Transform Data-Heavy System into Intelligent Search Experience with High Value Insights
Laxis’s AI sales tools help revenue teams automate daily tasks like lead generation and CRM updates, so they can focus on closing qualified opportunities and acting at the right moment.
Case Study
Team
Product Designer (Me)
Dev: 2 Front & 3 Backend
Product Manager
Sales
As the Product Designer, I led the redesign of the Prospect Research experience to help our users identify their prospective customers and grab the opportunity with qualitative sales insights.
2025-03
Launch
2025-06
+18.4%
Weekly Active Users
14→8 mins
Average Session Time
* To comply with my non-disclosure agreement (NDA), I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views of Laxis, Inc.
The Context
When a Powerful Database Isn't Enough
Although Laxis partnered with a leading B2B data provider that has over 300 million data points, the declining retention rate indicated that simply having a vast database wasn't improving the prospecting process.
The mismatch was between the data available and the clarity needed to act on it.
Discovery
Watching Experts Running Research
Virtual Meeting Room
Internal & External Sales Experts
When I began the investigation, not knowing where to start, I approached to experts for advice. I wanted to understand how they used the tool in their prospecting workflows.
My Hypothesis 🤔
Users were struggling to find contact data or there were usability issues.
What I Actually Found 😯
Massive Data ≠ Decisions. Users were struggling to understand which contacts were worth pursuing and why.
The Challenge
How might we help users turn guesswork into a clear picture of which contacts are worth pursuing so they can focus on having conversations that actually move deals forward?
Ideation
Separate Complex Workflows - Start Simple, Go Deep as Needed
I used previous research insights to sort out the structure of top-level navigation, e.g. the core filters, data view & saved profile, AI research.
Hand sketching helped me quickly iterate my ideas then I translated the ideal version to a low-fi wireframes to align with PM and stakeholders.
Using a systems-oriented approach to create hi-fi mockups from the foundation, that balance individual components with the overall experience
Integrating User Feedback to Map Core Interaction
I used a progressive disclosure approach to reduce UI complexity and guide end-users in sorting the data based on their ideal profile. The AI research will provide them with more insights without losing their current results.
Validation
Does the New Workflow Build Trust and Speed Up Decisions?
Unmoderated Usability Testing
5 Participants from Marketing or Sales Team
After we made an MVP, I ran 5 moderated usability sessions with our internal team. We weren’t only testing whether users could use the tool, but also testing whether they could trust it for further customer outreach. These sessions validated whether our workflow gave users enough clarity, rationale, and control to identify high-value prospects quickly and decisively.
How should we improve
Don’t: Overload users with options
The Prospect Research data table is powerful, but it can be hard for non-technical users to navigate. Instead of complicating the table further, I suggest we integrate Laxis's AI chat with the Prospect Research database. This way, users can simply ask the AI to conduct the search like, "show me SaaS companies in the Pacific Northwest with 50 to 200 employees" and get a concise summary or shortlist instead of just raw data. Users will also have the option to switch between chat view and database view.
1.1 Finalized version
1.0 Initial version
Don’t: Prioritize information density over clarity
In the initial design, AI insights were displayed in a side panel, requiring users to frequently switch between the panel and the main table. I revamped the layout to present detailed research insights in an expandable section, so that users can simply click on a target to view details such as contact info, company profile or AI-generated insights etc. This keeps the main table easy to scan while offering a dedicated area for users to delve deeper into the context and enhance their understanding without interruptions.
2.1 Finalized version
2.0 Initial version
Final Snapshot
Accessible Niche Insights on Prospects from 300M Verified B2B Contact Data
No more manual data hunting, Laxis automatically pulls relevant buying signals for building accelerated pipeline
Walk-through of New Flow Connecting Laxis AI & Search
Results
Turning Research Into a Competitive Advantage
By shifting the focus from "data provider" to "sales partner," the impact has shown that users weren't just searching more. They were finding enough value and willing to 'pay' for the product.
Separating "Scan" from "Research" Modes
Surfaces a compact list of accounts highlighting only the attributes needed for quick triage (ICP fit, key buying roles, and high-value signals)
Prioritizing Signals over Static Profiles
The new AI integration delivers specific "hooks," giving reps the confidence to message high-value prospects immediately.
Operational Efficiency
We automate repetitive data collection, so sales teams can focus on strategy and customer connections.
Other Projects
Establishing Trust in Legal Tech with a Scalable Design System
View Project
Connect Prospect Insights to Action: Automate Personalized Outbound Campaign at Scale
View Project
Let’s build something special
© 2026, Sky Yang
Licenses
Transform Data-Heavy System into Intelligent Search Experience with High Value Insights
Laxis’s AI sales tools help revenue teams automate daily tasks like lead generation and CRM updates, so they can focus on closing qualified opportunities and acting at the right moment.
