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Hi, I'm

Christoph Lengowski

I connect product ownership, requirements engineering, QA leadership, and AI delivery to create clear decisions, testable requirements, and reliable AI and delivery systems.

AI Product OwnershipAI Requirements EngineeringAI Evaluation & GuardrailsRapid AI PrototypingRAG & Agentic AI
8QA team members led
RAGLocal AI/RAG prototype
5+Years of delivery ownership
Get in TouchView Projects
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. Experience in test automation and CI/CD supports 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.

For AI-enabled delivery and knowledge systems, I build my own RAG, agent, and evaluation prototypes. I connect local LLMs, retrieval, structured workflows, and guardrails with the same quality questions that support conventional software delivery: clear requirements, controlled access, traceability, measurable criteria, and human review.

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
AI PoC practice
Rapid AI Prototyping
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

Career milestones that shaped my profile.

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

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 and test management 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

API-first

Functional preparation for React, Spring Boot, and OpenAPI

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. My focus is on requirements management, process analysis, MVP shaping, and quality assurance at the intersection of business stakeholders, engineering, architecture, and testing.

My role

Test Manager / Requirements Engineer / MVP Co-Lead

Tech Stack

React · Spring Boot · OpenAPI · Oracle Forms

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 support the functional shaping of the MVP through requirements engineering, process analysis, and close coordination with business stakeholders, engineering, architecture, and quality assurance. This includes user stories, acceptance criteria, gap analysis, business validation rules, risk-based test cases, and preparation of an API-first target architecture based on React, Spring Boot, and OpenAPI.

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 functional documentation of existing business processes
  • Derivation of requirements from legacy systems and existing domain logic
  • Creation and prioritization of user stories, acceptance criteria, and MVP scope
  • Modeling of process logic, validation rules, edge cases, and regulatory requirements
  • Definition of test strategy, test concept, and risk-based test cases for the MVP
  • Interface role between business stakeholders, engineering, architecture, and quality assurance

Impact

  • Analyzed existing business processes and derived durable requirements from legacy systems
  • Created and prioritized user stories, acceptance criteria, and MVP scope
  • Modeled domain logic, validation rules, and edge cases for digital application workflows
  • Defined test strategy, test concept, and risk-based test cases for the MVP
  • Acted as an interface between business stakeholders, engineering, architecture, and QA
  • Covered accessibility, traceability, and regulatory requirements in a public-sector context
System Architecture

System Flow

Legacy Domain Processes
Requirements & MVP Scope
React / Spring Boot
OpenAPI Interfaces
Risk-Based Tests
Quality Gates

Core Components

  • React frontend and Spring Boot backend
  • OpenAPI-based interface contracts
  • Requirements, test, and traceability artifacts
  • Reporting through Jasper Reports

Hard Decisions

  • Protect domain continuity before replacing technology
  • Define MVP scope together with testability
  • Describe interfaces API-first

Guardrails

  • Anonymize customer-specific details in public material
  • Test regulatory edge cases and traceability explicitly
  • Compare legacy and target behavior based on risk

Tech Stack

ReactSpring BootOpenAPIOracle FormsOracle ReportsREST APIsJWT/OAuth2JiraConfluenceXrayAgile EntwicklungRequirements EngineeringTestmanagementUser StoriesAkzeptanzkriterienMVP-Definition
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

Enterprise AI Integration – RAG Knowledge System and Delivery Automation

From AI use case discovery to integrated enterprise workflows

Design and build of an internal enterprise AI approach combining AI use case discovery, PoC/MVP shaping, a RAG knowledge system, and automated delivery workflows. Candidate use cases were assessed by value, feasibility, data and source quality, and measurable success criteria before being introduced incrementally into existing 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 structured AI use case discovery and prioritized candidates by business value, feasibility, source availability, and risk. For suitable cases, I shaped PoC and MVP scope and defined measurable criteria such as answer fidelity, source coverage, review effort, and processing time. This informed an internal RAG knowledge system with curated project documentation and n8n workflows integrating Jira, Confluence, and SharePoint into existing delivery processes. Source and retrieval quality were improved through cleanup, terminology alignment, and domain review loops.

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
Own Projects

More Own Projects

12 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
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
Jun 21, 20266 min read

From demo to delivery: QA governance for AI workflows

AI can speed up requirements work, test design, and document analysis. But the workflow only becomes production-ready when quality, traceability, and operating boundaries are designed into it.

AI WorkflowsQA GovernanceRequirementsLLM
Read article
Apr 29, 20265 min read

AI agents are shifting the quality bottleneck

AI agents shift the bottleneck in software projects: code can be produced faster, but domain clarity, quality, and control become even more important.

KI-AgentenQualitätssicherungRequirements EngineeringQA
Read article

Reusable Assets & Accelerators

Reusable workflows and delivery assets that help teams move from vague requests to reviewable outcomes faster.

Reusable AssetLLM 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
Reusable AssetRequirementsInterview 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
Reusable AssetClaude Code SkillsE2E TestingISTQBQA Automation

Claude Code QA Skills

Three skills for ISTQB test concepts, Playwright execution with failure analysis, and structured coverage optimization in AI-assisted delivery setups.

Best suited for

QA teams and projects that want to automate test documentation, execution, and coverage analysis as a repeatable delivery workflow.

Typical deliverables

  • ISTQB-compliant test concepts with intake and compliance checks
  • Structured E2E test execution with failure categorization
  • Coverage gap analysis with automated test creation
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 – grounded in proven experience with risk management, 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 to validate product ideas, data access, and technical risk early.

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 deliberately where I know the requirements, the context, and the edge cases – as a lever for faster, testable artifacts, not as a substitute for domain knowledge.

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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