AI Search
Senior Product Designer
2025
Background
Adapting to evolving expectations
The rise of AI has reshaped our expectations of digital experiences. Products have evolved from passive systems to intelligent tools, raising the bar for experiences that are more intuitive and better able to anticipate user needs.
Affinity recognized the need to move beyond manual workflows to a more intelligent system aligned with evolving expectations. As a platform built to surface relationship insights, this meant enabling users to uncover insights and act on them more easily.
At the time, customer expectations were evolving and many were beginning to feel that Affinity was no longer meeting their baseline needs. Within just a year, our survey showed a significant rise in customers planning to use AI in their daily workflows, highlighting how rapidly expectations had shifted.
While it was clear we needed to act, we wanted to do so thoughtfully, avoiding the temptation to introduce AI features without a clear understanding of their actual value.
👥 Team
I worked in a cross-functional squad that included 1 PM, 1 front-end engineer, 2 ML engineers, and a team of back-end engineers.
💼 Role
I was the sole designer and led the end-to-end design process, from research and vision, to defining the MVP and product roadmap.
Goals
How can AI create meaningful value for our users?
We set out to understand how AI could meaningfully improve our product focusing on uncovering gaps in the current experience. While we knew customers were increasingly asking for AI offered in the product, we didn't have as much clarity on what that actually meant in practice.
Our goal with this work was to define a clear direction for AI at Affinity, determine how it should fit into the product experience, and identify a starting point that could deliver value quickly.
Research
Uncovering gaps in the current experience
To kick off the research, I partnered with my PM to conduct interviews with the goal of understanding how customers were thinking about AI. To avoid bias, we kept conversations intentionally broad and focused on uncovering gaps in the overall experience rather than placing too much emphasis on AI.
I developed a set of exploratory concepts and asked users to rank them based on relevance to their day-to-day workflows.
Across multiple sessions, a clear need emerged: users wanted an easier way to ask questions and get answers from their data in Affinity. This concept ranked #1 out of 6 with an average score of 4.3 out of 5 in perceived value.
While users could technically access this information, doing so required a lot of manual effort, highlighting an opportunity for us to make information access easier and more aligned with expectations.
Problem
Users relied on keyword searches, filters, and digging through notes
The core problem was that when users had questions about their data in Affinity, they relied heavily on keyword searches, filters, and digging through notes to find answers. For investors managing multiple deals, this made quick and easy access to information critical.
While users relied on global search to navigate, it often fell short due to its rigidity. Even with some recent improvements, users still expected a smarter search experience.
Approach
Where should we start?
We explored multiple approaches to integrating AI into Affinity, which included a full-width immersive chat, an assistive modal, and an embedded experience within global search. While each of these offered value, we needed to identify a starting point that balanced feasibility with the highest immediate impact.
We identified global search as the most effective entry point for introducing AI into the product. As the most frequently used features of the product, it was where users already spent a lot of their time, which made it a natural place to meet users where they already were.
Our core hypothesis was that introducing semantic search would enable users to find information faster and with less effort, improving their overall efficiency and satisfaction.
Explorations
We learned that AI needed to be approached as a cohesive ecosystem
After early explorations, we brought a few concept back to customers and uncovered insights that challenged our assumptions:
There was still some skepticism toward AI results: There was a need for more transparency into why results were generated and the sources they referenced.
Keyword search still held value: Users saw semantic search as more of a complement, and not a replacement, as keyword search was still preferred for its speed and precision at times.
There was a clear need for a more conversational experience: Users preferred to explore results through natural back-and-forth interactions, especially when recalling information or when their queries weren't fully defined.
Although our focus was placed on introducing semantic search as an embedded experience, it was important to account for the broader ecosystem and its evolution in future phases.
Iterations
Bridging insights to our solution
While the insights were clear, translating them into the right type of experience surfaced a few key dilemmas. We conducted a final round of feedback with customers to evaluate options we were exploring.
Final Designs
Evolving the system while preserving what works
Our main goal was to preserve the default keyword search experience while making it easy for users to access an AI-powered search, especially in cases where traditional searches didn't return the results users needed.
I led the effort to define a visual system for how AI would be represented across the product. While we explored more experimental directions, we ultimately prioritized a design language that was more recognizable and leveraged patterns users were already familiar with. This system was later adopted by marketing, which unified the product and brand experience.
Zooming out, the vision was to transform Affinity from a platform where users manually uncover insights into an intelligent partner that works alongside them. The chat experience was our starting point, but the long-term vision was a proactive system that surfaces recommendations, insights, and opportunities at the right moment.
Impact
Establishing a shared vision across the org
The strategy and implementation plan were later presented to the executive tea and helped shape Affinity's AI vision, which was later shared with customers at Campfire 2025.
While I wasn't on the team long enough to see semantic search through its full implementation, early feedback showed positive indicators from customers and a clear alignment with their expectations.
Takeaways
Design's role in creating clarity and alignment
The AI work came with strong leadership interest and an elevated level of visibility as multiple teams depended on it for their roadmaps. In order to keep everyone aligned, consistent communication was critical. We regularly shared research findings and work in progress through weekly product updates and Slack posts to ensure teams were staying informed.
Equally as important was articulating the long-term vision for AI. By creating hi-fi mockups and prototypes early on, we were able to align stakeholders and build confidence around why we were prioritizing certain options and starting where we did.
The experience taught me that when working on ambiguous, emerging spaces like AI, design can play a critical role in not just shaping the product, but also in creating clarity and alignment across the org.