We caught up with the brilliant and insightful Robin Ahn a few weeks ago and have shared our conversation below.
Alright, Robin thanks for taking the time to share your stories and insights with us today. We’d love to hear about a project that you’ve worked on that’s meant a lot to you.
The most meaningful project I’ve worked on is building Cypris Q, our AI-powered research assistant that helps R&D teams uncover insights across patents, papers, organizations, and news.
When I joined Cypris as the first and only in-house designer, Cypris Q was still an early idea—more of a raw proof-of-concept than a usable product. We were getting strong signals from enterprise users that they loved our data but needed a faster, more intuitive way to extract insights from it. Researchers were spending hours sifting through PDFs, comparing patents, and manually summarizing long papers. We knew AI could accelerate that work, but we also knew that trust, transparency, and UX clarity were going to be the deciding factors.
The backstory:
Cypris was growing quickly, and our CEO, Steve, wanted to differentiate the platform with a truly useful AI experience—not just “chat with a model,” but something that understood scientific data, cited sources, and integrated directly into the R&D workflow. As the only designer, I had to build everything from scratch: the UX, the interaction model, the visual identity, the navigation, the error states, the side-by-side comparison flows, the history panel, the thinking UI, and even the long-term vision of how Q sits across the entire platform.
Why it was meaningful:
This project pushed me more than anything I’ve done. I wasn’t just designing screens—I was shaping how scientists and researchers interact with information. I spoke directly with chemists, materials scientists, data analysts, and innovation teams from companies like Halliburton, Sasol, LANL, and Mannington Mills. Their feedback shaped everything: how we present claims for patents, how we handle citations, how we show model transparency, how we manage context windows, and how Q should behave when a user asks for complex analyses.
It was also meaningful because it represented true 0→1 ownership. I wasn’t refining an existing system—I was building an entirely new product experience that now lives at the heart of the platform. Every detail, from loading animations to the “Full Cypris DB” toggle to how Q should act when answering regulatory vs. literature vs. prior-art questions, came from thoughtful iteration, user interviews, engineering constraints, and cross-team collaboration.
Seeing users get real value—like being able to understand a patent landscape in seconds, compare dozens of papers, generate executive-ready reports, or get answers they’d previously needed a full-time analyst for—has been the most rewarding part. Researchers tell us that Q has changed the way they work. That’s the type of impact I want to create as a designer.
Awesome – so before we get into the rest of our questions, can you briefly introduce yourself to our readers.
My Name is Robin Ahn: The Designer Building the Future of R&D Intelligence
For readers who may not know me, my name is Robin Ahn, and I’m a product designer who specializes in building 0→1 products—bringing things from a blank slate to a fully realized system. Today, my primary focus and most meaningful work is dedicated to designing the future of research and development at Cypris, an R&D intelligence platform.How I Got Into Design: The Foundation for Complexity
My story doesn’t fit the typical “design kid” narrative; I found design during the pandemic at Cornell when I was a lonely college student. It became a source of purpose and a lifeline, instilling in me the belief that design is a way to build meaning out of emptiness.
This early grit and a multidisciplinary background set the stage for my current work on complex platforms:
Multicultural Intuition: Growing up across four countries taught me that design is not universal, a lesson that is critical when building for a global audience of researchers and scientists.
Journalism & Research: My experience as a journalist and as an academic researcher at the Cornell Social Media Lab taught me how to extract emotion, translate complexity into something human, and focus on safety-by-design—all crucial skills for creating a trustworthy intelligence platform.
0→1 Mindset: My strength was forged by fighting for my place as a designer at an early startup, learning engineering language, and embracing A/B testing—a tenacity that is essential for leading design in a fast-moving, ambiguous environment like Cypris.
My Work at Cypris: Turning Complexity into Empowerment
I am the first and only in-house Product Designer at Cypris. My mission is to transform the incredibly complex, technical workflows of researchers, scientists, and innovation teams into intuitive, empowering interfaces.
Products and Features I Provide
My ownership spans nearly every major feature across the R&D intelligence platform:
Cypris Q: Our AI-powered research assistant and my most meaningful project, designed to help scientists navigate vast information more efficiently.
Research Hubs: Features like Projects/Workspace for collaborative research and Your Uploads for secure document ingestion.
Core Intelligence Systems: Including the Monitoring system for automated alerts across research and patents, and the redesign of intricate sections like Patent Data (claims, families, citations, legal status, etc.).
The Problem I Solve
I solve the problem of information overload and complexity for the R&D community. I bridge the gap between highly technical data (patents, scientific papers, global trends) and the user’s need for clarity and action. I sit in weekly onboarding calls and office hours to hear directly from researchers, ensuring the design is grounded in reality.
