in Jira and Zephyr
  • Reporting through Jasper Reports

Hard Decisions

  • Protect domain continuity before replacing technology
  • Define MVP scope together with testability
  • Derive end-to-end tests from business risks and acceptance criteria

Guardrails

  • Anonymize customer-specific details in public material
  • Test regulatory edge cases and traceability explicitly
  • Compare legacy and target behavior with risk-based automated end-to-end tests

Tech Stack

ReactSpring BootTypeScriptPlaywrightJiraZephyrOpenAPIOracle FormsOracle ReportsREST APIsRequirements EngineeringTest Automation
02Key Project

E-Gov Workflow Platform – Large-Scale QA in the Public Sector

QA Lead / Test Manager for a complex e-government platform

A large-scale e-government platform for digital files and process handling in public administration. Within this complex program, I was responsible for planning, steering, and evolving the entire quality assurance setup, raising QA maturity both operationally and methodologically.

My role

QA Lead / Test Manager / IT Consultant

Tech Stack

Playwright · Cucumber / Gherkin · Jenkins · Bitbucket

Challenge

The project ran in a highly complex public-sector environment with multiple clients, backend services, strong traceability requirements, and demanding release expectations. Quality had to be controlled not just through execution, but through risk-based test strategy, defect governance, test steering, and stakeholder reporting.

Solution

I built a structured test organization, led the QA team, and tightly connected test management, automatable test design, and KPI-based reporting with engineering, project leadership, and client stakeholders. I worked with Jira/Xray and Cucumber-based test assets, supported their handoff into Playwright implementation with developers, and embedded the resulting automation in Jenkins- and Bitbucket-supported delivery workflows. In addition, I used AI deliberately through prompt and context engineering to generate, expand, and plausibility-check test cases and test data faster. That turned quality into a controllable delivery capability with clear release readiness instead of a reactive bottleneck shortly before releases.

Highlights

  • Built a structured test organization inside a large e-government program
  • Built a risk-based test strategy across multiple system domains
  • Introduced and expanded Playwright- and Cucumber-based automation in close collaboration with development
  • Established defect governance and KPI-based quality reporting
  • Supported integration of automated tests into Jenkins and Bitbucket delivery workflows
  • Used AI deliberately for test case and test data work through prompting and context engineering
  • Owned release and regression steering in a complex public-sector environment
  • Improved release stability through systematic quality steering
  • Established test KPIs and reporting for leadership and stakeholders
  • Covered accessibility and data protection requirements as part of the QA scope

Learnings

“Large IT programs do not become stable through good test execution alone, but through strong QA leadership, transparent quality metrics, and tight coordination across team, delivery, and stakeholders. That combination is what I built and owned in this program.”

Tech Stack

PlaywrightCucumber / GherkinJenkinsBitbucketJiraXrayConfluence.NET / C#SQL ServerWebservicesSharePointOutlook Add-in
03Key Project

RAG Knowledge System for Internal Projects

Making project knowledge easier to find and use in workflows

An internal knowledge system that makes project documents searchable and connects them to existing workflows. Suitable AI use cases were assessed, tested with focused prototypes, and introduced incrementally into QA and requirements processes.

My role

AI Integration / IT Consultant

Tech Stack

LightRAG · Open WebUI · n8n · Lokale LLM-Infrastruktur

Challenge

Enterprise AI initiatives require more than a technically working demo: use cases need verifiable value, sources must be current and dependable, and each PoC needs clear boundaries to support a realistic MVP decision. Without success criteria and process integration, even promising ideas remain difficult to assess.

Solution

I gathered and assessed use cases and defined focused prototypes for suitable topics. This led to a RAG knowledge system with reviewed project documentation. n8n workflows connect Jira, Confluence, and SharePoint to existing processes, while source and retrieval quality were improved with domain teams.

Highlights

  • Structured AI use case discovery around value, feasibility, data availability, and risk
  • Shaped PoC and MVP scope with explicit assumptions, boundaries, and review points
  • Defined measurable success criteria for quality, source coverage, effort, and cycle time
  • Assessed source quality and curated project documentation into a dependable knowledge base
  • Built a RAG knowledge system using LightRAG, Open WebUI, and local LLM infrastructure
  • Integrated n8n, Jira, Confluence, and SharePoint into existing delivery workflows

Learnings

“Enterprise AI becomes investable when use case value, source quality, and success criteria are clarified before the PoC. Technology is only one part; dependable reviews and realistic MVP scope determine whether the result becomes a useful workflow.”

Tech Stack

LightRAGOpen WebUIn8nLokale LLM-InfrastrukturRAG-ArchitekturJiraConfluenceSharePointKnowledge Graph
Self-built projects

More self-built projects

6 projects

Articles about how I work

Short essays on how I connect QA, requirements, delivery, and complementary AI practice in real work.

View all articles
Sep 15, 20266 min read

From legacy application to automated quality assurance: AI in requirements engineering

How I connected AI-assisted analysis, agreed requirements, and automated regression tests in a public-sector redesign project.

Requirements EngineeringAIPlaywrightTest Automation
Read article
Sep 5, 20265 min read

My homepage as a development project

A personal homepage is more than a digital business card to me. It is a real product that shows how I connect requirements, agentic development, and quality.

