Interviews mit Lidiia Suslova

I’ve spent most of my career working on complex digital products across fintech, SaaS, and AI-assisted platforms.

Early on, I worked on large-scale marketplace and SaaS systems, including DesignContest. That experience taught me very quickly that the difficult part of product design is rarely the interface itself. It’s the structure underneath - flows, permissions, edge cases, states, and how the system behaves once real users start pushing against it.

Later I moved deeper into fintech, helping build several neobank products from zero to one during the pandemic years. Those environments were fast-moving, ambiguous, and operationally intense, which shaped how I think about trust, onboarding, clarity, and user decision-making under pressure.

More recently, I led UX work for Ender Turing, a real-time conversation analytics platform operating across 24 languages. The interesting challenge there wasn’t the AI itself - it was helping supervisors process huge amounts of information quickly enough to make useful decisions in high-pressure environments. That project received a Bronze A’ Design Award in interface, interaction, and user experience design.

In parallel, I’ve been working at increasing system scale - including a Voice of Customer platform for a large enterprise client in the MENA region, where I helped build the design infrastructure supporting scalable LTR/RTL adaptation across themes, breakpoints, and complex enterprise workflows. The project eventually exceeded 2.3 million layers inside a single Figma environment.

Separately, I led the design and delivery of a fintech SaaS platform from zero to one in roughly six weeks using AI-assisted workflows. What interested me there wasn’t speed alone, but figuring out how to keep the product coherent across dozens of iterations without the system drifting into inconsistency.

Over the last few years, my work has shifted less toward individual screens and more toward systems that can survive complexity, scale, and constant change.

It actually started with programming.

I was around nine years old when I first started learning it, and during one of my early school competitions I spent more time making the interface look good than solving the actual assignment properly. Which, academically, was not a great strategy.

But even back then I was much more interested in how humans interact with systems than in the code itself.

What pulled me into design wasn’t aesthetics as much as communication. Interfaces shape behavior. They influence trust, stress, attention, and decision-making. That became interesting to me very early.

I also naturally gravitated toward structured products - systems, flows, logic, edge cases. I never really saw design as decoration. For me it was always a problem-solving discipline with a human layer attached to it.

The biggest priority is clarity.

If users don’t understand something, it doesn’t work. Most product problems I’ve seen over the years were actually clarity problems, not visual ones.

Before designing anything visually, I usually need to understand the system underneath it:

* how decisions flow

* where complexity appears

* what happens when things break

* how different user roles interact

* where edge cases start affecting the experience

Especially now, with AI-assisted workflows becoming more common, structure matters even more. AI tools are very good at accelerating execution inside a well-defined system. Inside a messy system, they mostly accelerate confusion.

I also think a lot about implementation. One of the best signals for me is when developers receive a handoff and barely ask clarification questions. That usually means the logic is already embedded into the product itself instead of living in meetings or Slack threads.

Tool-wise I work mostly in Figma, increasingly with tools like Figma Make, Claude, Cursor, and AI-assisted workflows in general. But tools change constantly. The thinking behind them matters much more.

Probably relief more than excitement.

The phase I enjoy most is actually the messy middle - when the system still doesn’t fully make sense yet and you’re trying to hold a lot of ambiguity together at once.

I like the moment when things start stabilizing. The flows connect. The logic stops collapsing under edge cases. The product begins behaving predictably instead of fighting itself.

That’s the satisfying part for me.

I’m also much less emotionally attached to visual polish than many designers are. I appreciate strong visual work, obviously, but what interests me more is whether the system survives real usage without creating confusion or operational friction.

The strongest feeling usually comes much later, when something ships and quietly becomes part of people’s everyday lives. Especially in fintech, where small UX decisions can directly affect trust and confidence, that responsibility feels very real.

Technical thinking shaped me a lot.

I naturally approach products by asking questions like:

- where does the logic break?

- what happens under pressure?

- how do different parts affect each other?

- what fails once the product scales?

That mindset pushed me toward complex systems very early.

Another big influence was working in environments with incomplete information. Small teams, changing priorities, evolving products, constant ambiguity. You learn to make decisions without waiting for perfect clarity.

And finally, working across different regions changed how I think about users. I’ve worked with products across Eastern Europe, Southeast Asia, the Caucasus, and MENA markets. Things people consider “intuitive” are often deeply cultural. That experience made me much more careful about assumptions.

Right now I’m very interested in how AI changes product delivery workflows - not just generation, but reliability.

A lot of the industry conversation is still focused on producing screens faster. What became much more interesting to me recently is everything that happens after generation: keeping systems consistent across iterations, managing instability, reducing ambiguity, and preventing products from slowly drifting into chaos during rapid execution cycles.

I’m especially interested in workflows where design systems stop behaving like static UI libraries and start acting more like operational infrastructure.

As for dream projects - honestly, I’m less interested in “visionary” products and more interested in difficult ones. Products with real operational constraints, real scale, and real consequences when things go wrong.

Learn to think beyond screens as early as possible.

A lot of junior designers focus heavily on visuals, animations, polished case studies, and trends. But most real product work eventually becomes about decision-making, tradeoffs, implementation, and communication.

The second thing is learning how systems fail, not just how demos work. Production environments are messy. You deal with edge cases, unstable behavior, unclear requirements, iteration drift, and technical limitations all the time.

And finally - communicate clearly. The ability to explain why something exists is one of the most underrated skills in product design.

Treat your design system seriously.

Not as a UI kit, but as infrastructure.

One thing I noticed recently while working with AI-assisted workflows is that generation is rarely the real bottleneck anymore. Consistency is.

It’s relatively easy to generate a screen. It’s much harder to keep a product logically coherent after dozens or hundreds of iterations without slowly introducing contradictions into the system.

The more structured the foundation is, the easier it becomes to scale products, reduce ambiguity, improve handoff quality, and work effectively with AI tools without losing control of the product itself.

That changed how I think about design systems completely.

Morning walks help a lot. Ideally somewhere quiet with trees and not too much noise.

A surprising amount of product thinking happens there instead of at the desk.

After that I usually try to protect a few hours for deep work - complex flows, reviews, system decisions, AI workflow experiments, or anything that requires uninterrupted focus.

