Project Galaxy: Turning Archives into a Living Intelligence

Project Galaxy: Turning Archives into a Living Intelligence

Project Galaxy: Turning Archives into a Living Intelligence

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Galaxy
AI Product
AI Product
UX Design & Research
UX Design & Research
UI Design
UX Design & Research
UX Design & Research
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Introduction

I worked as the UX intern at Schlumberger Software Technology Innovation Center (STIC), a research center with a goal of leading the oilfield digital transformation by taking advantage of technology trends driven by Silicon Valley companies.

On this project, I worked with a software engineer intern, and a senior UX designer (my mentor). I was responsible for the end-to-end design process, including user research, concept development, prototyping, and UI design/ hand-off documentation.

I worked as the UX intern at Schlumberger Software Technology Innovation Center (STIC), a research center with a goal of leading the oilfield digital transformation by taking advantage of technology trends driven by Silicon Valley companies.

On this project, I worked with a software engineer intern, and a senior UX designer (my mentor). I was responsible for the end-to-end design process, including user research, concept development, prototyping, and UI design/ hand-off documentation.

I worked as the UX intern at Schlumberger Software Technology Innovation Center (STIC), a research center with a goal of leading the oilfield digital transformation by taking advantage of technology trends driven by Silicon Valley companies.

On this project, I worked with a software engineer intern, and a senior UX designer (my mentor). I was responsible for the end-to-end design process, including user research, concept development, prototyping, and UI design/ hand-off documentation.

Curiosity Unfolding

From data visualization problem to bigger picture

At STIC, every year brings dozens of bold experiments — from applications for newest XR headsets to digital twins — all logged into the internal research repository, called Radar.

At first, my assignment seemed straightforward: redesign the data visualization of past research projects. The radar tool looked sleek, but in conversations around the center it was often dismissed as “just a cool presentation layer.”

But as I talked to more people, I began to see Radar differently — not as a static map, but as the key to a hidden sea of knowledge.

This is what a Radar look like, but there are more than 20 more them!

This is what a Radar look like, but there are more than 20 more them!

This is what a Radar look like, but there are more than 20 more them!

Reframed Problem

Buried insights in sea of research documentations

Buried insights in sea of research documentations

Buried insights in sea of research documentations

Today’s Radar often bury insights in static lists and fragmented pages, making it hard for users to explore, connect, or act on innovation meaningfully.

There’s no intuitive way to see the bigger picture, nor a personalized path to uncover what matters most.

Users need a system that supports both discovery and depth—one that sparks curiosity while adapting to their evolving goals.

Today’s Radar often bury insights in static lists and fragmented pages, making it hard for users to explore, connect, or act on innovation meaningfully.

There’s no intuitive way to see the bigger picture, nor a personalized path to uncover what matters most.

Users need a system that supports both discovery and depth—one that sparks curiosity while adapting to their evolving goals.

Today’s Radar often bury insights in static lists and fragmented pages, making it hard for users to explore, connect, or act on innovation meaningfully.

There’s no intuitive way to see the bigger picture, nor a personalized path to uncover what matters most.

Users need a system that supports both discovery and depth—one that sparks curiosity while adapting to their evolving goals.

Discover Bigger Picture

Discover Bigger Picture

Discover Bigger Picture

Build Depth for Personalized Path

Build Depth for Personalized Path

Build Depth for Personalized Path

User Research

Diverging needs of Leaders, Engineers, and Researchers

Diverging needs of Leaders, Engineers, and Researchers

Diverging needs of Leaders, Engineers, and Researchers

Through 10 stakeholder interviews and 200+ notes, three distinct personas emerged, each with different goals and ways of navigating Radar.

Through 10 stakeholder interviews and 200+ notes, three distinct personas emerged, each with different goals and ways of navigating Radar.

Through 10 stakeholder interviews and 200+ notes, three distinct personas emerged, each with different goals and ways of navigating Radar.

Gradient 1 - Blue
Gradient 2 - Purple
Gradient 1 - Blue
Gradient 2 - Purple
Gradient 1 - Blue
Gradient 2 - Purple
Solution

Exploration and personalization, working in tandem

Exploration and personalization, working in tandem

Exploration and personalization, working in tandem

To address broad and individual needs, I divided the solution into two complementary parts:

To address broad and individual needs, I divided the solution into two complementary parts:

To address broad and individual needs, I divided the solution into two complementary parts:

Galaxy Visualization: Enables open-ended exploration, revealing hidden connections through an intuitive, visual map.

Galaxy Visualization: Enables open-ended exploration, revealing hidden connections through an intuitive, visual map.

Galaxy Visualization: Enables open-ended exploration, revealing hidden connections through an intuitive, visual map.

Personal Dashboard: Adapts to individual roles and goals, surfacing timely, relevant insights that support day-to-day decisions.

Personal Dashboard: Adapts to individual roles and goals, surfacing timely, relevant insights that support day-to-day decisions.

Personal Dashboard: Adapts to individual roles and goals, surfacing timely, relevant insights that support day-to-day decisions.

Galaxy

Navigate the R&D landscape through an interactive galaxy visualization.

Navigate the R&D landscape through an interactive galaxy visualization.

Navigate the R&D landscape through an interactive galaxy visualization.

For the MVP, we complied all the data from past radar pages into one, dynamic, interactive knowledge graph that allows users explore projects with flexibility and joy.

Each view — Technology, Domain, or Team — serves as a lens through which users can explore and compare projects based on different organizing principles. While the underlying dataset remains the same, these views reorganize the relationships and clusters based on user priorities.


For the MVP, we complied all the data from past radar pages into one, dynamic, interactive knowledge graph that allows users explore projects with flexibility and joy.

