Redesigning Company Profiles for Predictive Intelligence

Crunchbase’s company profile page 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 whether to keep researching, save, export, contact, or compare.

In 2025, Crunchbase was making a major strategic shift: from a static company data provider to an AI-powered company intelligence platform. That shift created a new challenge for the profile experience. 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.

As 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.

The challenge

The previous profile page was optimized for lookup. It gave users access to company details, funding information, people, news, and related companies, but it left most of the interpretation to the user.

For investors, sales teams, and analysts, that meant extra work. Users had to move between financials, news, signals, competitors, and company details to answer questions like:

Is this company growing?
Is it likely to raise funding?
Is it gaining market attention?
Is this a company I should prioritize?
How does it compare to others in the market?

The redesign needed to preserve the trust and utility of Crunchbase’s core data while introducing a new layer of AI-powered intelligence.

Research insights

Through moderated research, customer feedback, and post-launch analysis, we found that users were excited by the new direction but needed more clarity and trust before adopting predictive features deeply.

Users described the redesigned interface as sleek, clean, and futuristic. They responded positively to natural language capabilities and saw value in signals like funding predictions. But they also asked foundational questions: What is a signal? How is this score calculated? Why is it significant? How should I use this in my workflow?

The biggest insight was that AI-powered intelligence could not stand alone. It needed to be grounded in familiar data, supported by evidence, and connected to clear next steps.

Design approach

I used four principles to guide the redesign.

1. The profile should lead with user questions, not database categories. Instead of only showing “funding,” “people,” or “news,” the experience needed to answer higher-level questions like whether a company was growing or likely to raise.
2. Predictions needed evidence. A score or label was not enough. Users needed supporting signals, contributing factors, timeframes, and confidence indicators.
3. The redesign needed to preserve core company fundamentals. Financial data, funding history, investors, and firmographics remained essential to user trust.
4. Premium intelligence needed to be visible in the product without disrupting research workflows. P&I was a major business opportunity, but upsells had to feel contextual and value-based.

The solution

The redesigned profile introduced a more modern, AI-forward experience that combined company facts, insights, predictions, recommendations, products and services, and related company discovery.

The new profile was organized around a broader company story:

Past: historical company facts, funding history, investors, and firmographics
Present: growth signals, heat, news, products, services, and market activity
Future: predictions for funding, growth, acquisition, IPO, closure, and layoffs

This structure helped users move from lookup to interpretation. Instead of forcing users to stitch together disconnected data points, the profile began surfacing meaning directly in the experience.

Post-launch iteration

After launch, we continued improving the profile based on customer feedback.

One of the strongest signals was that users still wanted easier access to financial data. In observed sessions, many users went directly to financial details, bypassing newer predictive features. We used that behavior to explore bringing financial data into the overview, plotting funding alongside heat and growth so users could see the relationship between historical funding activity and newer intelligence signals.

We also refined P&I gating and upsells. Initial gating increased trial starts, but cancellation behavior showed that we needed to better align preview value, access, and user expectations.

Finally, we began exploring prediction credibility in the product. Sales teams were already using a Live Predictions Dashboard to build confidence during customer calls, but users inside the product did not have the same context. We explored accuracy indicators, contextual callouts, and supporting detail so users could better understand why a prediction should be trusted.

Outcome

The redesign helped establish the company profile as a key surface for Crunchbase’s shift toward predictive intelligence. It introduced AI-powered signals into an existing high-intent workflow, gave users faster ways to understand company momentum, and created a foundation for monetization and trust-building.

Just as importantly, the work created a post-launch learning loop. Customer feedback showed where the experience was working, where users still needed fundamentals, and where prediction credibility needed to be made more visible.

UX case study link will be available soon 

Notable Press:
Wall Street Journal exclusive on Crunchbase's AI features, "Can AI Predict the Next Big IPO? Crunchbase Thinks So," by Belle Lin.