The rest of the day depends on the project. Sometimes it’s product work in Figma. Sometimes reviewing AI-generated outputs and fixing inconsistencies. Sometimes debugging workflows after an agent decides to reinterpret half the product because of one poorly scoped prompt.

That happens more often than people think.

I pay much more attention to workflow shifts than visual trends.

Right now the interesting changes are operational. Tools like Figma Make, Cursor, Claude Code, and AI agents inside design environments are changing how products get built, not just how they look.

At the same time, I still think fundamentals matter more than trends. Clear structure, understandable behavior, good information hierarchy, and usability age much better than visual fashion.

For inspiration, I usually look at products operating under real constraints - fintech, logistics, healthcare, enterprise systems. In those environments, bad design decisions become visible very quickly.

If users understand it.

That’s still the core metric for me.

Good design reduces confusion and helps people make decisions with less friction and less cognitive load.

I also pay close attention to whether the product remains understandable once real complexity appears - edge cases, interruptions, overloaded states, operational pressure, unusual user behavior. A system can look polished in a demo and still completely fall apart in real usage.

And implementation quality matters a lot to me. A beautiful prototype that collapses during delivery isn’t successful design. Real design survives contact with production.

The strongest signal is usually developer reaction.

If a handoff produces endless clarification loops, something is probably missing structurally.

The best projects are the ones where developers can move confidently because the flows, logic, behaviors, and edge cases are already clearly defined inside the design itself.

I also tend to test products mentally against messy conditions - weak connections, interruptions, overloaded dashboards, confused users, situations where people are stressed or in a hurry. Products rarely fail in ideal conditions. They fail in edge cases.

Probably two types of work stand out for different reasons.

The first is fintech. Helping build several neobank products from zero to one during the pandemic taught me a lot about ambiguity, operational pressure, onboarding, trust, and how much responsibility sits inside seemingly small UX decisions.

The second is large-scale Voice of Customer and SaaS analytics platforms. Those projects pushed me much deeper into data visualization, adaptive design, design systems, and system-level thinking at scale.

The difficult part wasn’t just handling large amounts of data. It was making constantly changing information understandable and usable for people making fast decisions under pressure. Once you’re dealing with thousands of signals, filters, multilingual workflows, responsive states, and large operational ecosystems, the problem stops being “screen design” very quickly.

A big part of the work became building systems that could remain coherent as complexity kept growing - across RTL and LTR behavior, adaptive layouts, responsive logic, evolving workflows, and large-scale design systems. Some of those products eventually grew to millions of layers inside a single Figma environment, which completely changes how you think about structure, consistency, and delivery.

That work also received a Bronze A’ Design Award in interface, interaction, and user experience design.

Dieter Rams.

Mostly because his work was disciplined. A lot of his principles are really about restraint - removing unnecessary complexity instead of adding more.

I also admire teams behind products like Stripe, Deel, and Revolut. Not because they’re visually trendy, but because they make complicated systems feel manageable without oversimplifying them.

That’s very difficult to do well.

I’m Ukrainian and currently based in Baku.

Living and working across different regions changed how I think about products quite a lot. Different cultures have very different expectations around trust, communication, onboarding, hierarchy, even visual density.

Outside work, I genuinely need nature to think clearly. A walk somewhere quiet usually solves more design problems for me than staring at a screen for another three hours.

Also - I probably spend an unhealthy amount of time stress-testing AI workflows just to see where they break.

I like working with people who are direct, collaborative, and comfortable with ambiguity.

Fast product environments are rarely clean or perfectly organized. Good teams are usually the ones that can keep moving while the system is still being figured out.

I also care a lot about reducing friction between design and development. One of my favorite moments in a project is when engineering barely needs clarification because the structure is already clear.

That requires much more than attractive screens. It requires consistency, edge-case thinking, realistic implementation awareness, and clear decision-making.

I think design works best when it stays connected to reality instead of becoming presentation material.

Earlier in my career I worked on educational products and tools that helped young designers develop UX and visual communication skills.

More recently, I’ve been sharing practical knowledge publicly around AI-assisted workflows, prompt structure, design systems, and operational challenges inside modern product design.

I try to keep those conversations grounded in real experience rather than hype. There’s already plenty of abstract discussion around AI. What people often need more is practical information about what actually breaks, what scales, what saves time, and where the real bottlenecks appear.

If sharing that helps someone avoid wasting weeks on bad workflows, that’s useful.

The most valuable part was probably articulation.

Award submissions force you to explain your work clearly - what problem existed, why certain decisions were made, what constraints shaped the outcome, and what impact the system actually had.

That process is useful by itself.

The recognition for Ender Turingwas especially meaningful because the project was operationally complex, not just visually polished. It dealt with real-time information, large-scale conversation analysis, and people making decisions under pressure.

I also appreciate the international visibility that award platforms create. Complex B2B and infrastructure-oriented design work often stays invisible outside the companies using it. Awards help bring some of that work into broader conversations around where product design is heading.

The project started from a very practical observation:

companies already have huge amounts of customer insight inside conversations, but most of it is never really used.

Traditional QA workflows usually review only a small percentage of calls manually. Everything else disappears into archives because there’s simply too much information to process consistently.

What interested me most wasn’t only the AI side of the system. It was the human side.

How do you help people quickly understand what actually matters?

How do you surface patterns without overwhelming users with dashboards and noise?

How do you make large-scale conversational data feel clear instead of intimidating?

That became the core direction of the design.

A lot of the work was really about reducing complexity at the interface level while still preserving depth underneath.

The biggest focus was usability under real operational conditions.

The platform processes large amounts of conversational data across multiple languages, but raw AI output alone is not very useful if people cannot confidently act on it.

So the goal became making the system feel understandable for non-technical users:

supervisors, QA specialists, operations teams, business stakeholders.

I wanted people to open the platform and quickly understand:

what needs attention,

what changed,

what patterns are repeating,

where the problems actually are.

A lot of AI products look impressive during demos but become exhausting in everyday use. I wanted this system to feel useful in real workflows, not just visually sophisticated.

The platform is gradually evolving from a QA-focused system into something closer to customer intelligence infrastructure.

