Customer Development & User Research Workshop

✨ Welcome & Workshop Overview

Welcome to our Customer Development and User Research workshop! Today, we'll explore how to validate ideas effectively using customer development, design thinking, and user research methodologies. Crucially, we will examine how AI can augment these processes, where its limitations lie, and how it is reshaping the roles of product and UX professionals.

🎯 Workshop Objectives

By the end of this session, you will be able to:

Apply core principles of customer development, design thinking, and user research
Understand why problem-solution fit is essential for any business
Identify and avoid common validation pitfalls, especially in an accelerated AI development landscape
Leverage AI tools to enhance research and analysis (with proper understanding of their benefits and limitations)
Understand the emerging concept of Agent-Based Experience Design (AX) and its implications for user experience
Develop a foundational validation plan for your own projects
Adapt your mindset to effectively collaborate with AI and navigate evolving team dynamics

🗺️ Workshop Structure

Module 1: The Foundation - Why Customer Development is Essential
Module 2: Customer Development, Design Thinking & User Research Principles
Module 3: Qualitative Research Techniques & Best Practices
Module 4: Modern Tools & AI in Research (Benefits & Limitations)
Module 5: Handling Negative Feedback & Building Resilience
Module 6: Putting It All Together - Crafting Your Strategy

🔍 Module 1: The Foundation - Why Customer Development is Essential

⚠️ Critical Truth: Problem-Solution Fit is a Must

If you are not doing customer development, you are not building a real business – regardless of whether you're a startup or an established company.

Here's the reality check:

🚀 For Startups: Without customer development, you're just building features in a vacuum – not a viable business.
🏢 For Established Companies: Without ongoing customer understanding, you're slowly losing your competitive moat to those who do listen to customers.
💡 The Universal Truth: Customer needs evolve constantly – companies that stop listening become irrelevant.

Customer development is essential for everyone because:

💰 Resource Efficiency: Understanding customers before building saves time, money, and prevents failure.
🎯 Market Relevance: Customer insights drive innovation and competitive advantage.
📈 Sustainable Growth: Companies that understand their customers grow faster and last longer.
🛡️ Risk Mitigation: Customer validation reduces the risk of building something nobody wants.

💡 Bottom Line: Whether you're a day-one startup or a Fortune 500 company, customer development isn't optional – it's the foundation of sustainable business success.

📊 The Data Speaks: Why Customer Development Matters

In today's rapidly evolving market, customer understanding is no longer optional—it's essential for survival. The statistics paint a sobering picture of what happens when businesses ignore their customers:

~90% of startups fail.
~10% collapse in their first year.
Small businesses: 20% fail in year one, 70% fail by year ten.

💡 Key Insight

The #1 reason for startup failure: "No Market Need" (42%).

This signals systemic weaknesses in early-stage validation and understanding user needs.

🚫 Common Validation Pitfalls

Founder's bias: Overlooking actual customer problems.
Skipping/misinterpreting market research: Making assumptions without data.
The "build-first trap": Developing solutions before validating problems. This pitfall is exacerbated in the AI era, where the "hot take" that "Lean Startup is dead because AI can code 100x faster" encourages a "build-build-build" approach without sufficient validation. However, pushing more code faster when most ideas don't generate value only creates more waste.
Poor planning: 78% of failed businesses had no/poor business plan.

🚗 Case Study: Volkswagen Digital:Lab - Customer Development in Action

Volkswagen faced a critical challenge: their traditional waterfall development was "falling out of fashion due to limited ability to adjust course as new information was uncovered." Their solution? Create a radical experiment in customer development:

🔄 Deliberate Methodology Transformation

🏙️ Separate Berlin location - away from traditional VW culture.
🤝 Partnered with Pivotal Labs - "to leverage Pivotal's agile experience and acquire a new competency for VW".
📊 Daily agile standups - discussing progress on various Lab products.
⚡ Short development sprints - allowing quick reaction to user lessons.

🎯 Problem-First Mindset (Design Thinking in Practice)

"The traditional model is for business managers just to request IT teams to build something. But we want to help solve the problems of the actual users. One of the most repeated questions at Digital:Lab is, 'Okay, but what is the problem?'"

🔬 Rigorous User Research & Validation

📋 6-week Discovery & Framing phase - standard for every project.
📊 Quantitative user research - found 50% drop-off rate in existing identity solution.
🎯 MVP thinking - building smallest viable solution to validate assumptions.
👥 User-centric solutions - IdentityKit became "user- and developer-friendly".

