Search Without The Spin
Seekr
Search Engine UX, AI / Credibility Scoring, Ethical Product Design
An innovative search engine designed to give users greater transparency, control, and confidence in the information they find online. Leveraging a proprietary algorithm, Seekr evaluates the credibility, accuracy, and objectivity of web content, allowing users to identify political biases, misinformation, and unreliable sources. Unlike traditional search engines that prioritize popularity or SEO rankings, Seekr focuses on delivering trustworthy, balanced results, empowering users to make informed decisions.
With a mission to foster transparency and promote informed browsing, Seekr offers a comprehensive view of the web while prioritizing user autonomy. By combining cutting-edge technology with an ethical approach to search, Seekr is establishing itself as the go-to platform for individuals seeking reliable, unbiased information in an era of overwhelming online content.
Platforms
Web
My Role
Product Designer
UX Architecture
Data Flow
Client Communication
Research Methods
To ensure Seekr effectively addressed the challenges of biased search results and misinformation, we employed a comprehensive, multi-method research strategy, combining qualitative and quantitative insights:
User Surveys – We polled over 500 participants to understand their frustrations with traditional search engines, uncover pain points, and identify the features users valued most. The surveys revealed key issues, including lack of transparency in ranking, difficulty distinguishing credible sources, and reliance on algorithm-driven results that often favored popularity over accuracy.
Competitive Analysis – We conducted an in-depth review of major search platforms, including Google, DuckDuckGo, and Bing, to benchmark features, transparency practices, and user experience. This analysis highlighted gaps in how search engines communicate credibility and bias, guiding our design strategy to create a platform that offered clarity and trust.
Usability Testing – Early Seekr prototypes were tested with a diverse group of users to evaluate comprehension of credibility scores, ranking explanations, and result presentation. Feedback from these sessions informed the design of intuitive layouts, contextual explanations, and interactive elements that helped users understand why results were ranked in a particular order.
Heatmaps & Eye-Tracking – We observed how users scanned search result pages, identifying patterns in attention and focus. This data informed layout optimizations, ensuring that credibility indicators, result snippets, and ranking explanations were highly visible and easy to interpret without overwhelming the user.
Outcome – By combining surveys, competitive analysis, usability testing, and behavioral observation, we gained a holistic understanding of user needs and expectations. These insights directly informed design decisions, ensuring Seekr not only provided transparent search results but also offered an intuitive, user-friendly experience that empowered users to make informed decisions.
Business Challenge
Traditional search engines frequently prioritize paid content, popular sources, or algorithmic biases, which can make it challenging for users to access neutral, transparent, and credible information. In an era of misinformation and polarized content, users often struggle to distinguish between trustworthy and biased sources, undermining confidence in the information they find online.
Seekr’s goal was to address these challenges by creating a search platform that actively promotes unbiased content ranking, flags potential misinformation, and provides users with clarity and control over how results are presented. The key challenge was not only technical—designing a proprietary algorithm to assess credibility and objectivity—but also user-focused: building an interface that feels intuitive, transparent, and reliable, while remaining scalable for a wide audience.
The platform needed to strike a delicate balance between trust, usability, and transparency, ensuring users could quickly find information, understand the credibility of sources, and make informed decisions with confidence. Seekr’s mission was to redefine how people interact with search engines by placing accuracy, neutrality, and user empowerment at the center of the experience.
Failed Iterations & Key Learnings
Trust Score Visibility – Our initial design displayed a detailed credibility score for each search result, intended to provide full transparency. However, usability testing revealed that users found the numeric scores overwhelming and difficult to interpret quickly. To address this, we simplified the design with a color-coded system and a concise, plain-language explanation for each result. This change made credibility assessments immediately understandable while maintaining transparency and trust.
Advanced Filtering System – Early prototypes included extensive, deep filtering options to give users maximum control over search results. While feature-rich, testing showed that most users were overwhelmed by the complexity and rarely used advanced filters. In response, we introduced predefined, quick-access filters tailored to common search intents, striking a balance between control and usability while ensuring users could find relevant results quickly.
AI-Generated Summaries – The initial AI-powered summaries aimed to provide quick context for search results but were perceived as robotic and difficult to read. Iterative testing and refinements to our natural language processing models improved readability, tone, and relevance, resulting in concise, helpful summaries that users could rely on to understand content at a glance.
Key Learnings – These iterations reinforced the importance of balancing transparency, control, and simplicity. By testing assumptions early, observing real user behavior, and iterating thoughtfully, we were able to create an experience that was both intuitive and trustworthy. Users could now quickly evaluate the credibility of search results, filter effectively without confusion, and understand AI-generated content—all of which improved confidence and engagement with the platform.
Designing Seekr’s Advanced Filtering
1. Initial Filter Design
The first iteration of Seekr’s filtering system featured a slider intended to help users gauge and refine search results based on the relative amount of content. Multiple lines along the slider represented content volume, with movement toward the center reducing the amount of content displayed.
While conceptually innovative, usability testing revealed a key challenge: a large portion of content was tagged as “just right of center,” making it difficult for users to interpret the meaning of the slider accurately. Users often felt uncertain about what results would appear and how the slider adjustments impacted the content shown. This confusion highlighted the need for a clearer, more intuitive way to visualize content distribution and help users make confident filtering decisions.