Agentic DevelopmentNext.jsPortfolioQuality by Design
Read article
Jul 12, 20267 min read

Enterprise RAG: Good answers are not enough

A RAG system does not become trustworthy because its answers sound plausible. Controlled access, sufficient evidence, verified citations, and measurable quality limits make the difference.

Enterprise RAGAI EvaluationGuardrailsAccess Control
Read article

Reusable building blocks

Skills, workflows, and small tools I can carry from one project into the next.

Building blockLLM QARelease GatesPlaywright

AI QA Release Gates

A reusable QA framework for AI features with traceability, multi-model tests, and explicit release criteria before shipping.

Best suited for

Teams that want to move AI features beyond prototyping and qualify them as measurable, reliable delivery components.

Typical deliverables

  • Test suite for security, bias, RAG, performance, and UI
  • Requirements-to-test traceability
  • HTML reports and release gate logic
View repository
Building blockRequirementsInterview FlowPDF Output

Requirements & Quality Workspace

A combined discovery and quality workspace that turns vague ideas into reviewable requirements, traceability, test strategy, and exportable delivery artifacts.

Best suited for

Product owners, business teams, and delivery setups that need to turn unclear requests into testable, reviewable implementation artifacts faster.

Typical deliverables

  • Guided discovery flow with analysis and quality review
  • Structured requirements, traceability, and test strategy artifacts
  • Jira and PDF exports for alignment, review, and delivery kickoff
View live
Building blockClaude Code SkillsAgentic DevelopmentDelivery Workflows

Claude Code Skills

Custom skills for agentic development and delivery workflows. They give coding agents reusable context, clear processes, and reviewable outputs.

Best suited for

Projects where coding agents need to follow project context, conventions, and quality criteria instead of producing isolated code.

Typical deliverables

  • Reusable instructions for coding agents
  • Structured workflows for development, review, and verification
  • Project context and quality criteria as executable skills
View repository

Capabilities & Technologies

Product strategy and AI requirements engineering meet hands-on RAG, LLM, and PoC experience plus quality-by-design for AI systems.

AI Evaluation & Guardrails

Evaluation, guardrails, and quality-by-design for reliable AI systems—complemented by risk-based testing, test-suite design, traceability, and robust quality gates.

QA LeadershipTest ManagementRelease GatesRisk-Based TestingDefect GovernanceQuality ReportingStakeholder SteeringSecurity TestingTraceability

Rapid AI Prototyping

Hands-on PoC development with RAG, local LLMs, APIs, and workflow automation. I also design and implement E2E test automation with Playwright and TypeScript.

PlaywrightCucumber / GherkinAPI TestingREST APIsTest Data DesignTool IntegrationNext.js 16TypeScripttRPC / APIsWorkflow Prototypes

Delivery & Release Governance

Release-oriented delivery with Jenkins, Bitbucket, Jira/Xray, CI/CD coordination, workflow automation, and operational hardening in day-to-day project work.

JenkinsBitbucketGitHub ActionsJira / XrayConfluenceCI/CDWorkflow AutomationReview LoopsRelease ReadinessOperational Hardening

AI, RAG & Knowledge Workflows

AI enablement from use case discovery and PoC/MVP shaping to RAG knowledge systems, Copilot Studio agents, and local LLM workflows using Docker, Ollama, Open WebUI, and LightRAG. Focused on source quality, evaluation, guardrails, and practical adoption.

RAG-ArchitekturAI Use Case PriorisierungMicrosoft Copilot StudioSharePoint Knowledge SourcesAgent WorkflowsAI EnablementAI Product & Use Case DiscoveryPoC-/MVP-AusgestaltungLLM Evaluation & GuardrailsMCP (Model Context Protocol)AI-Assisted RequirementsGitHub Copilot Fundamentalsn8nLightRAGOpen WebUIOllamaDocker ComposeAI Agent DevelopmentPrompt EngineeringRed-Teaming

AI Product & Requirements

AI product ownership, IREB-based requirements engineering, use-case prioritization, and stakeholder alignment for traceable AI product decisions.

Requirements EngineeringSpecification FacilitationStakeholder FacilitationAgile DeliveryProduct ThinkingAI Product OwnershipAI Implementation ConsultingAI Enablement & AdoptionUse Case WorkshopsConsulting LeadershipStrategic Automation

How I Work

Three principles that make my QA, requirements, and AI practice distinct.

Quality as a Delivery Factor

I build test strategy, requirements review, and release criteria into delivery flows from the start – not as a final checkpoint, but as a steerable variable throughout the whole process.

Domain First, Feature Second

Before I automate or build a tool, I ask: which delivery problem gets smaller? What has to be true for this to hold in production?

AI With Context, Not Instead of It

I use AI where I understand the requirements, context, and edge cases. I build reusable skills and workflows for tasks such as STM-CPs, test concepts, E2E tests, and coverage analysis.

Contact

Looking to structure, prioritize, or evaluate an AI use case as a robust prototype? Let’s discuss the goal, value, and a realistic next step.

or reach me directly

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