💬 Powerful Quote from Jochen Scherl, Digital:Lab Lead

"I'm very proud that we stopped six projects in the first year because we found out there was no need. There was an idea but there was no need. Normally stakeholders have an idea and they spend money and you find out after one-and-a-half years that there's no need. At the Digital:Lab, we would find out in six weeks."

💡 Key Lesson: Customer development isn't just about validating good ideas – it's about quickly invalidating bad ones before wasting resources. This speed of invalidation (6 weeks vs. 1.5 years) is a significant advantage that rigorous customer development offers.

💬 Discussion Questions

Think about and share in chat:

Cultural Challenge: VW's most repeated question became "What is the problem?" instead of "Build this feature." What would it take to shift your organisation to this problem-first mindset?
Success Metrics: Jochen Scherl was "proud to stop six projects" because there was no real need. How would you convince stakeholders that stopping projects early is actually success?
Speed vs. Thoroughness: 6 weeks vs. 1.5 years to discover "no need" - what would be the biggest barrier to achieving this speed in your context?

💭 Module 2: Customer Development, Design Thinking & User Research Principles

🤔 What are Customer Development, Design Thinking & User Research?

These methodologies are similar in essence but have different focuses:

👥 Customer Development

🎯 Focus: Discovering and validating customer needs
📊 Approach: Systematic hypothesis testing
🔄 Process: Customer Discovery → Customer Validation → Customer Creation → Company Building
💼 Origin: Steve Blank's lean startup methodology

🧠 Design Thinking

🎯 Focus: Human-centered problem solving
📊 Approach: Empathy-driven innovation
🔄 Process: Empathize → Define → Ideate → Prototype → Test
💼 Origin: IDEO and design consultancies

🔗 The Connection

All three approaches share the same core principle: understand your users deeply before building solutions. They can be called differently but in essence, they solve the same fundamental problem – reducing the risk of building something nobody wants.

AI doesn't change this fundamental principle; it augments it. The idea that AI can replace the build-measure-learn cycle and lead to "build-build-build" is a "warmed-up version of good old output-focus" and will generate more waste if ideas don't create value for users. Instead, AI helps to improve the odds and achieve outcomes by enhancing good product practices.

🚗 Real-World Integration: VW Digital:Lab's Methodology Stack

VW Digital:Lab successfully combined all three approaches in practice:

🔄 Agile Development: "Short development sprints allowed software developers to react quickly to new events and lessons from users".
🧠 Design Thinking: "One of the most repeated questions at Digital:Lab is, 'Okay, but what is the problem?'" - problem-first mindset.
👥 User Research: "Researching customer behavior, the Lab team found the existing identity solution struggled... 50% drop-off rate".

💡 Key Insight: They didn't choose one methodology – they integrated all three to create a comprehensive validation system.

In the AI era, this integration becomes even more critical. While AI can generate code for MVPs 5-10x faster and speed up product velocity, it doesn't change the need to understand what is actually needed. The focus remains on productisation for known needs of a known userbase.

🎯 Scoping Session Framework

Activity: Internal Team Customer Development Scoping

Use this structured framework with your team to systematically examine your customer development assumptions. Work through each section together, documenting your answers and identifying areas that need validation.

1. 🎯 Customer Segments

Who specifically are we building this for? (Be as specific as possible - demographics, job titles, company sizes, etc.)
What unites our target customers? What shared characteristics, behaviors, or contexts do they have?
How do we currently reach these people? Where do they spend their time, get information, or make decisions?
Who are we NOT building for? What customer segments should we explicitly exclude and why?

2. 🔍 Problems

What specific problem keeps our target customers awake at night? What frustrates them most in their current workflow/life?
How do they currently solve this problem? What workarounds, tools, or processes do they use today?
What does this problem cost them in time, money, stress, or missed opportunities?
How urgent is this problem? Are they actively seeking solutions right now, or is it a "nice to have"?
Who else is affected when our target customer has this problem? (colleagues, family, customers, etc.)