Initially the focus was operational:

agent performance, call quality, compliance, coaching.

Now the more interesting direction is understanding broader customer behavior through conversations:

complaints, product friction, pricing concerns, sentiment shifts, recurring patterns.

That changes the design challenge quite a lot.

A supervisor and a business owner consume information very differently. One lives inside the platform daily. The other needs strategic clarity very quickly.

So a lot of the current work is around adaptive dashboards, information hierarchy, data visualization, and helping different types of users navigate large amounts of information without getting overloaded.

The project evolved over multiple years.

The early stages involved a lot of research into how contact centers actually work day-to-day:

observing workflows, speaking with supervisors, understanding where decisions slow down or break.

The first versions were honestly much more complicated than they needed to be.

That became one of the biggest lessons of the project:

the more powerful the underlying system becomes, the more clarity the interface requires.

Over time the design moved toward simpler navigation, clearer prioritization, and faster access to actionable information.

And realistically, systems like this are never fully finished. They continue evolving together with the workflows around them.

The project itself was commissioned, but many of the core design decisions came from the research process rather than the original brief.

The original goal was relatively straightforward:

build an AI-powered analytics platform for conversation quality management.

But after observing real operational environments, the problem became much more interesting.

The challenge was not simply displaying AI-generated information.

It was helping people make faster and clearer decisions under pressure.

That shifted the entire direction of the design.

Yes, the platform is actively used by companies across multiple industries including fintech, insurance, operations, and customer support environments.

It’s a real production system used in daily workflows, not a conceptual project.

That was actually one of the most valuable parts of the experience for me:

seeing how the design behaves under real operational conditions and continuously refining it based on live usage.

A lot of design decisions only reveal their strengths or weaknesses once real teams start depending on the system every day.

I’m naturally drawn to products where complexity is unavoidable.

Enterprise SaaS, fintech, analytics platforms, Voice of Customer systems - these environments force you to think beyond individual screens very quickly.

What interested me here was the combination of AI, large-scale data visualization, and operational decision-making.

The difficult part wasn’t “how do we show more information?”

It was:

how do we make large amounts of information understandable and usable for actual humans?

That balance between complexity and clarity is probably the type of problem I enjoy most.

Probably more product systems than individual designers.

I’ve always appreciated products that make complex environments feel manageable without pretending the complexity doesn’t exist.

Systems like Stripe, Linear, and some financial platforms influenced how I think about clarity, hierarchy, and structured information flow.

Dieter Rams also influenced my thinking quite a lot, especially his focus on restraint and reduction.

In enterprise systems every unnecessary element creates cognitive load very quickly, so simplicity becomes operationally important, not just aesthetic.

The platform serves several types of users at the same time.

Operational teams use it daily for quality management, coaching, performance tracking, and workflow monitoring.

At the same time, higher-level stakeholders use it to identify broader customer behavior patterns and operational trends across conversations.

One of the more interesting design challenges was building a system that works both for people deeply inside operations and for people who need fast strategic visibility without spending hours inside dashboards.

I think the biggest difference is the focus on operational usability rather than simply showcasing AI capabilities.

A lot of AI analytics products become extremely feature-heavy very quickly.

Too many dashboards.

Too much information.

Too much cognitive overhead.

We focused much more on prioritization, clarity, and helping users navigate large amounts of information without feeling overwhelmed.

Another important aspect was scalability.

The system handles multilingual workflows, complex analytics, and large amounts of conversational data simultaneously, so maintaining coherence across the experience became a major part of the design work.

The name was inspired by ideas around intelligence, pattern recognition, and understanding large amounts of information quickly.

The goal of the platform was never simply “analyze conversations.”

It was helping people identify meaningful signals inside extremely large amounts of communication data.

So the naming direction reflected that broader idea of interpreting complexity rather than just processing data.

Figma was the primary environment for interface design, prototyping, and system structure.

For research and workflow mapping I used Miro and Notion quite heavily.

More recently the workflow also expanded into AI-assisted design pipelines using tools like Cursor, Claude, Figma Make, and Storybook for design system consistency and component validation.

As systems become larger and more iterative, maintaining coherence across design, logic, and implementation becomes just as important as creating screens themselves.

Probably the balance between scale and clarity.

The platform processes very large amounts of conversational information continuously, but the interface tries to surface only what is contextually important for a specific user at a specific moment.

That required a lot of work around hierarchy, progressive disclosure, adaptive design systems, and data visualization.

As the platform grew, maintaining coherence became much harder than generating new functionality.

In many ways the real design work was not creating screens.

It was keeping the system understandable while the complexity underneath kept increasing.

Very closely.

The project involved collaboration with engineers, product teams, AI specialists, data teams, QA teams, and operational stakeholders.

For systems like this, design decisions are deeply connected to technical constraints and workflow realities, so constant communication across disciplines was necessary.

A lot of the strongest design decisions came from understanding how people actually work under pressure rather than designing from assumptions.

Technology creates the possibility space, but design determines whether people can realistically use it.

The AI layer processes huge amounts of information:

speech recognition, sentiment analysis, conversational patterns, multilingual processing.

But without good interface structure, that complexity quickly becomes overwhelming.

The design work was really about translating large-scale AI outputs into something understandable, actionable, and operationally useful for humans.

Yes, heavily.

The project involved direct observation of operational workflows, structured interviews, usability testing, and continuous iteration based on real usage patterns.

One important thing I learned is that users often behave very differently in real environments compared to how they describe their behavior during interviews.

So a lot of the most valuable insights came from observing friction points during actual usage:

where people slowed down,

what they ignored,

what created confusion,

what required unnecessary cognitive effort.

That research shaped most of the major structural decisions inside the platform.

One of the biggest challenges was balancing complexity and usability.

The system processes enormous amounts of information, but the users are not necessarily technical specialists.

So the interface needed to feel clear without oversimplifying the underlying logic.

Another challenge was multilingual scalability.

Different languages create very different interface and communication behaviors, especially in conversational systems.

And probably the hardest long-term challenge was maintaining coherence while the platform kept expanding in scope over time.

As systems grow, consistency becomes much harder than feature creation.

The project solved a genuinely difficult design problem, but most people outside the industry would never normally see this type of work.

