Enhancing Domain Exploration with AI-Domain Mapping

Enhancing Domain Exploration with AI-Domain Mapping

Enhancing Domain Exploration with AI-Domain Mapping

My Role

My Role

Product Designer

The Team

Product Designer

1 PM & Founder

3 developers

Key Contributions

Feedback Analysis

Competitive Analysis

Ideation

Design

About Mathlabs

Mathlabs is a startup specializing in streamlining market exploration, enabling investment research analysts to efficiently identify targets, compare peers, and generate deep company insights fast. In my work with Mathlabs, we help 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.

Mathlabs is a startup specializing in streamlining market exploration, enabling investment research analysts to efficiently identify targets, compare peers, and generate deep company insights fast. In my work with Mathlabs, we help 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.

Based on continuous user feedback and customer interviews, we identified two distinct types of analysts using our platform:

Our goal was to ensure that both types of analysts could achieve deep domain understanding while maintaining comprehensive coverage throughout their exploration process

🏆 Domain Experts

Analysts who are well-versed in a domain, familiar with key players, sub-domains, and research strategies. They conduct top-down research and can assess when they have achieved sufficient coverage before diving deeper.

🤔 New-in-Domain Analysts

Analysts who are exploring a domain for the first time, are unaware of key players, sub-domains, or even what questions to ask. Their challenge is that they don’t know what they don’t know—leading to gaps in research and potential missed opportunities.

Identifying the Problem

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: 

1

Repetitive suggestions
that failed to provide comprehensive market coverage

2

Lack of focused queries
making it difficult for analysts to refine their research.

3

Challenges in tracking progress
resulting in a lack of control over domain exploration.

Conclusion

These issues led to a lack of confidence in the exploration process, causing some analysts to turn to external tools for better coverage.

These issues led to a lack of confidence in the exploration process, causing some analysts to turn to external tools for better coverage.

These issues led to a lack of confidence in the exploration process, causing some analysts to turn to external tools for better coverage.

To refine our understanding of the problem, we framed three key questions:

"How might we improve new-in-domain analysts' coverage within the Explore feature?"

"How might we enhance the depth and thoroughness of domain research?"

"How might we provide analysts with a greater sense of control over the process?"

We ultimately defined the problem as follows:

Probmlem Statement

Probmlem Statement

"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 process—leading to frustration and lack of confidence in MathLabs’ tools."

"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 process—leading to frustration and lack of confidence in MathLabs’ tools."

"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 process—leading to frustration and lack of confidence in MathLabs’ tools."

Exploring Solutions

User Flow

Our quest for the right solution was fostered by user interviews and 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. 

Our quest for the right solution was fostered by user interviews and 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. 

Our quest for the right solution was fostered by user interviews and 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. 

Competitive Analysis and Inspiration

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.

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.

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.

Logo
Logo
Logo
Logo

Tree structure

Mini map

Floting actions bar

Prompt inside bubble

Prompt to generate map

We selected this approach because it enables:

We selected this approach because it enables:

We selected this approach because it enables:

1

Hierarchical topic expansion
breaking down subjects into clear sub-subjects.

2

Intuitive navigation allowing analysts to see relationships between topics.

3

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.

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.

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.

Iterating on the Design

Although the design breakthrough, we soon realized that navigating and expanding nodes through this structure became cumbersome, leading us to reconsider the format.

During early internal testing, we noticed key usability challenges:

Although the design breakthrough, we soon realized that navigating and expanding nodes through this structure became cumbersome, leading us to reconsider the format.

During early internal testing, we noticed key usability challenges:

Although the design breakthrough, we soon realized that navigating and expanding nodes through this structure became cumbersome, leading us to reconsider the format.

During early internal testing, we noticed key usability challenges:

1

Analysts wanted brief descriptions for each sub-domain
(sub-topic) to understand their relevance.

2

Navigation felt cumbersome
requiring excessive scrolling 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.

  • Enhanced clarity by structuring information in an easy-to-digest format.

  • Flat Relationship Visibility – Providing a structured view of how sub-topics connect, ensuring analysts easily track relationships and make informed decisions.

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.

  • Enhanced clarity by structuring information in an easy-to-digest format.

  • Flat Relationship Visibility – Providing a structured view of how sub-topics connect, ensuring analysts easily track relationships and make informed decisions.

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.

  • Enhanced clarity by structuring information in an easy-to-digest format.

  • Flat Relationship Visibility – Providing a structured view of how sub-topics connect, ensuring analysts easily track relationships and make informed decisions.

Flexible interaction, allowing analysts to explore sub-domains, products, or manually input queries.

Flexible interaction, allowing analysts to explore sub-domains, products, or manually input queries.

Flexible interaction, allowing analysts to explore sub-domains, products, or manually input queries.

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

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

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 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.

Early feedback from analysts has been highly positive. The new domain 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.

Early feedback from analysts has been highly positive. The new domain 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 reduces churn and strengthens MathLabs' position as a trusted, all-in-one platform for investment research, increasing long-term engagement.

With a more effective research tool, analysts are less likely to turn to external solutions to complete their research. This reduces churn and strengthens MathLabs' position as a trusted, all-in-one platform for investment research, increasing long-term engagement.

With a more effective research tool, analysts are less likely to turn to external solutions to complete their research. This reduces churn and strengthens MathLabs' position as a trusted, all-in-one platform for investment research, increasing long-term engagement.

Conclusion

The evolution of the domain map feature underscores the importance of iterative design and user feedback in developing effective research tools. By addressing core challenges, we have created a structured, intuitive process that fosters confidence, efficiency, and comprehensive market coverage for analysts.

The evolution of the domain map feature underscores the importance of iterative design and user feedback in developing effective research tools. By addressing core challenges, we have created a structured, intuitive process that fosters confidence, efficiency, and comprehensive market coverage for analysts.

The evolution of the domain map feature underscores the importance of iterative design and user feedback in developing effective research tools. By addressing core challenges, we have created a structured, intuitive process that fosters confidence, efficiency, and comprehensive market coverage for analysts.

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