Jupiter Intelligence

Transforming enterprise-level climate risk analytics into a flexible and self-serve product experience.

THIS WORK IS NDA PROTECTED

If you would like to learn more, please reach out to me!

If you would like to learn more, please reach out to me!

THIS WORK IS NDA PROTECTED

If you would like to learn more, please reach out to me!

If you would like to learn more, please reach out to me!

A look into ClimateScore Global.

Timeline

Timeline

8 weeks

Role

Role

Product Designer

Team

Team

1 project lead, 2 designers

Deliverables

Deliverables

High-fidelity Figma prototype

Background

Jupiter Intelligence is a B2B climate tech company that provides in-depth and actionable climate risk analytics for clients to conduct risk assessments, drive compliance, and make resilient business decisions.

Their flagship product, ClimateScore Global (CSG) provides 22,000+ metrics across various climate perils and economic impact indicators on client-provided geofence (area of analysis) inputs.

Challenge

Jupiter Intelligence’s powerful analytics are hindered by its complex web platform. The current process for defining a geofence requires significant technical expertise.

As a result, non-technical clients are unable to independently explore CSG's offerings, meaning solution consultants must be heavily involved, going through multiple rounds of client communication and manual revision for each project.

Results

We redesigned geofencing inputting into a self-serviced experience. Now users of all backgrounds can access insightful, tailored analytics and Solution Consultants gain time for higher-touch work. Win-win!

Key Problem: The existing geofencing input process for CSG analytics is overwhelming for non-technical clients and takes up a valuable time from Solution Consultants.

01. The platform's one-size-fits-all model…

does not accommodate the varying needs of Jupiter's highly segmented customers.

02. Budget-driven contract constraints…

force consultants to base data point mapping on a pre-set budget, not on risk assessment needs.

03. Lack of self-service experimentation… 

prevent users from experimenting freely and seeing live changes, making insights difficult to understand.

01. Lack of a centralized review process… 

made reviewing and collaborating especially difficult 

02. Users needed a way to route reviews to the right expert…

or else some questions were left partially or completely unreviewed.

03. There was no clear collaborative system… 

so users had to self-assign and review thousands of questions without structured coordination.

How might we translate CSG's iterative consultant-led portfolio-building process into a self-service tool that feels equally tailored and flexible?

Our Design Approach: Reducing Complexity, Increasing Control

While our details of our process and designs cannot be shared, we reimagined the geofencing input process around the idea of guided flexibility, Here are some highlights of our redesigned flow:

Adaptable Inputs

We created multiple area of interest input paths, from manual entry to file upload, to accommodate different levels of users' data readiness and GIS familiarity.

Intelligent Presets

Our design automatically applies sampling presets based on common practices to reduce cognitive load. This provides a logical starting point that users are free to adjust to their needs.

Visual, Real-Time Feedback

"Preview-as-you-go" interactions were created, allowing users to see their edits in real-time and contextualize their input choices

Guided Customization

The input process was broken down into clear steps with embedded tooltips at each stage to assist user decision-making. Advanced options were condensed into dropdown menus, letting users control their level of customization.

Reflections & Learnings

Harnessing Constraints as a Catalyst for Better Design

It took us a while to familiarize ourselves with this complex product in a field we were unfamiliar with. At times when our focus drifted, circling back to their internal processes and working off of the established experience of our stakeholders helped us ground our research and designs in their real-world constraints.

It was through fully understanding their constraints that we were able to make key pivots that made our final solution much more effective and implementable.

Evidently, the team at Jupiter Intelligence was a delight to work with, and I'm thankful they trusted us with transforming their flagship product!

Venturing Into Climate Tech!

It was fascinating to sink my teeth into the climate tech space and tackle the complex challenge of designing for two distinct user groups: the intermediate user (Solution Consultants) and the end user (Jupiter's clients). Balancing their differing needs was an incredibly insightful process that honed my ability to consider how each design decision impacts two sides of the same coin.

6 hours

of user interviews with solution consultants, architects, and geospatial scientists

3 iterations

Low, mid, and high fidelity with constant revisions in between

40+ screens

Delivered to Jupiter's Head of Product and executives

Reflections & Learnings

Harnessing Constraints as a Catalyst for Better Design

It took us a while to familiarize ourselves with this complex product in a field we were unfamiliar with. At times when our focus drifted, circling back to their internal processes and working off of the established experience of our stakeholders helped us ground our research and designs in their real-world constraints.

It was through fully understanding their constraints that we were able to make key pivots that made our final solution much more effective and implementable.

Evidently, the team at Jupiter Intelligence was a delight to work with, and I'm thankful they trusted us with transforming their flagship product!

Venturing Into Climate Tech!

It was fascinating to sink my teeth into the climate tech space and tackle the complex challenge of designing for two distinct user groups: the intermediate user (Solution Consultants) and the end user (Jupiter's clients). Balancing their differing needs was an incredibly insightful process that honed my ability to consider how each design decision impacts two sides of the same coin.

How might we allow teams of reviewers to efficiently and collaboratively approve or deny sets of synthetic data?