3. 💡 Solutions

What is the simplest version of our solution that would meaningfully address the core problem?
What specific outcome will customers achieve after using our solution? How will their life/work be different?
If we're using AI, what superpower does it give our customers? (Time savings? Better decisions? Cost reduction? Enhanced capabilities?)
Remember: AI is not a value proposition in itself - focus on the benefit it delivers to customers.
What would customers be willing to give up (time, money, current tools) to get this outcome?
How is our approach different from existing solutions? What's our unique angle or advantage?

4. 📊 Hypotheses

What are we assuming about our customers that we haven't actually validated? List your biggest assumptions.
What do we believe about the problem's severity, frequency, or impact that might be wrong?
What assumptions are we making about customer behavior, willingness to pay, or adoption patterns?
Which of these assumptions, if wrong, would completely invalidate our business model?

5. 🧪 Tests

What's the fastest, cheapest way to test our riskiest assumption? What could we do this week?
Who are 10 specific people we could interview to validate our understanding of the problem?
What would we need to see/hear from customers to feel confident we're on the right track?
What would convince us to pivot or abandon this approach? What evidence would change our minds?
How will we measure success in our validation efforts? What metrics or signals matter most?

💡 Scoping Session Tips

Time-box each section: Spend 10-15 minutes per section, don't overthink
Write everything down: Capture assumptions, questions, and action items
Prioritize ruthlessly: Identify the 1-2 most critical assumptions to test first
Schedule follow-up: Plan when you'll reconvene to review validation results
Stay honest: Challenge each other's assumptions - this is where real insights emerge

🔬 Module 3: Qualitative Research Techniques & Best Practices

💡 Key Insight: The Role of Qualitative Research

Qualitative research mainly serves for insights gathering – it is your hypothesis validation and improvement because it is faster to talk with a few customers than to develop something and market it.

But the real verification happens when you earn money with your product.

As a startup founder, you need to talk with people and be able to listen to what they are saying. It can be disillusioning and might hurt, but it is an important part of the process.

🤖 AI Era Context

While AI can help "see patterns and ask better questions," it cannot "skip the questions entirely". Organisations treating human insight as optional and assuming AI can shortcut understanding risk "getting blind" when new sign-ups, engagement, and retention drop. AI summarises what happened but cannot explain why it matters, nor can it decode context, friction, trust, or motivation. It also cannot understand why humans behave irrationally, make emotional decisions, or have unconscious needs they can't articulate.

📊 Understanding Qualitative vs Quantitative Research

🗣️ Qualitative Methods

👥 User Interviews: In-depth conversations to understand needs, motivations, and pain points
👁️ Contextual Inquiry: Observing users in their natural environment
👪 Focus Groups: Facilitated discussions with multiple users
🧪 Usability Testing: Observing users interact with prototypes
📔 Diary Studies: Users document experiences over time

📈 Quantitative Methods

📝 Surveys: Collecting structured data from many users
📊 Analytics: Analysing user behaviour data
🔄 A/B Testing: Comparing performance of different versions
🃏 Card Sorting: Understanding how users categorise information
📏 Benchmark Testing: Measuring against standards or competitors

🎙️ Effective User Interviews

📋 Prepare thoughtfully: Create an interview guide with open-ended questions
🔍 Start broad, then narrow: Begin with context, then dive deeper
❓ Use the "Five Whys" technique: Probe to uncover root causes
👂 Listen more than you speak: Let users tell their stories
👀 Watch for behavioural signals: Note body language and emotional responses
⚠️ Avoid leading questions: "Tell me about your experience" vs. "Was your experience good?"
💬 Capture verbatims: Record exact phrases users say about their problems

💬 Chat Exercise: Open-Ended Questions

Think about and add your most important open-ended question about your own product in the chat right now. Let's see how it looks like.

Examples of good open-ended questions:

"Tell me about the last time you faced [problem your product solves]"
"Walk me through your typical day when dealing with [relevant situation]"
"What's the most frustrating part about [current solution/process]?"