Enterprise systems and operational platforms often stay relatively invisible compared to consumer products.

Submitting it to an international competition felt like a good opportunity to present the thinking behind the system:

the UX challenges,

the information architecture,

the AI-human interaction layer,

the operational complexity.

The recognition also helped bring visibility to a type of product design work that usually stays behind the scenes.

Probably the biggest lesson was that trust matters more than technical sophistication.

People don’t trust systems simply because the AI is powerful.

They trust systems when the behavior feels understandable, predictable, and transparent.

I also learned a lot about designing for operational pressure instead of ideal conditions.

Real users are busy.

They’re interrupted.

They switch contexts constantly.

They don’t have time to “figure out” complicated systems.

Designing for those realities changed how I approach product design in general.

One thing that became very clear during this project is that customer conversations contain far more business intelligence than most organizations realize.

People say things in conversations that never appear in surveys, dashboards, or reports:

confusion,

frustration,

pricing concerns,

unexpected needs,

competitor comparisons.

The interesting challenge now is not collecting more data.

Most companies already have more than enough data.

The real challenge is helping humans understand it clearly enough to make better decisions from it.

That’s the direction I’m most interested in continuing to explore.

The core principle guiding the design of Ender Turing is to create an intuitive and efficient user experience that leverages AI to enhance call center operations. Inspired by the potential of AI to transform customer interactions, the design focuses on clarity, accessibility, and user empowerment. By integrating advanced analytics seamlessly into the user interface, we aim to provide users with actionable insights without overwhelming them.

The primary focus was to ensure that complex AI-driven analytics are presented in a user-friendly manner. The goal was to enable call center managers and agents to easily access and interpret data to improve performance and customer satisfaction. Achieving a balance between sophisticated functionality and simplicity was paramount.

Future plans include iterative enhancements based on user feedback, integrating more advanced AI capabilities, and expanding the platform's adaptability to various industries beyond call centers. Continuous improvement will ensure the design remains at the forefront of user needs and technological advancements.

The initial design phase spanned approximately six months, encompassing user research, prototyping, and usability testing. This period allowed for thorough exploration and refinement to ensure the design met user expectations and business objectives.

The design was driven by the recognition of a gap in the market for AI-powered tools that enhance call center efficiency and customer satisfaction. While the project aligned with organizational goals, it was also fueled by a personal passion for leveraging technology to solve real-world problems.

The design is proprietary to Ender Turing and is actively used within our platform. There are currently no plans to sell or lease the design rights; instead, we focus on continuously improving and offering it as a core component of our services.

The increasing demand for efficient customer service solutions and the advancements in AI technology highlighted an opportunity to innovate in the call center industry. This convergence motivated the creation of a design that harnesses AI to deliver tangible benefits to users.

The design was influenced by industry best practices and the work of pioneers in user-centered design. Collaborative discussions with cross-functional teams also provided diverse perspectives that enriched the design process.

The primary users are call center managers and agents seeking to improve operational efficiency and customer satisfaction through AI-driven insights. The design caters to organizations aiming to leverage technology for enhanced customer interactions. Currently we are focused on providing more value to dusiness owners and gain business insights from analysing call center data.

The seamless integration of advanced AI analytics into an intuitive user interface distinguishes this design. Unlike other platforms that may present data in a complex manner, Ender Turing focuses on clarity and actionable insights, ensuring users can easily interpret and act upon the information provided.

The name "Ender Turing" pays homage to Alan Turing, a pioneer in computer science, symbolizing the integration of advanced technology and intelligence. "Ender" signifies the aim to provide end-to-end solutions, reflecting the comprehensive nature of the platform.

The design process utilized tools such as Sketch for wireframing and prototyping, InVision for collaborative feedback, and Adobe Illustrator for detailed graphic design elements. These tools facilitated efficient iteration and collaboration throughout the project.

The most unique aspect is the real-time feedback mechanism that allows supervisors and teammates to highlight specific moments or behaviors within conversations. This fosters a culture of continuous improvement and collaboration within call center teams.

ENDERTURING.COM

The design process was highly collaborative, involving professionals with expertise in AI development, data analytics, and user experience research. This multidisciplinary approach ensured the design was both technically robust and user-centric.

Technology plays a central role, with AI algorithms analyzing voice and digital conversations to provide actionable insights. The design ensures these complex processes are presented to users in an accessible and meaningful way.

Yes, the design is heavily influenced by user data and analytical research. We conducted user interviews, usability testing, and analyzed interaction data to inform design decisions, ensuring the platform meets user needs effectively.

One significant challenge was presenting complex AI-driven analytics in a way that is easily understandable to users without technical backgrounds. Balancing functionality with simplicity required iterative testing and refinement.

Recognizing the innovative aspects of the design and its potential impact on the industry, we felt it was appropriate to submit it to gain broader recognition and feedback from the international design community.

The design process reinforced the importance of user-centered design principles and the value of cross-disciplinary collaboration.

I’ve spent most of my career working on complex digital products across fintech, SaaS, and AI-assisted platforms.

Early in my career I worked on large-scale marketplace and SaaS systems, which pushed me pretty quickly toward system-level thinking - flows, permissions, edge cases, responsive behavior, and information architecture.

Over time the products became more operationally complex: fintech platforms, multilingual enterprise systems, Voice of Customer products, and AI-powered analytics tools working with large volumes of real-time data.

A big part of my recent work has been around scalable product systems, adaptive design, and AI-assisted delivery workflows. One of the enterprise platforms I worked on eventually exceeded 2 million layers inside a single Figma environment, which completely changed how I think about consistency and structure at scale.

It actually started with programming when I was a child.

Very quickly I realized I was less interested in the code itself and much more interested in how humans interact with systems. Why some interfaces feel stressful. Why others feel obvious. Why people trust one product and abandon another.

What still motivates me now is the moment when something genuinely complex becomes understandable without oversimplifying it.

That never gets boring.

Completely chose it.

I’ve always had a mix of technical thinking and strong curiosity about human behavior, and design ended up sitting exactly between those two things.

I never really saw design as decoration. For me it was always about making systems easier to navigate and decisions easier to make.

