Our Work

Preamble AI

AI Policy Marketplace for companies

Research and Design a Conversational AI Marketplace

Services: Product Research, Strategy, UX/UI, and Startup Accelerator

Objective: To create a prototype of the Preamble Marketplace for Investors

In the rapidly evolving AI industry, investors require a user-friendly platform that provides comprehensive insights, and that allows them to navigate potential investment opportunities with ease. Our challenge is to design and develop a prototype of the Preamble Marketplace that addresses these needs, ensuring that it is intuitive, engaging, and informative for investors seeking to explore and invest in AI tools.

Project Background

Preamble AI is a company that builds tools to help businesses set guardrails and guidelines for their AI tools. Their goal is to ensure that AI systems are legally compliant, socially aware, and aligned with company values. For example, Preamble AI can help businesses eliminate bias from their AI systems and create policies that discourage cyberbullying and hate speech. We worked with Preamble AI to identify product-market fit, two core user segments and design the core marketplace that will be the jumping off point for Preamble AI to get another round of investment to develop future products.

Scope

Process

Our process for identifying and targeting core customer segments began with a deep dive into market research. We examined the needs, behaviors, and motivations of different user groups, while conducting qualitative user interviews. In terms of benchmarking, we utilized a combination of primary and secondary research. We examined direct and indirect competitors, their offerings, strengths, and weaknesses. We analyzed their user interfaces, customer reviews, and overall user satisfaction.

Our approach to user interviews and concept testing was rooted in iterative design. We created low fidelity prototypes and conducted user testing sessions. This enabled us to gather insights about usability, user satisfaction, and areas for improvement. We then incorporated these insights into our design iterations, continuously refining until we achieved a product that met user needs and expectations.

Results

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