🤖 Module 4: Modern Tools & AI in Research (Benefits & Limitations)

🧠 AI's Role in User Research & Validation

AI is transforming how we conduct research, analyse data, and validate ideas. Key benefits include:

🛡️ Risk reduction through deeper insights
💰 Enhanced investor credibility with data-backed decisions
📊 Optimal resource allocation
⚡ Dramatically accelerated insight generation: Potential impact includes reducing validation costs by up to 83% and condensing timelines from months to 72 hours

⚠️ The Problem with AI Tools for Beginners

When you start doing and analysing user interviews via LLMs without experience, you face the main problems:

🔧 Same as with coding: If you didn't build software before, you'll be able to build a prototype but nothing more
📊 No evaluation framework ("evals"): You don't have ways to judge if the AI output is good enough
📝 Text-heavy output: It's not visual, it's hard to understand compared to well-structured analysis
🎯 Lack of context: AI doesn't understand your business context and customer nuances like you do
🧠 Missing experience: You need to understand what good insights look like before AI can help you find them
"Winging chats" instead of systematically digging: It's not enough to endlessly chat with an LLM; you need to think ahead and progressively load AI for better results
Forgetting your own knowledge: Don't let AI exclusively tell you what the data says; familiarise yourself with the data first and challenge AI's interpretations with your expertise
Poorly formatted data: Disorganised information and transcripts with errors will ruin results
Expecting AI to do 100% of the work: AI can be a predictable "85% shortcut," freeing up time for the remaining crucial 15% of insights

Using ChatGPT and similar tools is great, but you need foundational knowledge first. AI isn't inherently "good"; it's about understanding "how it works, how to interact with it, what it's good at, and where it falls short" to compensate for weaknesses and benefit from strengths.

🔄 AI for Qualitative Research Synthesis

⚠️ Traditional Challenges: Time-consuming, biased, hard to scale
✨ AI Capabilities:
- 🎙️ Automated Transcription (saving hours)
- 🧠 Semantic AI Synthesis (understanding meaning, context, nuances)
- 😊 Sentiment, Intent, Emotion Analysis
- 🏷️ Automated Coding, Tagging, Categorisation
- 🔍 Identification of Pain Points, Needs, Behaviours
🛠️ Example Tools: AxWise, Insight7, Quals.ai, NVivo (with AI), Dovetail

💡 AxWise Approach to Research Synthesis

AxWise transforms how teams analyse qualitative data by providing visual, structured insights that are easier to understand and act upon compared to raw text outputs from general AI tools. This addresses the challenge of text-heavy output from LLMs without visual structure.

🔮 The Future of UX: Agent-Based Experience Design (AX)

🤖 Redefining the "User"

A significant shift is occurring where UX is no longer just for human users; it's for the AI agents acting on their behalf. These autonomous, AI-powered entities are redefining "user experience" by performing tasks like answering customer support questions or scheduling appointments. This evolution marks the rise of agent-based experience design (AX), which considers how software interacts with software on behalf of humans.

🔄 Designing for Agents

Clean APIs: Well-documented, predictable data structures
Context Awareness: Understanding availability, preferences
Transparency: Clear reasoning for decisions
Human Control: Clear off-ramps to override decisions

👻 Invisible Interfaces ("Zero UI")

No Visible UI: Chatbots, voice assistants
Behind-the-scenes: Decisions made without user awareness
New Accountability: Product teams must ensure responsible automation
Outcome Focus: User satisfaction based on results, not process

💼 Implications for UX Roles

Some experts project that up to 85% of today's UX researchers could see their roles displaced as AI automates research and synthesis. However, this also presents an opportunity for designers and researchers to pivot, understanding how agents operate and architecting systems that support both human goals and agent execution. It's still about empathy, but with an expanded lens that includes the intelligent agent.

🎯 AX Design Exercise

Think about your product/service:

What tasks could an AI agent perform on behalf of your users?
What information would the agent need to make good decisions?
How would you ensure users maintain control and trust?
What would "success" look like from both human and agent perspectives?

💪 Module 5: Handling Negative Feedback & Building Resilience

🎯 Critical Mindset Shift

Your task is not about ego, but to build business and earn money.

Negative feedback is not actually "negative" – it's just words from a person's perspective that can help you build a better product.

Sometimes you need to embrace being "cringe" to build something people actually want.

Sometimes you need to embrace being "cringe" to build something people actually want

🔄 Reframing "Negative" Feedback

❌ Ego-Driven Thinking

"They don't understand my vision"
"This feedback is wrong"
"They're not my target customer"
"I know better than they do"
"This hurts my feelings"

✅ Business-Driven Thinking

"What can I learn from this perspective?"
"How does this help me build a better product?"
"What underlying need is being expressed?"
"How can I validate or invalidate this concern?"
"What would make this person pay for my solution?"