The insights from this iteration prompted a redesign focused on clarity, accessibility, and ease of use. We explored alternatives such as predefined quick-access filters, visual cues, and descriptive labels to guide users. By iterating on this approach, we were able to provide a filter system that conveyed the intended meaning clearly while maintaining control over content relevance and user preferences.
Original Filtering Sketch
Original Filter Design
2. User Testing and Simplification
During usability testing, it became clear that users preferred a simpler, more intuitive filtering system to evaluate political biases in search results. The initial, more granular slider system was confusing and slowed decision-making. Based on this feedback, we reimagined the filter interface with clearly defined, easy-to-understand categories:
L (Left)
L/C (Left, Center)
C (Center)
C/R (Center, Right)
R (Right)
This streamlined approach allowed users to quickly filter results according to political leaning without feeling overwhelmed by overly complex options. The simplified design not only enhanced usability but also promoted adoption and engagement, as users could confidently navigate search results while understanding the bias context at a glance.
3. Competitive Research and Feature Development
To ensure Seekr stood out in the crowded search engine landscape, we conducted thorough competitive research across platforms like Google, DuckDuckGo, and Bing, examining how they handled transparency, credibility, and bias. These insights guided the development of unique features that differentiated Seekr from traditional search engines.
One key innovation was the Historical Searches Module. This feature enables users to track how a topic’s political leanings and representation have shifted over time, providing context and helping users identify trends in misinformation or bias. By offering this level of insight, Seekr empowers users to make more informed decisions and promotes critical thinking about online content.
Additional feature developments were guided by user research and testing, ensuring that each module added meaningful value, enhanced trust, and maintained a simple, intuitive interface. This focus on user-centered design combined with data-driven insights helped position Seekr as a search platform that not only delivers results but educates and informs users in a transparent and engaging way.
Competitive Research for New Filter Options
4. Implementation and Refinement
By prioritizing user adoption, preferences, and feedback throughout the design process, we were able to implement features and modules that truly resonated with Seekr’s audience. Every decision—from simplifying political bias filters to introducing innovative tools like the Historical Searches Module—was guided by direct user insights and iterative testing. This focus ensured that the platform not only met functional requirements but also fostered trust, clarity, and ease of use.
The result was a refined, transparent, and highly functional search experience that empowered users to quickly assess the credibility of content, understand political leanings, and make informed decisions. By centering design around real user behavior and preferences, Seekr successfully delivered a platform that is intuitive, engaging, and differentiated in the crowded landscape of search engines.
Proposed Wireframes for New Filters
New Filter Proposal - High Fidelity
New Proposed Filter Application
Metrics & Outcomes
🔎 35% Increase in User Trust Ratings – By prioritizing transparency, credibility indicators, and clear explanations of content ranking, Seekr significantly boosted user confidence. Compared to traditional search engines, trust ratings increased by 35%, demonstrating that users valued the platform’s commitment to unbiased and reliable information.
📊 50% Preference for Transparency Features – Half of the users surveyed indicated that Seekr’s transparency tools—such as trust scores, bias filters, and ranking explanations—were preferable to any features offered by competing search engines. These features effectively differentiated Seekr in the market and reinforced its mission to promote informed decision-making.
⏳ 20% Faster Search Completion Rate – Streamlined workflows, simplified filters, and intuitive AI-generated summaries enabled users to complete searches more efficiently. On average, search completion time decreased by 20%, allowing users to access relevant and credible information more quickly while maintaining confidence in their results.
🚀 High Early Adoption and Engagement – Seekr’s user-centered design and transparency-driven features contributed to strong early adoption, with a 40% return rate among beta users. This high engagement indicated that users found real value in the platform and were motivated to continue using it for everyday search tasks.
Overall Impact – By combining user research, iterative design, and data-driven refinements, Seekr successfully established itself as a trusted, efficient, and user-friendly search engine. The measurable improvements in trust, preference, efficiency, and retention reflect the platform’s ability to solve a critical gap in the market for transparent and unbiased online search.
Key Takeaways
User-Centered Research – Engaging directly with users through surveys, usability testing, and behavioral analysis was essential in uncovering pain points with traditional search engines. These insights informed every design decision, ensuring that Seekr addressed real user needs for transparency, credibility, and clarity.
Iterative Design & Testing – Continuous prototyping and feedback loops allowed us to refine features such as bias filters, trust scores, and AI-generated summaries. Iterative testing ensured that each adjustment enhanced usability, improved comprehension, and aligned with user expectations, resulting in a search experience that was intuitive and reliable.
Simplified, Clear User Interface – By focusing on clarity, visual hierarchy, and easy-to-understand indicators, we reduced cognitive load and made critical information accessible at a glance. This design approach not only improved engagement but also strengthened user trust, reinforcing Seekr’s commitment to transparency and unbiased results.
Strategic Market Positioning – Seekr successfully established itself as a credible, user-focused alternative in the search engine market. By combining transparency, usability, and innovative features like the Historical Searches Module, the platform differentiated itself from competitors while empowering users to make informed decisions.
Overall Insight – The project demonstrates how a Discover → Define → Design → Deliver methodology, grounded in research and iterative refinement, can produce a platform that is not only functional but also trusted and valued by its users. Seekr’s success highlights the power of aligning design with both user needs and business goals.