The Impact of Strong Design
These continuous feedback loops and strong design processes have had a tangible impact on platform engagement:
Increased user sessions by 58%.
Increased platform events by 21%.
Grew monthly active users from 25% to 51%.
What Sets Me Apart From Others
My distinct advantage is my ability to integrate my background with the needs of a highly technical domain:
I excel in 0→1 environments and specialize in building for technical, complex domains.
I design AI systems with transparency and trust, informed by my research into digital safety and ethics.
I bring structure to chaos, providing the foundational design systems and processes a nascent R&D platform needs to scale.
What I’m Most Proud Of and What I Want Readers to Know
I’m proud of the products I’ve built at Cypris—but I’m even prouder of the scientists and researchers they help navigate critical data more efficiently. Everything I create traces back to real humans with real needs.
My design philosophy is simple: “Designing from nothing means believing something meaningful can exist before it does, and then convincing others it can too.”
I want clients and followers to know that my work blends empathy, clarity, systems thinking, and education. I build to empower, I build to teach, and I build because I believe design can turn uncertainty into progress for the world’s most innovative minds.
Let’s talk about resilience next – do you have a story you can share with us?
One moment that really reflects my resilience happened during the multi-month effort to rethink the Cypris Q experience — including the loading UI system, dataset review logic, and the entire side panel redesign.
At the time, I was the only designer at a fast-moving startup, and multiple major initiatives were happening at once: the new AI reports builder, a reorganized side panel, and a deeper integration of patents, papers, organizations, and news into a single unified workflow. I was responsible for design, specs, research notes, and prototypes — often switching contexts dozens of times a day.
A particularly challenging part was the Loading UI overhaul. What initially sounded like a simple improvement quickly turned into a highly complex system. After benchmarking GPT-5, Claude, and Gemini, it became clear we needed a structured, model-aware loading framework involving:
granular chips
dataset-level search blocks
skeleton states
token/time guards
expanded chip clustering rules
click behavior that stayed consistent across Q
decisions about in-platform vs external search visibility
Every choice influenced another part of the system, which meant constant collaboration with both product and engineering. Our PM pushed for clarity around user-facing behaviors, while the software engineer highlighted technical constraints and sequencing that would affect what was actually feasible in the short term. It required me to rewrite sections of the spec multiple times, reorganize milestones, and continuously refine Figma prototypes so engineering could evaluate the logic end-to-end.
There were moments where the scope changed overnight, or where a decision in the reports builder impacted the loading system, or where feedback revealed we needed to rethink how Q handled multi-dataset review altogether. But I learned to stay calm in the ambiguity. Instead of letting the complexity pile up, I broke decisions into structured options, packaged trade-offs clearly, and kept everyone aligned even as the work expanded.
Ultimately, that effort produced the first truly cohesive loading experience for Cypris Q — one that the team felt confident socializing internally and testing with users. What started as an overwhelming project became an opportunity to practice resilience: not just enduring pressure, but generating clarity, structure, and forward momentum for the team when everything felt complex and interconnected.
To me, that’s what resilience looks like in a product design role.
What do you think is the goal or mission that drives your creative journey?
Yes—there is, and it’s become really clear to me through my work on our AI research assistant.
My core mission is to make powerful, complex AI feel genuinely dependable and usable for people who are doing serious, high-stakes work—scientists, R&D managers, engineers—people who don’t have time for “clever” but unreliable magic.
In my current role, I see this tension every day:
Users are asking extremely complex, multi-step questions (e.g., finding specific assignees, recent patents, extracting keywords, then turning it all into tailored outreach hooks).
When it works, they’re blown away and talk about how it’s “the best job they’ve seen AI do.”
But when a chat silently stalls, sources don’t open, or context disappears from a previous session, their trust is shaken immediately.
Those moments are what drive me. I’m not just trying to make something look polished—I’m trying to close the gap between what the model could do and what the user can rely on.
So my creative journey is really about:
Designing transparent, traceable experiences (clear citations, history that feels stable, smooth ways to move from a Q answer into projects, monitoring, or deeper research).
Shaping interactions so the product is honest about its limits—no fake “I’ll deliver this later” behavior, no hidden failures.
Turning every bit of feedback—bugs, partial answers, “it stopped here and never continued”—into a more trustworthy, explainable system.
If I put it in one line:
My mission is to design AI tools that experts actually dare to build their workflows around—because the experience feels trustworthy, grounded in real sources, and respectful of how they work.
Contact Info:
- Website: https://robinahn.me/
- Linkedin: https://www.linkedin.com/in/robin-ahn-869b081bb/
- Other: https://techbullion.com/beyond-the-awards-how-robin-ahn-designs-to-educate-not-just-impress/
https://www.artisanglobal.online/article/b00eadc8-c9a1-4e1c-ba16-3152f4470757
Image Credits
I have the credits