🛡️ Building Feedback Resilience

📊 Separate emotion from data: Treat feedback as information, not personal attacks
🔍 Look for patterns: One person's opinion vs. multiple people saying the same thing
❓ Ask follow-up questions: "Can you tell me more about that?" "What would ideal look like?"
🎯 Focus on the problem, not the solution: Users are great at identifying problems, less reliable at proposing solutions
💰 Remember the goal: You're building a business, not seeking validation

🧠 Mindset Exercise

Think of the harshest feedback you've received about your idea. Now rewrite it from a business perspective:

What specific problem or concern does this feedback highlight?
How could addressing this concern make your product more valuable?
What questions could you ask to better understand this perspective?
How might this feedback help you reach product-market fit faster?

🎭 The "Cringe" Factor

Sometimes building a successful product means:

🗣️ Asking questions that feel obvious or "dumb"
🔄 Pivoting when your original idea doesn't work
📞 Making cold calls to potential customers
🎤 Presenting ideas that aren't fully polished
💬 Having uncomfortable conversations about money and value

Remember: Being "cringe" for a few months is better than building something nobody wants for years.

🚀 Module 6: Putting It All Together - Crafting Your Strategy

📈 Evolving Product Roles and AI

🔧 Modern Product Management Challenges

PMs and "Vibe Coding": Google is reportedly asking Product Manager candidates to "vibe code" in interviews, which involves creating prototypes using AI-powered tools. This reflects a future where PMs are expected to be comfortable with building and evaluating AI-powered user experiences.
Product Velocity vs. Engineering Velocity: GitHub research indicates that engineers using AI co-pilots are shipping code 2-3x faster, but product decisions are still moving at pre-AI speeds. This is creating a widening gap and organisational bottlenecks.
Generalists vs. Specialists: AI is contributing to a shift from an era of specialists to one where "creative generalists with access to GPT" can work like a team of five specialists from twenty years ago.

📋 Your Customer Development Action Plan

🎯 Step 1: Define Your Hypotheses

Write down your key assumptions:

Customer Segment: Who do you think your customers are?
Problem: What problem do you think they have?
Solution: How do you think your product solves it?
Value Proposition: Why would they choose you over alternatives?
- Refinement for AI products: Remember, AI isn't the value proposition itself. Focus on the outcome or superpower AI enables for your customer.

🔍 Step 2: Design Your Research

Plan your validation approach:

Interview Questions: 5-7 open-ended questions to test your hypotheses
Target Participants: 10-15 people from your assumed customer segment
Success Criteria: What would prove/disprove your assumptions?
Timeline: When will you complete these interviews?

📊 Step 3: Analyse & Iterate

Process your findings:

Pattern Recognition: What themes emerged across interviews?
Hypothesis Testing: Which assumptions were validated/invalidated?
Pivot Decisions: What changes do you need to make?
Next Steps: What's your next round of validation?

🛠️ Tools & Resources

📝 Research Tools

Calendly: Easy interview scheduling
Zoom/Google Meet: Remote interviews
Otter.ai: Automated transcription
Miro: Visual analysis and mapping
Typeform: Follow-up surveys

🤖 AI-Powered Analysis

AxWise: Comprehensive interview analysis
Insight7: Automated theme extraction
Dovetail: Research repository
ChatGPT/Claude: Quick analysis (with limitations, require foundational knowledge and specific prompting for good results)
SLMs (e.g., Phi-4 Mini, Qwen2.5-Coder, Ministral 8B, SmolLM 3B): Consider for highly specific, focused analysis tasks where speed, cost, and local deployment are priorities. Advanced techniques like Yarn (Yet Another RoPE extensioN) can extend context windows beyond base limits, enabling longer document analysis while maintaining efficiency
Earmark: For Product Managers, consider tools like Earmark as an "AI co-pilot" for surfacing discovery questions, translating requirements, and generating specs from meetings

💡 Pro Tip: Start Small, Think Big

Begin with 5-10 interviews to test your biggest assumptions. Don't try to validate everything at once. Focus on the riskiest hypotheses first – usually around problem-solution fit.

