Polar Hedgehog proudly spotlights our work with Mathlabs, a startup specializing in streamlining market exploration, enabling analysts to efficiently identify targets, compare peers, and generate deep company insights fast. In our collaboration with Mathlabs, we help investment research analysts and venture teams in M&A, and investment decisions. With a strong focus on domain research, the platform helps analysts map their research landscape, achieve comprehensive market coverage, and conduct in-depth analysis.

The product’s AI driven Domain Research functions help investment analysts understand unfamiliar industries and identify untapped opportunities. In this case, domain refers to a specific or intersecting industry, like semiconductors, which include sub-domains such as digital chip design, chip manufacturing, and memory production. Analysts assess key players like Intel, AMD, and NVIDIA, analyzing market trends and competition to uncover investment opportunities and mitigate risks. Through structured research, they generate actionable insights for informed decisions.

Through such ongoing research, Erez Raanan, CEO of Mathlabs, observed that the platform was primarily used by two distinct types of investment analysts:

1. Domain Experts – Analysts who are well-versed in a domain and familiar with the industry trends, technological advancements, regulatory landscapes, and competitive positioning. They conduct top-down research, starting with a broad overview and systematically narrowing the focus. Analysts using this approach explore sub-domains first to ensure comprehensive coverage before diving deeper into specific companies.
2. New-in-Domain Analysts – Analysts who are exploring a domain for the first time to get the coverage and overview needed before diving deeper. Their primary challenge is that they are unaware of key players, sub-domains, or even what questions to ask. Essentially, they don’t know what they don’t know, which leads to frustrating and exhausting research, gaps in relevant discoveries, and missed opportunities.

Mathlabs entrusted our team to help them achieve their objective to equip both types of analysts with the ability to conduct thorough market research, ensuring they gain deep insights while maintaining broad coverage of their domain. Using an agile, user-centered approach, we embarked on a solution-driven process. 

Identifying the Problem

To improve the user experience of Mathlab’s AI research product, our team first set out to understand how the tool is applied. Analysts primarily utilize the Explore tool for their research, leveraging an AI chat interface, multiple sources, and a preview window of the portfolio list and other market overview features. This comprehensive suite of tools allows analysts to discover new companies, build lists, enhance insights, and gain a comprehensive understanding of a domain.

To help analysts ask the right questions and motivate them to continue exploration, we provided queries that were identified by the product's AI and guided them in continuing their domain exploration. While it helped some analysts, this solution still had limitations: 

• Repetitive suggestions that failed to provide comprehensive market coverage.
• Insufficiently focused queries makes it difficult for analysts to refine their research.
• Challenges in tracking progress, resulting in unmanageable and deficient domain exploration.

These issues diminished analysts’ confidence in the exploration process, some even turning to external tools for better coverage.

To formulate a solution, we dove deeper in understanding the user experience framing 3 key questions:

1. How might we improve new-in-domain analysts' coverage within the Explore feature?
2. How might we enhance the depth and thoroughness of domain research?
3. How might we provide analysts with a greater sense of control over the process?

We ultimately defined the problem as follows: When new-in-domain analysts start exploring, they know how to begin but struggle to continue, leading to incomplete market coverage. Suggested questions help partially but do not provide a structured, manageable, and productive process—This uncertainty and ineffectiveness discouraged busy users from being able to rely on Mathlabs’ tools.

Exploring Solutions

Our quest for the right solution was fostered by user interviews conducted by Erez and our field research. We learned that the workflow of domain exploration closely resembles mapping in the users’ minds. Analysts systematically structure and categorize information to gain a comprehensive market overview and build a clear picture. This realization led us to the concept of mind mapping within the Explore feature, enabling analysts to visually organize their research, track relationships between sub-domains, and ensure full domain coverage. 

To assess the viability of this concept, we explored competitors' solutions and sought inspiration from relevant research tools. We were inspired by whiteboard and canvas products because their high interactivity and well-known mental models featured attributes missing from the existing platform.

Specifically, these concepts enable:

• Hierarchical topic expansion, breaking down subjects into clear sub-subjects.
• Intuitive navigation, allowing analysts to see relationships between topics.
• Quick discovery of new sub-topics, using AI suggestions instead of manual queries.

Translating those insights into design we incorporated a mind map interface, where each node represented a sub-domain, while the whole map is devoted to a specific domain. This concept allowed for a visual, hierarchical breakdown of sub-domains, making it easier for analysts to explore them in an organized way.

Iterative Design

While this concept breakthrough removed significant barriers, early testing of it with Shiri Levini, Mathlabs’ in-house designer, revealed some lingering challenges:

• Analysts wanted brief descriptions for each sub-domain to understand their relevance.
• Navigation was cumbersome, requiring excessive scrolling, zooming in and out, and dragging within the mind map.

To resolve these issues, we incrementally adjusted our design and derived a new concept to try structured around Kanban board methodology. This solution prioritized content over structure, making it easier for analysts to quickly scan information, decide where to dive deeper and ensure efficient domain mapping.

Key improvements included:

• Enhanced clarity by organizing information in user-friendly format.
• Improved interaction, allowing analysts to explore sub-domains, products, or manually input queries.
• Flat Relationship Visibility – Providing a structured view of how sub-domains and companies connect, ensuring analysts easily track relationships and make informed decisions.

Once analysts achieve sufficient coverage, they can select relevant sub-domains and execute queries to identify companies for their investment portfolio.

Initial Feedback and Impact

Impact on Analysts:

Early feedback from analysts has been highly positive. The new domain exploration map enhances usability, making domain exploration more structured and intuitive. Analysts find it easier to navigate, track research progress, and confidently build a comprehensive understanding of new domains.

Impact on the Business:

With a more effective research tool, analysts are less likely to turn to external solutions to complete their research. This improved all-in-one platform reduces churn, strengthens Mathlabs' position as the authority in intelligent systems for investment research, and increases long-term brand loyalty through user engagement.

Conclusion

The evolution of the domain exploration map feature showcases how fast, research-based solutions and close collaboration drive innovation. As Mathlabs’ trusted design agency, we meticulously applied an agile, user-centered approach, aligning with market needs to deliver impactful tools.

Working closely with CEO Erez Raanan and Senior Product Designer Shiri Levin, we enriched Mathlabs’ product development with new concepts and innovative feature exploration. By leveraging iterative design, rapid prototyping, and continuous validation, we streamlined domain mapping, making research faster, clearer, and more actionable for analysts.

Indeed, we achieved Mathlab’s objective to equip all analysts with the ability to conduct meaningful, insightful market research, sustaining broad coverage of their domain. While it’s a happy outcome, the story doesn’t end there! Our ongoing collaboration with Mathlabs remains focused on continuous improvement, delivering the most valuable research solutions, ensuring analysts gain the insights they need while reinforcing Mathlabs’ leadership in the investment research technology sector.