Mostly complex product systems - fintech, enterprise SaaS, AI-assisted platforms, analytics products.

A lot of my recent work involves large operational systems with multiple user roles, adaptive behavior, large amounts of data, and constantly evolving product logic.

What interests me most now are products where AI is deeply integrated into the workflow itself, not just added as a feature afterward.

Learn to think beyond screens as early as possible.

Visual skills matter, obviously. But real product work becomes much more about structure, communication, tradeoffs, implementation, and understanding how systems behave once real users start interacting with them.

And honestly - learn how things fail, not just how demos look.

A good designer can make a screen usable and visually coherent.

A great designer can make an entire system stay coherent once complexity starts increasing - multiple states, user roles, edge cases, changing requirements, responsive behavior, AI-generated outputs.

That’s where the real difficulty usually begins.

If users understand it.

That’s still the core metric for me.

Good design reduces confusion and cognitive load. It helps people move through a system confidently instead of constantly stopping to interpret what’s happening.

I also pay a lot of attention to consistency. Especially in large systems, small inconsistencies create friction surprisingly fast.

Good design reduces friction in places where friction becomes expensive.

In fintech it affects trust.

In SaaS it affects adoption and operational efficiency.

In AI systems it often determines whether people actually use the product confidently or feel overwhelmed by it.

Design is not just presentation. It shapes how understandable the product becomes in everyday use.

Probably something much more personal than the products I usually work on.

Maybe an interactive biographical experience or a narrative-driven digital project where people move through someone’s life instead of simply reading about it.

Something slower and more emotional than enterprise software for once.

A truly AI-native product designed from the beginning around collaboration between humans and AI systems.

Not AI added later as a feature, but a product where the interaction model itself is built around shared decision-making and adaptive behavior from day one.

I think we’re still very early there.

Probably structure before visuals.

I usually start by understanding:

what decisions users need to make,

where complexity appears,

what happens under stress,

where confusion is likely to happen.

Once the structure becomes clear, the visual layer tends to become much stronger naturally.

Dieter Rams definitely.

I also admire teams behind products like Stripe, Linear, and Revolut because they consistently make complicated systems feel manageable without oversimplifying them.

And honestly, I draw a lot of inspiration from fields outside design too - psychology, systems thinking, behavioral science, even operational workflows.

I usually admire products that make complexity feel calm.

Good enterprise systems. Strong information architecture. Interfaces where every element feels intentional and nothing exists only for decoration.

I especially appreciate restraint in design. Removing unnecessary complexity is much harder than adding more features or visuals.

Probably two types of work stand out for different reasons.

The first is fintech. Helping build several neobank products during their early zero-to-one stages taught me a lot about trust, onboarding, ambiguity, and designing for people making important decisions under real constraints.

The second is large-scale Voice of Customer and SaaS analytics platforms. Those projects pushed me much deeper into data visualization, adaptive design, design systems, and system-level thinking. The challenge there was not individual screens anymore, but helping people navigate large amounts of information without losing clarity.

One of those projects later received international recognition in interface and UX design, which was meaningful mostly because the work itself was operationally difficult, not just visually polished.

Stay close to real problems.

A lot of growth comes from seeing where products actually break - confusing flows, bad handoffs, unclear systems, failed assumptions, operational chaos.

I also think writing and communication skills are extremely underrated in design.

The ability to explain your reasoning clearly changes everything.

Probably something connected to storytelling, psychology, or research.

I’ve always been interested in how people interpret systems, narratives, and behavior patterns. That instinct exists underneath design too.

For me, design is problem-solving through structure and empathy.

Empathy helps you understand what actually matters to users.

Structure helps you build something that continues working once complexity increases.

Both are necessary.

My mother had a huge influence on me. She’s a scientist and educator, and I think a lot of my analytical thinking comes from that environment.

Beyond that, honestly - difficult projects and strong teams shaped me more than anything else.

Real production environments teach very quickly.

Curiosity, probably.

And a tendency to look underneath the visible layer of products instead of stopping at aesthetics.

Over time I became much more interested in system behavior, operational logic, edge cases, and how products survive real-world complexity.

Learning how to operate without waiting for perfect clarity.

A lot of real product environments move fast while requirements are still evolving and the system itself is changing in parallel with the design process.

The second challenge was learning how to communicate design decisions clearly across engineering, business, and product teams.

I think the best presentations explain:

the problem,

the thinking,

the tradeoffs,

and the outcome.

Not just the final visuals.

Beautiful screens without context usually don’t say very much about the actual quality of the work.

Right now I’m very interested in AI-assisted product delivery workflows, especially the question of consistency at scale.

Generation itself is becoming relatively easy.

Maintaining coherence across long iteration cycles is much harder.

That’s where most of my current experimentation is happening.

To build systems that continue working well under real conditions - not only in demos or presentations.

Especially in products where people rely on clarity to make important decisions quickly.

Probably clear thinking more than visual style.

And design decisions that survive implementation instead of collapsing between prototype and production.

By making important systems more accessible and understandable.

Financial services, healthcare, education, government systems - people interact with these things during stressful moments. Good design removes unnecessary barriers and confusion.

That matters more than people sometimes realize.

AI-assisted workflows for large-scale product systems.

Especially workflows where AI helps accelerate delivery without slowly breaking consistency across the product.

That operational side of AI design feels much more interesting to me right now than the hype side.

Usually the projects where the impact became visible in real usage.

Products that reached large scale, systems that teams genuinely relied on every day, workflows that reduced friction for real users instead of only looking impressive in presentations.

Those projects stay with you longer.

Probably less obsession with surface-level trends and more attention to operational thinking.

The industry talks a lot about visuals while the actual work is increasingly moving toward systems, workflows, product logic, AI-assisted delivery, and maintaining consistency at scale.

The tooling evolved very quickly. The design conversation is still catching up.

Toward systems that behave more dynamically and less predictably than traditional interfaces.

Designers will spend less time defining static screens and more time defining behaviors, relationships, states, and adaptive logic inside evolving systems.

I think that shift is already happening.

It depends entirely on the complexity of the system.

A single screen can happen quickly.

A coherent product system usually takes weeks or months because the difficult part is rarely the visual itself - it’s maintaining consistency across the entire product.