🚗 VW Digital:Lab Validation Framework

Remember Jochen Scherl's approach: "We would find out in six weeks" instead of spending 1.5 years. Apply this systematic methodology:

📋 The 6-Week Discovery & Framing Process

🔍 Problem validation first - "What is the problem?" before any solution
👥 User research with data - quantify pain points (like the 50% drop-off rate)
🎯 MVP definition - smallest thing to validate core assumptions
- While AI can help spin up MVPs faster, the critical step remains the discussion and validation of "what is actually needed".
📊 Success metrics defined - know what "validated" looks like
- For AI products, this means rigorous validation of accuracy, effectiveness, speed, and business impact using a "scientific method" to avoid shipping products no one wants or that don't work well.

🚫 Validation as Success Metric

🏆 Celebrate stopping projects - "proud that we stopped six projects"
💰 Measure resources saved - not just projects launched
⚡ Speed of invalidation - weeks vs. years of discovery
🔄 Iterative learning - short sprints with user feedback

🎯 Problem-First Culture

"We want to help solve the problems of the actual users" – shift from feature requests to user problem solving.

🎯 Success Metrics

How to know if your customer development is working:

📈 Increasing Clarity: Your understanding of the problem becomes more specific
🎯 Consistent Patterns: Multiple customers describe similar pain points
💰 Willingness to Pay: Customers express interest in paying for a solution
🔄 Referral Behaviour: Customers suggest others who have the same problem
⚡ Urgency Signals: Customers are actively seeking solutions now

🎉 Workshop Conclusion & Next Steps

🔑 Key Takeaways

🎯 Customer development is essential – not optional for any business
🗣️ Qualitative research provides insights – but revenue provides validation
🤖 AI tools are powerful – but require foundational knowledge, strategic prompting, and human oversight to use effectively
💪 Negative feedback is valuable data – not personal attacks
🚀 Start small and iterate – don't try to validate everything at once
The landscape of product and UX is evolving with AI, requiring new skills and a deeper understanding of human-agent interaction

📅 Your 30-Day Challenge

Week 1: Hypothesis Formation

Define your customer segment and problem hypotheses
Create interview guide with 5-7 open-ended questions
Identify 15-20 potential interview participants

Week 2-3: Research Execution

Conduct 10-15 customer interviews
Take detailed notes and record (with permission)
Look for patterns and unexpected insights

Week 4: Analysis & Planning

Analyse findings and test hypotheses
Decide on pivots or iterations needed
Plan next round of validation

📚 Additional Resources

📖 Books: "The Mom Test" by Rob Fitzpatrick, "The Lean Startup" by Eric Ries
🎓 Courses: Steve Blank's Customer Development course, IDEO Design Thinking
🛠️ Tools: AxWise for AI-powered analysis, Miro for visual mapping
🌐 Communities: Indie Hackers, Product Hunt, local startup meetups
For deeper AI understanding: Explore resources on "Context Engineering", "Agent-Based Experience Design", and the application of SLMs

👥 Recommended Experts to Follow

🔬 Research & Analysis Experts

Dr. Susanne Friese - Leading expert in qualitative data analysis and ATLAS.ti, author of multiple books on qualitative research methods
LinkedIn: /in/dr-susanne-friese/
Caitlin Sullivan - User Research + Service Design Consultant, expert in product discovery and UX research impact measurement
Website: userresearchstudio.com

🎨 Design & Product Experts

Tiago Machado, PhD - Strategic design and product leader at Nokia, expert in Enterprise Design Thinking and digital innovation
LinkedIn: /in/tiagomachadocom/
Hugo Alves - Founder of Synthetic Users, expert in psychology, product management, and AI-powered user research
LinkedIn: /in/hugomanuelalves/

💡 Why Follow These Experts?

These professionals represent the cutting edge of customer development and user research:

Dr. Friese brings deep academic rigor to qualitative analysis
Caitlin Sullivan offers practical startup-focused research methodologies
Tiago Machado provides enterprise-scale design thinking insights
Hugo Alves bridges psychology and AI-powered research tools

Following their work will keep you updated on the latest methodologies, tools, and best practices in customer development and user research.

👨‍💼 About the Presenter

Vitalijs Visnevskis – Founder of AxWise.de

Expert in AI-powered User Research & Customer Development, Vitalijs brings deep, field-tested knowledge on how to build with—and for—your users, especially in the evolving AI landscape. His platform, AxWise, focuses on providing visual, structured insights from qualitative data, moving beyond raw text outputs from general AI tools.

📧 vitalijs@axwise.de

🌐 axwise.de

🙏 Thank You!

Questions? Let's discuss!

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