Case Study
Team
Product Designer (Me)
Dev: 2 Front & 3 Backend
Product Manager
Sales
As the Product Designer, I led the redesign of the Prospect Research experience to help our users identify their prospective customers and grab the opportunity with qualitative sales insights.
2025-03
Launch
2025-06
+18.4%
Weekly Active Users
14→8 mins
Average Session Time
* To comply with my non-disclosure agreement (NDA), I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views of Laxis, Inc.
The Context
When a Powerful Database Isn't Enough
Although Laxis partnered with a leading B2B data provider that has over 300 million data points, the declining retention rate indicated that simply having a vast database wasn't improving the prospecting process.
The mismatch was between the data available and the clarity needed to act on it.
Discovery
Ideation
Validation
Results
Watching Experts Running Research
Virtual Meeting Room
Internal & External Sales Experts
When I began the investigation, not knowing where to start, I approached to experts for advice. I wanted to understand how they used the tool in their prospecting workflows.
My Hypothesis 🤔
Users were struggling to find contact data or there were usability issues.
What I Actually Found 😯
Massive Data ≠ Decisions. Users were struggling to understand which contacts were worth pursuing and why.
The Challenge
How might we help users turn guesswork into a clear picture of which contacts are worth pursuing so they can focus on having conversations that actually move deals forward?
Discovery
Ideation
Validation
Results
Separate Complex Workflows - Start Simple, Go Deep as Needed
I used previous research insights to sort out the structure of top-level navigation, e.g. the core filters, data view & saved profile, AI research.
Hand sketching helped me quickly iterate my ideas then I translated the ideal version to a low-fi wireframes to align with PM and stakeholders.
Using a systems-oriented approach to create hi-fi mockups from the foundation, that balance individual components with the overall experience
Integrating User Feedback to Map Core Interaction
I used a progressive disclosure approach to reduce UI complexity and guide end-users in sorting the data based on their ideal profile. The AI research will provide them with more insights without losing their current results.
Discovery
Ideation
Validation
Results
Does the New Workflow Build Trust and Speed Up Decisions?
Unmoderated Usability Testing
5 Participants from Marketing or Sales Team
After we made an MVP, I ran 5 moderated usability sessions with our internal team. We weren’t only testing whether users could use the tool, but also testing whether they could trust it for further customer outreach. These sessions validated whether our workflow gave users enough clarity, rationale, and control to identify high-value prospects quickly and decisively.
How should we improve
Don’t: Overload users with options
The Prospect Research data table is powerful, but it can be hard for non-technical users to navigate. Instead of complicating the table further, I suggest we integrate Laxis's AI chat with the Prospect Research database. This way, users can simply ask the AI to conduct the search like, "show me SaaS companies in the Pacific Northwest with 50 to 200 employees" and get a concise summary or shortlist instead of just raw data. Users will also have the option to switch between chat view and database view.
1.1 Finalized version
1.0 Initial version
Don’t: Prioritize information density over clarity
In the initial design, AI insights were displayed in a side panel, requiring users to frequently switch between the panel and the main table. I revamped the layout to present detailed research insights in an expandable section, so that users can simply click on a target to view details such as contact info, company profile or AI-generated insights etc. This keeps the main table easy to scan while offering a dedicated area for users to delve deeper into the context and enhance their understanding without interruptions.
2.1 Finalized version
2.0 Initial version
Final Snapshot
Accessible Niche Insights on Prospects from 300M Verified B2B Contact Data
No more manual data hunting, Laxis automatically pulls relevant buying signals for building accelerated pipeline
Walk-through of New Flow Connecting Laxis AI & Search
Discovery
Ideation
Validation
Results
Turning Research Into a Competitive Advantage
By shifting the focus from "data provider" to "sales partner," the impact has shown that users weren't just searching more. They were finding enough value and willing to 'pay' for the product.
Separating "Scan" from "Research" Modes
Surfaces a compact list of accounts highlighting only the attributes needed for quick triage (ICP fit, key buying roles, and high-value signals)
Prioritizing Signals over Static Profiles
The new AI integration delivers specific "hooks," giving reps the confidence to message high-value prospects immediately.
Operational Efficiency
We automate repetitive data collection, so sales teams can focus on strategy and customer connections.
Other Projects
Establishing Trust in Legal Tech with a Scalable Design System
View Project
Connect Prospect Insights to Action: Automate Personalized Outbound Campaign at Scale
View Project
Let’s build something special
© 2026, Sky Yang
Licenses
Transform Data-Heavy System into Intelligent Search Experience with High Value Insights
Laxis’s AI sales tools help revenue teams automate daily tasks like lead generation and CRM updates, so they can focus on closing qualified opportunities and acting at the right moment.