Usually with understanding user decisions and system structure.

What are users trying to accomplish?

What blocks them?

Where does confusion appear?

What happens when things go wrong?

Everything else follows from that.

"Design with intention. Solve with empathy."

Good design usually comes from solving real problems clearly, not from following trends.

Trends change very fast. Clarity doesn’t.

Technology defines what becomes possible.

Design defines how people experience it.

Right now AI is changing product workflows extremely quickly, which makes the relationship between technology and design much more interconnected than before.

My workflow became much more AI-assisted over the last few years, so the stack now is a mix of traditional product design tools and AI pipeline tooling.

Figma is still the core environment, especially together with Figma Make for rapid prototyping, exploration, and structured generation inside large systems.

For AI-assisted workflows I work a lot with Cursor and Claude - both for product thinking and for operational tasks like design system extraction, flow documentation, component logic analysis, prompt iteration, and code-aware UX refinement. Claude is also extremely useful for research synthesis and structuring large amounts of product context quickly.

On the system side, we use Storybook heavily for design system consistency and component validation between design and engineering. Once products become large enough, keeping alignment between design tokens, components, and implementation becomes critical.

I also use tools like Notion, Miro, and occasionally AI-assisted automation workflows depending on the project complexity.

The interesting shift recently is that tools are starting to behave less like static software and more like collaborative operational environments. The workflow itself becomes part of the design process now.

They shape attention, emotion, hierarchy, and cognitive load.

In complex interfaces especially, color is not decoration. It helps users understand what matters, what changed, what requires attention, and what can safely stay in the background.

“Why does it work this way?”

That question usually leads to much more interesting conversations than simply discussing visuals.

I immediately start mentally deconstructing it.

What constraints shaped it?

What tradeoffs were made?

What happens if one part changes?

How does the system maintain coherence?

That process became automatic a long time ago.

Absolutely.

Complex systems benefit enormously from collaboration.

My ideal collaborators are people who can think structurally, communicate directly, and stay intellectually flexible while working through ambiguity.

Real users under real constraints.

Especially people operating under time pressure, cognitive overload, or difficult environments. Observing how people actually behave inside systems changes your understanding of design very quickly.

Interestingly, not many traditional design books.

Books about psychology, systems thinking, behavior, and human decision-making influenced me much more strongly than purely visual design literature.

Mostly by working on increasingly difficult systems.

Every project introduced new complexity - scale, operational pressure, multilingual environments, AI-assisted workflows, large design infrastructures.

The skills usually grow in response to the problems.

Probably Dieter Rams.

I’d love to hear him talk less about philosophy and more about the practical discipline behind restraint and consistency at scale.

I see recognition more as a signal than a goal.

Awards and rankings help create visibility and credibility internationally, especially for complex B2B and infrastructure-oriented work that normally stays invisible outside the companies using it.

But they don’t really change the work itself.

Color: deep green.

Place: somewhere with trees and quiet.

Season: autumn.

Food: something simple and warm after a long day.

Brand: probably Apple for consistency and restraint.

At some point I spent hundreds of AI credits on a massive prompt trying to redesign an interface.

The output was almost identical to the input.

That was the moment I realized AI becomes expensive mostly when it has to make decisions instead of execute clearly scoped tasks.

Very educational. Slightly painful.

A complex flow finally making sense.

A handoff that developers understand immediately.

A difficult product discussion resolving clearly.

A long walk solving a problem that looked impossible the night before.

Usually small moments like that.

Not specifically a designer, but it was obvious that I constantly wanted to understand how systems worked and how they could work better.

That instinct was there very early.

I think interfaces will become much less visible and much more integrated into everyday environments.

But even if the technology changes completely, people will still need clarity, trust, and understandable systems.

That part probably doesn’t disappear.

I think good design is much less about decoration than people assume.

Most of the real work happens underneath the visible layer - structure, decisions, logic, communication, consistency.

And honestly, some of the best design thinking still happens away from the screen during a long walk.

I’ve spent more than 15 years designing digital products - mostly fintech, SaaS, AI-powered platforms, and large operational systems.

I actually started from programming when I was a kid, but very quickly became more interested in how people interact with systems than in the code itself. Even during school programming competitions I was spending more time thinking about the interface than the technical task. Which was probably not the intended goal, but apparently the direction was already obvious.

Over time that evolved into product and UX design, especially systems with a lot of complexity underneath them. The bigger the system became, the more interesting the work got for me.

I work independently with product teams, mostly on fintech and SaaS products.

A lot of my work today sits somewhere between UX, systems thinking, AI workflows, and design infrastructure. I’m usually involved not just in screens, but in the logic underneath them - flows, architecture, design systems, operational behavior, and how AI fits into real product workflows without making everything chaotic.

Most projects I work on are fairly complex environments where usability directly affects business outcomes.

For me, design is making complicated things understandable.

Not oversimplified - understandable.

Good design helps people move through systems with less friction, less confusion, and more confidence. Especially in products where people are already under pressure or dealing with a lot of information.

I think design is much closer to problem-solving and communication than decoration.

Complex systems.

Fintech, enterprise SaaS, AI-powered products, Voice of Customer platforms, analytics systems - anything where there’s a lot happening underneath and the challenge is making it usable for normal humans.

I especially enjoy products where people need to make decisions quickly and clearly. That creates interesting UX problems because the design has to reduce cognitive load instead of adding to it.

Probably the work I did in fintech during the pandemic, helping build several neobank products from zero to one.

That experience changed how I think about trust, onboarding, and responsibility in product design. When someone uses a banking product for the first time, small UX decisions suddenly matter a lot more than they seem.

What stayed with me most wasn’t visual design. It was the realization that interfaces genuinely affect how safe, confident, or stressed people feel while making important decisions.

A large crowdsourced SaaS platform with multiple user roles, multilingual support, payments, and a pretty complicated operational structure underneath.

That project taught me something very early:

the real challenge is rarely the screen itself - it’s keeping the whole system coherent while it grows.

I think that project shaped a lot of how I still approach design today.

