full ux case study 

Crunchbase AI Company Profiles
Role: Lead Product Designer
I led the redesign from research through QA, partnering with Product, Engineering, Data Science, Product Marketing, Sales, and customer-facing teams. My work focused on introducing Predictions & Insights into the profile experience, redesigning the information architecture around user decision-making, and creating a foundation for post-launch improvements around financial context, monetization, and prediction trust.

Featured in the Wall Street Journal  
"Can AI Predict the Next Big IPO? Crunchbase Thinks So"
By Belle Lin

Project overview & reframing the company profile experience
Crunchbase’s company profile has historically been one of the most important surfaces in the product. It is where users go to understand a company, validate an opportunity, and decide what to do next. As Crunchbase evolved from a company data provider into an AI-powered company intelligence platform, the profile became the foundation for that shift. The page could no longer simply organize facts about a company. It needed to help users understand what those facts meant, how a company was changing, and what might happen next.

A high-traffic surface with high-itent decisions
The company profile was not a low-stakes content page. It was one of Crunchbase's primary evaluation surfaces, where uses assessed companies, validated opportunities, and chose their next action. 

11.4M desktop pageviews, 20.9M clicks on the profile, 47.8K unique users within a three-month period. And 39.6M pageviews over the following year. At this scale, every change to the profile expetrience had the potential to shape how users understood Crunchbase's new AI-powered intelligence layer. 

Trusted data, manual synthesis
The previous profile gave users access to valuable company information, but the experience required them to connect the story themselves. Key signals were split across tabs, and users had to synthesize what the data meant.
 

One profile had to support multiple decisions
Three UX problems converged on the same surface: fragmented IA, multiple workflows, and AI signals that needed a trust foundation.
Fragmented IA: the tab model forced users to synthesize company signals across isolated destinations
Multiple workflows: investors, GTM teams, and founders each used the profile differently, but the old IA was organized around data types rather than decision paths.
AI signals needed a trust foundation: users were interested, but only when they could understand what data supported the signal.
Design challenge: design one profile experience that could support different evaluation workflows while making predictive signals feel credible.

A prediction alone was not enough
Research showed that users needed evidence before trusting AI-generated findings. They were asking how the signal was formed, what data supported it, and whether they could inspect the evidence before using it.

The common pattern was:
Prediction appears → reasoning is unclear → user returns to fundamentals

This reframed the design problem. We were not just designing prediction cards. We were designing the evidence system that made those predictions usable.

Why the Funding Prediction resonated with users:
Funding Prediction resonated because it paired a forward-looking claim with familiar financial context, visible contributing factors, probability, timing, and a path to inspect the underlying data. This made Funding Prediction a bridge between trusted historical data and new predictive intelligence.

The pattern became: Prediction statement, supporting evidence, probability and timeframe, validation path

Principles that guided decision-making
1. Organize the profile as a company story: what happened, what is happening now, and what might happen next.
2. Support multiple workflows from one surface: the page architecture needed to serve investors, founders, and GTM teams.
3. Create a repeatable system for new AI signals: claim, evidence, timing, detail path, and feedback.

One surface for company evaluation
The key design approach was sequencing familiar data, predictive interpretation, and validation paths into one coherent evaluation experience. The launch experience included: header, overview, Predictions & Insights, financials and market intelligence, left navigation, and detail pages. The key design move was not simply adding AI cards. It was sequencing familiar data, predictive interpretation, and validation paths into one coherent company evaluation experience.

Key UX decisions that shaped the new experience
The new AI profile created a more opinionated structure around momentum, prediction, evidence, and market context. Scores in the sticky header traveled with the user. Left navigation helped users move through a long, data-rich page without losing context. Funding Summary stayed near the Overview chart. Consistent P&I patterns reduced cognitive load.

From tabs to a profile landing page
The new experience also moved the profile from a tabbed experience to a landing page model. Instead of forcing users to leave context to find different types of data, the new IA created a single scrollable page with vertical navigation, section anchors, and detail paths. This helped users move between Overview, Predictions & Insights, Financials, Market Intelligence, People, News, and Technology without restarting their evaluation.

A repeatable system for new AI signals
Predictions & Insights needed a repeatable structure so users could understand unfamiliar AI-powered signals. Each tile included timestamp, main statement, supporting description, indicators/evidence, feedback, and workflow CTAs. This structure turned AI outputs into a cohesive product experience.

Designing a repeatable AI system
Growth Score, Heat Score, Funding Prediction, Growth Prediction, IPO and Acquisition predictions, notable events, and research insights had different data models, but users needed a consistent way to evaluate them.

Launch impact
The redesigned profile launched in February 2025 and created immediate engagement with Predictions & Insights.

~9,000 P&I upsell clicks in Week 1
~208 trial starts in Week 1
~$13K estimated net-new ARR from Week 1 trial starts

Building through iteration
The AI launch created a feedback loop for improving trust, hierarchy, and adoption. Financial data moved into Overview, the P&I; feedback modal captured evidence-guided corrections, and upsell/gating patterns continued to evolve.

Reflection
This project clarified one of the most important lessons in designing AI-powered products: users do not trust predictions because they are explained well. They trust them when they are grounded in data they already believe in.

The launch also made it clear that trust couldn’t be designed all at once. It had to be earned over time through continuous learning and iteration. Many of the most impactful improvements came after launch, as research revealed new opportunities to help users evaluate not just the prediction, but the model behind it.

Continue reading → Building trust in AI Predictions & Insights