Astrolabe
The core challenge of this project was designing a complex AI infrastructure platform from scratch, navigating considerable technical complexity without an initial clear product vision. Early on, the breadth of possibilities led to multiple directions, which required careful focus to define and align around a cohesive user experience and a systematic path forward. This situation demanded strategic clarity and thoughtful decision-making to transform initial ambiguity into a unified, user-centric vision.
We began our design process by conducting in-depth interviews with Astrolabe’s founders, helping us understand the company’s vision and core identity. Through careful mapping of existing user journeys, we identified critical process steps and pain points, ensuring the design directly addressed user challenges. Additionally, constructing detailed user personas allowed us to keep user needs at the forefront of every design decision.
Technically proficient user who utilizes existing models with greater complexity and customization. Requires advanced features, detailed technical documentation, and extensive configurability to optimize model performance.
Develops machine learning models intended for broader use. Has a primary focus on creating robust, flexible, and well-documented models that can be easily adopted by others.
After establishing the different personas, we mapped comprehensive user journeys to clearly define the end-to-end experiences for both creating and using machine learning models. This approach allowed us to identify key pain points and opportunities, ensuring the platform design directly aligned with users’ workflows and expectations. Visualizing these journeys helped guide strategic decisions, prioritize features, and establish a shared understanding among stakeholders.
We collaborated closely with Astrolabe to shape their product strategy, identifying key market opportunities, monetization strategies, and essential functionalities. By deeply understanding user challenges such as the difficulty of finding quality datasets and navigating complex workflows, we developed a strategy focused on streamlining the user experience. This included establishing clear monetization pathways that benefit both model and dataset creators, alongside creating a marketplace and intuitive tools that empower users at every technical level.
OPPORTUNITY
• Finding quality datasets is challenging because many are incomplete
• Special-purpose models are also hard to come by
• The workflows for creating and managing models have steep learning curves
MONETIZATION
• Model creators earn money from their published models
• Dataset creators earn money from their published datasets when others use them
• The Astrolabe platform receives a portion of these revenue sources
FUNCTIONALITY
• Guided workflow for training, deploying, and managing models
• Ability to “look under the hood” and customize models with code
• Marketplace for both datasets and models
After defining clear user journeys and aligning on product strategy, we were able to begin designing. We needed to create all key screens for Astrolabe’s platform. Our goal was to produce an intuitive, cohesive interface that simplified complex workflows and made powerful features accessible. Each design decision prioritized clarity, ease-of-use, and flexibility, ensuring both novice and advanced users felt confident and empowered.
We guided the founders through design and product strategy, delivered the platform’s complete end-to-end design, and ultimately helped the company secure its seed funding.
Ferras Hamad, Co-Founder, Astrolabe