I started by analyzing user behavior and pain points, then built a structured UX flow to streamline contest creation, designer participation, and winner selection. The goal was to make the process effortless while ensuring clarity at every step. Once the UX foundation was set, I designed a modern, intuitive UI that enhanced usability and engagement.

This project was a deep dive into multi-language, multi-currency systems, country-specific payment options, and high-performance design — a real test of scalability and precision. After 20,000+ development hours of a team, the result was a 98% client satisfaction rate, solidifying the platform as one of the most effective ways to get custom designs online.

Right now probably AI-assisted design workflows.

I work mostly in Figma, Cursor, Claude, Figma Make, Storybook, Notion, and different AI-supported systems for design pipelines and documentation.

The interesting part for me isn’t AI image generation or visual experiments. It’s using AI to accelerate operational design work while still maintaining coherence across large systems.

That’s a much harder problem than people think.

Usually while walking somewhere quiet with trees around me.

A surprising number of design problems solve themselves outside the screen. Especially structural problems.

I think constant screen exposure compresses thinking a bit. Walking helps restore perspective and lets the system reorganize itself mentally.

Structure first.

Flows, user behavior, information hierarchy, edge cases, permissions, states, what happens when something fails - all of that matters much more to me initially than visual polish.

Once the structure works, visual decisions become much easier and much more consistent.

Honestly, the most exciting phase is usually the messy middle.

The moment where the system is still unclear, everything feels slightly chaotic, and you’re trying to figure out how all the parts connect together.

That process is stressful sometimes, but also the most intellectually satisfying part of design for me.

Relief, mostly.

Especially when something ships and actually works under real conditions, not just in presentations.

One of my favorite moments is when developers receive a handoff and almost don’t need clarification questions because the logic is already clear inside the design itself.

That’s always satisfying.

If people understand it and can use it confidently.

That still feels like the core metric to me.

Good design reduces confusion, supports decisions, and helps people accomplish what they came to do without unnecessary friction.

If the interface constantly forces users to stop and think “wait, what is happening here?” then something failed structurally.

Clarity first.

Can users immediately understand where they are, what matters, and what they should do next?

Then consistency. Especially in larger systems.

A lot of products look good visually but start breaking apart once you move into edge cases, operational complexity, or real-world usage.

That’s usually where good systems thinking becomes visible.

I think designers have a responsibility not to manipulate people unnecessarily.

Especially now, when products are increasingly optimized around attention, behavioral pressure, or dark patterns.

In AI systems specifically, designers also shape who can comfortably use these tools and who gets excluded by complexity. That’s becoming a much bigger responsibility than people realize.

The field is shifting away from static screens and toward systems, behavior, and AI-assisted interactions.

Products are becoming more dynamic, adaptive, and probabilistic. Interfaces increasingly react to context, user behavior, and AI-generated outputs in real time.

That changes the designer’s role quite a lot.

I think systems thinking and operational thinking are becoming much more important than pure visual execution.

Most of my work exists inside live digital products rather than traditional exhibitions. I usually share projects through online platforms, articles, and case studies focused on UX, AI workflows, systems thinking, and large-scale product design.

I regularly publish design work on Dribbble - dribbble.com/cls and Behance - behance.net/clrscr

I also write about UX, AI-assisted design workflows, and product thinking on Medium - medium.com/@lidacls

Mostly from observing real users and real operational environments.

I’m much more inspired by watching how people struggle with systems than by design trends.

I also read a lot outside design - psychology, behavioral science, systems thinking, biographies. Those areas influence my work more than visual references most of the time.

And honestly, nature helps a lot too.

Minimal, structural, and functional.

I don’t like decorative complexity very much. I prefer interfaces where visual decisions have a clear reason behind them.

In complex systems, simplicity becomes less about aesthetics and more about reducing cognitive load.

That naturally shaped my style over time.

Definitely.

I’m Ukrainian and currently based in Baku, Azerbaijan, and I’ve worked across different regions including Eastern Europe, Southeast Asia, the Caucasus, and MENA.

Working across different cultures changes how you think about users very quickly. You realize that ideas like “intuitive” or “clear” are much more culturally dependent than many designers assume.

That experience made me much more careful about designing from assumptions.

Very collaboratively.

I usually work closely with product, engineering, and research teams because complex systems can’t really be designed in isolation.

I also stay involved beyond visual design itself - into workflows, system logic, design systems, AI integration, and implementation discussions.

The goal is always reducing the gap between the design idea and the actual built product.

Hire designers who ask questions about users, workflows, and system behavior before discussing visual style.

Good product design decisions usually come from understanding the operational reality of the product, not from trends or aesthetics alone.

And honestly, companies should involve designers earlier. A lot of UX problems become expensive simply because design enters the process too late.

Usually it starts with research and system understanding.

I try to understand:

who the users are,

what decisions they need to make,

where friction appears,

what operational pressure exists,

and where the current logic breaks.

After that I move into flows, architecture, and structure before visual refinement begins.

AI tools help accelerate parts of the workflow now, but the structural thinking still has to happen first.

Probably my notebook, Kindle, headphones, ergonomic chair, and sketchbooks.

Most of them are less about aesthetics and more about supporting deep focus and thinking clearly for long periods.

Morning walk first.

Then usually a deep work block for the hardest design or systems problems before meetings start.

The rest of the day depends on the project:

research,

flows,

reviews,

AI workflow experiments,

team discussions,

design system work,

or implementation coordination.

The best days are usually the ones where something suddenly clicks structurally.

Learn systems thinking early.

A lot of junior designers focus mostly on visuals and trends, but real product work quickly becomes about logic, structure, tradeoffs, communication, and implementation.

Also: learn how AI tools fail, not only how they impress people in demos.

And learn to explain your decisions clearly. That skill becomes incredibly important later.

The positive side is that the work stays intellectually interesting. Technology changes constantly, products evolve, and there’s always a new layer of complexity to solve.

The difficult part is that design is still underestimated in many business environments until problems become expensive enough to notice.

You often have to prove the value of clarity after the absence of clarity already caused damage.

Design for clarity - if people don’t understand it, it doesn’t work.

That rule survives almost every design discussion.

Systems thinking, communication, adaptability, research skills, and the ability to stay calm inside ambiguity.

And increasingly: understanding AI workflows realistically, not just aesthetically.

