This month, we’re looking at what it means to build smart, scalable systems—whether in design, research, or strategy. From creating a design system that evolves with a fast-growing cybersecurity startup, to enhancing AI-driven research workflows, to exploring how UX teams challenge assumptions to make better decisions, this edition of Polar Updates is all about laying strong foundations for growth.
We’re also celebrating major milestones for two of our long-time partners, as PerfectScale and Metis join forces with DoiT and Dynatrace, respectively. Seeing them scale, succeed, and become industry leaders has been incredible, and we’re proud to have played a part in their journeys.
Let’s dive into this month’s insights—where design meets strategy, and innovation meets execution.
This month, we’re diving into how we helped Bold.ai, a fast-growing cybersecurity startup, lay the foundation for a scalable, flexible design system from day one. Our goal? Creating a shared language between design and development—one that evolves with their product and accelerates execution at every stage.
In the MVP phase, speed is everything—but so is resilience. Manually adjusting dozens of buttons, layouts, and components is unsustainable, especially for a product designed to evolve. Bold.ai knew that design updates, rebrands, and dark mode were inevitable.
They had one chance to build it smart from the start. That’s where design tokens came in.
Rather than designing individual components first, we established a structured system using core design tokens as the foundation.
This approach ensured every design decision was scalable and easy to maintain, eliminating inconsistencies and redundant work.
Tokens weren’t just a technical tool—they became a shared language between design and engineering, ensuring alignment and efficiency.
At Polar Hedgehog, we believe design systems should be built to scale seamlessly, not just look good. Design tokens aren’t just about efficiency—they create alignment, reduce redundant work, and future-proof products.
By taking a scalable approach from day one, Bold.ai now has a resilient foundation that evolves with their product—without adding unnecessary complexity.
Smart design decisions today prevent costly redesigns tomorrow.
We're thrilled to celebrate not one, but two major milestones for our clients this month! Both PerfectScale and Metis, long-time partners of Polar Hedgehog, have been acquired by leading companies in the cloud and observability space—DoiT and Dynatrace, respectively.
PerfectScale, known for its powerful Kubernetes optimization and governance platform, is joining forces with DoiT, a leader in cloud intelligence. This acquisition brings together deep FinOps expertise and next-level Kubernetes automation, giving cloud teams smarter ways to optimize workloads at scale. At Polar Hedgehog, we’ve had the pleasure of shaping PerfectScale’s visual language and website, helping them communicate their bold vision through design. It’s been an incredible journey, and we’re excited to see what they accomplish next as part of DoiT.
Meanwhile, Metis, a pioneer in AI-driven database observability, has been acquired by Dynatrace. Their technology helps developers and SREs troubleshoot and optimize databases with expert-level, automated insights. We’ve worked closely with Metis to develop a strong brand presence and web experience that reflects the sophistication of their platform and the ambition behind their mission. With this acquisition, Dynatrace is accelerating its vision for seamless observability—making performance monitoring and automation smarter and more accessible for teams worldwide.
To both teams: thank you for letting us be part of your story. Watching you scale and succeed has been inspiring, and we’re so proud to have contributed to your journeys. Here’s to your next chapters—and to designing for what’s next.
👉 Read More About PerfectScale’s Acquisition
👉 Read More About Metis’ Acquisition
Investment analysts rely on structured research to uncover emerging opportunities, market trends, and key players. As part of our work with our client Mathlabs, we’ve helped enhance their Explore feature, making domain research more intuitive and AI-driven.
The challenge? New-in-domain analysts often struggled with coverage gaps and unclear next steps. While Mathlabs’ AI suggested questions, they were sometimes repetitive and lacked structure, leading some analysts to turn to external tools. Through user interviews and field research, we identified that analysts naturally map out their research, organizing insights into categories and relationships.
This led us to explore a mind map approach, visually breaking down sub-domains and company connections. While this structured the research process, usability testing revealed navigation challenges, including excessive scrolling and difficulty managing complex paths.
To solve this, we refined the experience into a Kanban board structure, prioritizing content clarity and ease of navigation. Analysts can now quickly scan information, explore sub-domains efficiently, and systematically track relationships, leading to a more structured, confident decision-making process.
With these updates, Mathlabs’ Explore feature is now a more powerful, intuitive tool, helping analysts—whether experts or newcomers—achieve deeper insights with greater efficiency.
For this month’s Worthy Mention, we’re highlighting an insightful episode of the MindDesign podcast, which explores decision-making and behavior. In this episode, Tom Even interviews Noam Segal, an expert in user experience research, to discuss how UX research is conducted in large companies.
Two standout practices from the episode—"red teams" and "pre-mortem" exercises—caught our attention as valuable tools for startups looking to challenge assumptions and foster innovation.
🔴 Red Teams – Borrowed from military strategy, this approach involves adopting an adversarial mindset to uncover failures and biases before they happen. Instead of validating existing ideas, red teams challenge hypotheses through "what if" scenarios, helping companies explore alternative solutions and identify blind spots.
💡 Pre-Mortem Exercises – This technique involves anticipating potential failures in a research project before they happen, allowing teams to take proactive measures to avoid errors and biases.
These methods align closely with our own approach at Polar Hedgehog, where we actively challenge assumptions, test hypotheses, and explore alternative perspectives to ensure that ideas are not just validated, but refined, disrupted, and reimagined for greater impact.
In early 2025, AI innovators introduced “deep research” tools that autonomously conduct multi-step research, analyzing and synthesizing information from online sources. Integrated into platforms like ChatGPT and Perplexity AI, these tools generate comprehensive reports in minutes—dramatically improving research efficiency across domains.
As we explored these features, we needed to understand how to seamlessly integrate them into our design process. When tasked with improving a date picker for a log management platform, we recognized that generic UI principles wouldn’t suffice. Date pickers, while common, serve different purposes across various domains, each with unique user behaviors and technical requirements.
By leveraging ChatGPT’s deep research capabilities for field review, we explored existing solutions and identified functionalities that could enhance the user experience within the log management context. This research helped us understand the nuanced details of the date picker, such as the role of presets, the presentation of time, and the user flow of switching between hours and days. This understanding enabled us to make smarter decisions by balancing user needs with opportunities for innovation, while also adhering to established mental models and UI conventions.
As a result, we were able to refine the component with confidence, ensuring it met both functional and cognitive expectations. Ultimately, AI-assisted research saved us hours of manual field study, accelerating our development process while achieving broader coverage and deeper insights than traditional methods would allow.
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