The Client

Clime is a digital platform designed to help users make informed, safe decisions about the products they buy, with a focus on ingredients, allergens, and ethical production. As an internal venture of Valere, Clime operates with a lean, agile approach, where validation speed is critical to the product roadmap.

The Challenge: Moving Beyond Internal Assumptions

The Clime team had completed a sleek new interface iteration, but “looking good” doesn’t always mean “working well.” Before committing resources to code the new design, the team needed to verify that the complex flows—specifically comparing products and managing chat history—were actually intuitive to new users.

They faced three specific challenges:

• The “List vs. Table” Dilemma: The design team was divided on how to display product comparisons. Some argued for a visual “List View” for mobile friendliness, while others insisted on a “Table View” for data density. Without user data, this was just a matter of opinion.
• Navigational Blind Spots: Early observations suggested that users might struggle with the transition between the active “Chat” and the “Chat History.” If users couldn’t easily retrieve past searches, the core value of the product would be compromised.
• Information Balance: Clime deals with complex data (ingredients, allergens, safety). The challenge was presenting enough detail to be useful without overwhelming the user with text. The team didn’t know if they had struck the right balance.

The Goal: Validate usability and intuitiveness to ensure the engineering team built the right product the first time.

The Solution: Rapid Qualitative Prototyping

Clime utilized Dactic to run a design validation campaign. Instead of scheduling weeks of Zoom interviews, they uploaded their context and objectives into Dactic, which then facilitated asynchronous interviews with potential users.

The Process:

1. Targeted Scenarios: The Dactic AI was instructed to guide users through specific flows: finding a product, comparing two options, and accessing previous chat logs.
2. Format Testing: Users were explicitly asked to evaluate the “List vs. Table” layouts, with the AI probing into why they preferred one over the other.
3. Friction Detection: The AI interviewer identified moments of hesitation—such as difficulty locating the search bar—and asked follow-up questions to understand the user’s mental model.
4. Feature Requests: Beyond usability, Dactic collected “wish list” items, identifying a strong user desire for personalization filters (e.g., specific allergen settings).

The Results: A Roadmap Defined by Data

The feedback from Dactic provided immediate clarity, turning design assumptions into actionable tasks.

Key Outcomes:

• Settling the Layout Debate: The data revealed that there was no single winner—users needed both. The feedback indicated that while the List view was pretty, the Table view was necessary for hard comparisons. This led to the decision to implement a toggle/hybrid view.
• Fixing Navigation Flaws: The research flagged that the search bar placement and “Chat History” flow were confusing. The team prioritized repositioning these elements before development started.
• Content Strategy Pivot: Users reported that product details were sometimes “insufficiently detailed” or “repetitive”. Clime adjusted their content strategy to include richer attribute differentiation and potential “downsides” of products to build trust.

The Voice of the Customer

“Dactic helped us settle internal design debates by bringing actual user voices to the table. While it doesn’t replace granular user testing tools, the human-like conversational flow captured a level of context and nuance that we usually only get in live interviews, giving us the confidence to move forward.”

— Karina Somiers, Product Designer at Valere for Clime Project

Conclusion: Building with Confidence

For Clime, Dactic acted as an insurance policy against bad UX. By identifying that the “search bar location was unexpected” and that “chat history lacked clarity” before the code was written, Clime avoided the high cost of re-work.

This case study illustrates that Dactic isn’t just for market research—it is a vital tool for the Product Design Lifecycle, enabling teams to validate complex interactions at scale and ensuring that when development starts, the team is building the right thing.