Case Study
Team
Product Designer (Me)
Dev: 2 Front & 3 Backend
Product Manager
Sales
As the Product Designer, I led the redesign of the Prospect Research experience to help our users identify their prospective customers and grab the opportunity with qualitative sales insights.
2025-03
Launch
2025-06
+18.4%
Weekly Active Users
14 → 8 mins
Average Session Time
* To comply with my non-disclosure agreement (NDA), I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views of Laxis, Inc.
The Context
When a Powerful Database Isn't Enough
Although Laxis partnered with a leading B2B data provider that has over 300 million data points, the declining retention rate indicated that simply having a vast database wasn't improving the prospecting process.
The mismatch was between the data available and the clarity needed to act on it.
Discovery
Ideation
Validation
Results
Watching Experts Running Research
Virtual Meeting Room
Internal & External Sales Experts
When I began the investigation, not knowing where to start, I approached to experts for advice. I wanted to understand how they used the tool in their prospecting workflows.
My Hypothesis 🤔
Users were struggling to find contact data or there were usability issues.
What I Actually Found 😯
Massive Data ≠ Decisions. Users were struggling to understand which contacts were worth pursuing and why.
The Challenge
How might we help users turn guesswork into a clear picture of which contacts are worth pursuing so they can focus on having conversations that actually move deals forward?
Discovery
Ideation
Validation
Results
Separate Complex Workflows - Start Simple, Go Deep as Needed
I used previous research insights to sort out the structure of top-level navigation, e.g. the core filters, data view & saved profile, AI research.
Hand sketching helped me quickly iterate my ideas then I translated the ideal version to a low-fi wireframes to align with PM and stakeholders.
Using a systems-oriented approach to create hi-fi mockups from the foundation, that balance individual components with the overall experience
Integrate User Feedback to Map Core Interaction
I used a progressive disclosure approach to reduce UI complexity and guide end-users in sorting the data based on their ideal profile. The AI research will provide them with more insights without losing their current results.
Discovery
Ideation
Validation
Results
Does the New Workflow Build Trust and Speed Up Decisions?
Unmoderated Usability Testing
5 Participants from Marketing or Sales Team
After we made an MVP, I ran 5 moderated usability sessions with our internal team. We weren’t only testing whether users could use the tool, but also testing whether they could trust it for further customer outreach. These sessions validated whether our workflow gave users enough clarity, rationale, and control to identify high-value prospects quickly and decisively.
How should we improve
Don’t: Overload users with options
The Prospect Research data table is powerful, but it can be hard for non-technical users to navigate. Instead of complicating the table further, I suggest we integrate Laxis's AI chat with the Prospect Research database. This way, users can simply ask the AI to conduct the search like, "show me SaaS companies in the Pacific Northwest with 50 to 200 employees" and get a concise summary or shortlist instead of just raw data. Users will also have the option to switch between chat view and database view.
1.1 Finalized version
1.0 Initial version
Don’t: Prioritize information density over clarity
In the initial design, AI insights were displayed in a side panel, requiring users to frequently switch between the panel and the main table. I revamped the layout to present detailed research insights in an expandable section, so that users can simply click on a target to view details such as contact info, company profile or AI-generated insights etc. This keeps the main table easy to scan while offering a dedicated area for users to delve deeper into the context and enhance their understanding without interruptions.
2.1 Finalized version
2.0 Initial version
Final Snapshot
Accessible Niche Insights on Prospects from 300M Verified B2B Contact Data
No more manual data hunting, Laxis automatically pulls relevant buying signals for building accelerated pipeline
Walk-through of New Flow Connecting Laxis AI & Search
Discovery
Ideation
Validation
Results
Turning Research Into a Competitive Advantage
By shifting the focus from "data provider" to "sales partner," the impact has shown that users weren't just searching more. They were finding enough value and willing to 'pay' for the product.
Separating "Scan" from "Research" Modes
Surfaces a compact list of accounts highlighting only the attributes needed for quick triage (ICP fit, key buying roles, and high-value signals)
Prioritizing Signals over Static Profiles
The new AI integration delivers specific "hooks," giving reps the confidence to message high-value prospects immediately.
Operational Efficiency
We automate repetitive data collection, so sales teams can focus on strategy and customer connections.
Other Projects
Establishing Trust in Legal Tech with a Scalable Design System
View Project
Connect Prospect Insights to Action: Automate Personalized Outbound Campaign at Scale
View Project
Let’s build something special
© 2026, Sky Yang
Licenses