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Muir AI
I led UX and product design for Muir AI, a seed-stage climate tech startup using machine learning and remote sensing to help corporations measure and reduce supply chain emissions. For this zero-to-one product, I partnered closely with the founders, engineering, and early customers to define and design the UX. My work focused on making complex data clear, decision-ready, and useful for sustainability and supply chain managers.
Company
Muir AI
Project length
3 months
Role
Product & UX design
Tools
Figma, Webflow
Problem
Corporations are under growing pressure to meet net-zero climate commitments, but most struggle to accurately measure emissions, especially within their supply chains which account for over 70% of their total footprint. Traditional carbon accounting methods rely heavily on generic emissions factors or manual, consultant-driven processes that are slow and expensive. They also don't scale. As a result, companies don't have the visibility they need to identify emissions hotspots or take meaningful action to reduce them.
We conducted dozens of early conversations with sustainability and procurement leaders. We learned that most teams were flying blind. Sourcing managers had no way to compare suppliers based on environmental impact, and sustainability teams were stuck with outdated data. Or even worse, the data is often incomplete to the point where companies can't make real operational decisions. They want to make improvements, but they don't have the confidence to move forward.
We conducted dozens of early conversations with sustainability and procurement leaders. We learned that most teams were flying blind. Sourcing managers had no way to compare suppliers based on environmental impact, and sustainability teams were stuck with outdated data. Or even worse, the data is often incomplete to the point where companies can't make real operational decisions. They want to make improvements, but they don't have the confidence to move forward.
Goals
Our primary goal with Muir AI was to create a platform that could deliver accurate, supplier-level emissions data and surface actionable abatement opportunities—at scale and without requiring direct supplier input. We set out to replace fragmented, consultant-driven carbon accounting with a fast, intelligent tool that made emissions data usable across both sustainability and procurement workflows.
From a UX perspective, the goal was to turn complex models and data sets into a clear, decision-ready experience. This included enabling users to estimate emissions with partial data, explore trade-offs between suppliers, and generate reports that supported both internal strategy and external disclosures. We also aimed to design a scalable system that could grow with customer needs from single-use assessments to enterprise-wide analytics.
From a UX perspective, the goal was to turn complex models and data sets into a clear, decision-ready experience. This included enabling users to estimate emissions with partial data, explore trade-offs between suppliers, and generate reports that supported both internal strategy and external disclosures. We also aimed to design a scalable system that could grow with customer needs from single-use assessments to enterprise-wide analytics.
My role
I was the sole Product & UX Designer, leading design from early concept through alpha release. I worked closely with the founding team to define the product vision and create the UX strategy for a new category of emissions intelligence tools. I was responsible for everything from early user discovery and research synthesis to wireraming, prototyping, and polished UI execution. In addition to the product experience, I also designed and built Muir’s marketing website using Webflow, helping establish the brand’s credibility and clearly communicate its value to early customers and investors.
Research & concept validation
Our research process was grounded in direct conversations with sustainability and procurement leaders at mid-sized food, beverage, and apparel companies. Through these sessions and feedback on early Figma prototypes, we learned that users were overwhelmed by fragmented emissions data and lacked tools to turn reporting into action. Many struggled to understand the source of emissions at a supplier level or evaluate tradeoffs between different sourcing options. We also got strong positive reactions to the Muir AI recommended actions and predicted impacts. These insights helped us focus the product on delivering clarity, context, and confidence—transforming complex data into decisions users could actually act on.
Outcome
Our early product and design work laid a strong foundation for both customer interest and investor confidence. The platform resonated with sustainability and procurement leaders who saw immediate value in having supplier-level emissions data they could trust and act on. Our design approach helped simplify complex data, build trust in AI-generated insights, and demonstrate the impact of emissions transparency in decision-making. Through research, we avoided costly build-outs of product features that we initially thought were important, but were found to be less valuable to customers.
These efforts contributed directly to a successful fundraise. Muir AI raised a $3.25 million seed round led by Base10 Partners and Soma Capital. After fundraising, I peeled off to start a new project at MVL and the Muir team continued product development and go-to-market efforts.
These efforts contributed directly to a successful fundraise. Muir AI raised a $3.25 million seed round led by Base10 Partners and Soma Capital. After fundraising, I peeled off to start a new project at MVL and the Muir team continued product development and go-to-market efforts.