Mostly Figma, Cursor, Claude, Figma Make, Storybook, Notion, and Miro.

But honestly, tools change constantly now.

The more important thing is whether the underlying thinking is solid enough for the tools to scale effectively.

Protecting uninterrupted focus became very important for me.

Complex systems work degrades quickly under constant context switching, so I try to keep dedicated deep work time every day before communication-heavy tasks start.

AI tools also help compress execution time significantly now, especially for iteration and documentation.

Depends entirely on complexity.

A simple product flow can take days. Large enterprise systems with multiple roles, AI integration, and adaptive behavior can evolve for months.

The interesting thing is that visual execution is often not the slowest part anymore. Structural decisions are.

“Can we simplify this?”

Which is usually the correct question, honestly.

But sometimes the harder conversation is understanding whether the interface is too complicated or whether the underlying system itself is too complicated.

Those are very different problems.

Probably fintech during the pandemic.

Building products under uncertainty, with real operational pressure and real user trust involved, changed my standards permanently.

You learn very quickly that small UX decisions can have very large consequences.

Mostly fintech, SaaS, enterprise software, AI analytics, and Voice of Customer products across different regions.

The common thread is usually complexity rather than industry itself.

AI-powered systems and large operational platforms.

Especially environments where humans and AI are effectively collaborating inside the same workflow.

That space still feels very new, very messy, and full of unsolved design problems - which makes it interesting.

Going deeper into AI design systems, operational UX, and AI-assisted product workflows.

I’m especially interested in how design systems evolve when AI becomes part of the execution layer itself.

There’s still a lot the industry hasn’t figured out yet there.

Both.

Some phases require uninterrupted individual thinking, especially structural work.

But strong collaboration with engineering, research, and product teams is essential for complex systems.

The best products are rarely created in isolation.

Mostly large-scale AI-assisted systems, fintech infrastructure, Voice of Customer platforms, and design pipeline experimentation.

A lot of the current work is focused less on individual screens and more on maintaining coherence across large evolving systems.

The easiest way to reach me is through my portfolios on Dribbble - dribbble.com/cls or Behance - behance.net/clrscr where I also share selected projects and design work.

Probably just this:

AI is changing design workflows very quickly, but strong systems thinking matters more now, not less.

Generation is becoming easier.

Coherence is becoming harder.

And I think that difference will define the next generation of product design work.

It all started with recognizing the gaps in traditional call centers — like inefficient processes and missed opportunities to improve customer satisfaction and sales conversion rates. We focused on building practical AI tools like real-time speech recognition and agent assist to make life easier for agents and deliver better outcomes for customers. Working closely with users and refining the solution along the way helped us create something that truly works. Winning the Bronze A' Design Award is a great acknowledgment of that effort.

The decision to focus on specific metrics came from conversations with call center supervisors and other management roles who shared their biggest challenges — like tracking agent performance, spotting trends early, and ensuring customer satisfaction in real time. We identified the most common pain points and aligned the dashboard with actionable data, like customer sentiment, average handling time, agent adherence, etc. The goal was to give supervisors the insights they need without overwhelming them.

We tackled this challenge by combining advanced natural language processing models with language-specific customizations. Each language required careful tuning to account for nuances like tone, context, and cultural differences. Collaboration with native speakers and leveraging multilingual datasets ensured we captured sentiment accurately. Continuous testing and feedback loops helped refine the models, ensuring consistency in analysis and scoring across all 24 languages.

The self-coaching prescriptions were designed by focusing on actionable insights tailored to individual agents. Using AI-driven analysis of conversations, we identified key areas for improvement, such as communication tone, adherence to scripts/internal procedures, or resolution strategies. The system delivers concise, specific feedback along with the best examples, making it easy for agents to understand and act on.

One of the biggest challenges was creating a scorecard system that was both highly customizable for different use cases and easy to use. Early versions were not ideal, which we discovered through user testing. We simplified the design by introducing templates and easy configurations while ensuring advanced users could still access deep customization options and automations. Regular feedback cycles with supervisors helped fine-tune features like updates, weight adjustments, and accuracy measurement to make the system both powerful and user-friendly.

The 20-fold increase in call scoring speed achieved by Ender Turing SaaS is the result of a combination of advanced technical and design optimizations. By leveraging AI-driven automation through natural language processing (NLP) and machine learning, much of the scoring process is pre-handled, significantly reducing manual workload. This is paired with user-centric interface designs that streamline workflows and minimize friction for reviewers. Additionally, optimized batch processing and cloud-native scalability ensure efficient handling of large volumes of calls with minimal lag. Together, these elements deliver remarkable performance improvements without compromising accuracy or usability.

Ender Turing SaaS’s interface was designed to balance simplicity and depth. For non-technical users, we focused on clean layouts, clear language, and pre-configured options that require minimal setup. User testing with diverse groups helped refine the interface, ensuring it works intuitively for supervisors and analysts alike while still delivering powerful, AI-driven insights seamlessly.

End user insights played a pivotal role in shaping Ender Turing SaaS by driving the development of intuitive interfaces and practical features. Feedback highlighted pain points in workflows, leading to simplified navigation, faster access to key functions, and automation where possible. These conversations ensured the platform addressed real-world needs, balancing ease of use with robust capabilities tailored to users' priorities.

We see Ender Turing SaaS evolving into advanced predictive models that not only highlight issues but also proactively suggest solutions before they escalate. On the agent side, we’re focusing on AI-driven assistance that enables agents to perform at their peak levels without lengthy training. Additionally, we’re building a dedicated dashboard for C-level executives, providing direct, real-time access to customer insights. This eliminates the need to wait for lengthy reports, empowering faster, data-driven decision-making.

Organizations have achieved remarkable transformations by leveraging Ender Turing SaaS's real-time insights and automation. For example, a financial services client reduced average handling time by 20% through AI-driven agent assistance, boosting efficiency and allowing agents to focus on upselling opportunities. Similarly, a healthcare provider utilized Ender Turing to analyze conversions into appointments, resulting in an 8% revenue increase across its entire chain of clinics. These outcomes showcase how actionable insights and streamlined processes can transform call centers into strategic growth drivers.