Each view — Technology, Domain, or Team — serves as a lens through which users can explore and compare projects based on different organizing principles. While the underlying dataset remains the same, these views reorganize the relationships and clusters based on user priorities.


For the MVP, we complied all the data from past radar pages into one, dynamic, interactive knowledge graph that allows users explore projects with flexibility and joy.

Each view — Technology, Domain, or Team — serves as a lens through which users can explore and compare projects based on different organizing principles. While the underlying dataset remains the same, these views reorganize the relationships and clusters based on user priorities.


Try out the Galaxy Visualization MVP

Try out the Galaxy Visualization MVP

Try out the Galaxy Visualization MVP

Technology View

What it does: Groups projects by the technologies they apply or explore.

  • Hierarchy: Tech field → Specific methods/models → Projects

  • Why it’s helpful: Ideal for identifying innovation trends, technical overlaps, or evaluating tech adoption maturity.

Technology View

What it does: Groups projects by the technologies they apply or explore.

  • Hierarchy: Tech field → Specific methods/models → Projects

  • Why it’s helpful: Ideal for identifying innovation trends, technical overlaps, or evaluating tech adoption maturity.

Technology View

What it does: Groups projects by the technologies they apply or explore.

  • Hierarchy: Tech field → Specific methods/models → Projects

  • Why it’s helpful: Ideal for identifying innovation trends, technical overlaps, or evaluating tech adoption maturity.

Domain View

What it does: Groups projects based on problem space within SLB, application area, or impact field (e.g., “Subsurface,” “Grid Modernization”).

  • Hierarchy: Domain → Product → Projects

  • Why it’s helpful: Useful for strategists or external partners to see applied impact areas and identify gaps or overlaps in research.

Domain View

What it does: Groups projects based on problem space within SLB, application area, or impact field (e.g., “Subsurface,” “Grid Modernization”).

  • Hierarchy: Domain → Product → Projects

  • Why it’s helpful: Useful for strategists or external partners to see applied impact areas and identify gaps or overlaps in research.

Domain View

What it does: Groups projects based on problem space within SLB, application area, or impact field (e.g., “Subsurface,” “Grid Modernization”).

  • Hierarchy: Domain → Product → Projects

  • Why it’s helpful: Useful for strategists or external partners to see applied impact areas and identify gaps or overlaps in research.

Team View

What it does: Organizes projects by contributing teams (e.g., AI Lab, Frontend, Robotics).

  • Hierarchy: Team → Sub teams within a Lab → Projects

  • Why it’s helpful: Great for internal alignment, performance tracking, and collaboration mapping across the org.

Team View

What it does: Organizes projects by contributing teams (e.g., AI Lab, Frontend, Robotics).

  • Hierarchy: Team → Sub teams within a Lab → Projects

  • Why it’s helpful: Great for internal alignment, performance tracking, and collaboration mapping across the org.

Team View

What it does: Organizes projects by contributing teams (e.g., AI Lab, Frontend, Robotics).

  • Hierarchy: Team → Sub teams within a Lab → Projects

  • Why it’s helpful: Great for internal alignment, performance tracking, and collaboration mapping across the org.

Galaxy

Proposed Features

Proposed Features

Proposed Features

Due to the limitation of time, we didn't get to implement all the features. Below are the proposed features that are possible with available data from radar.

  1. Inspiration Prompts and Smart Search

  1. Inspiration Prompts and Smart Search

  1. Inspiration Prompts and Smart Search

Inspiration Prompt:

  • Inspire user about what question to ask

  • Presentation of popular questions

  • Build mental model of how the system work

Inspiration Prompt:

  • Inspire user about what question to ask

  • Presentation of popular questions

  • Build mental model of how the system work

Inspiration Prompt:

  • Inspire user about what question to ask

  • Presentation of popular questions

  • Build mental model of how the system work

Intelligent Search & Filters:

Help user ask better questions by:

  • Evaluating if the system has enough parameters from user input

    • Ask follow up questions and give suggestions on refinement

Intelligent Search & Filters:

Help user ask better questions by:

  • Evaluating if the system has enough parameters from user input

    • Ask follow up questions and give suggestions on refinement

Intelligent Search & Filters:

Help user ask better questions by:

  • Evaluating if the system has enough parameters from user input

    • Ask follow up questions and give suggestions on refinement

  1. Interactive Timeline

  1. Interactive Timeline

  1. Interactive Timeline

The interactive timeline serves as both a filter and a line graph that indicates change in activity over time.

The interactive timeline serves as both a filter and a line graph that indicates change in activity over time.

The interactive timeline serves as both a filter and a line graph that indicates change in activity over time.

  1. Cross-Cluster Links

  1. Cross-Cluster Links

  1. Cross-Cluster Links

See projects with related domains, tech, team easily

See projects with related domains, tech, team easily

See projects with related domains, tech, team easily

Galaxy

Future Vision

Future Vision

Future Vision

As we mapped abstract data into a spatial galaxy metaphor, we found many promising visual parameters that intuitively encode complex project characteristics. However, we also encountered limitations in data availability and granularity that prevent full implementation today.

To guide future development, I documented a roadmap of potential visual mappings that could unlock richer storytelling and decision-making once the underlying data becomes accessible:

As we mapped abstract data into a spatial galaxy metaphor, we found many promising visual parameters that intuitively encode complex project characteristics. However, we also encountered limitations in data availability and granularity that prevent full implementation today.

To guide future development, I documented a roadmap of potential visual mappings that could unlock richer storytelling and decision-making once the underlying data becomes accessible:

As we mapped abstract data into a spatial galaxy metaphor, we found many promising visual parameters that intuitively encode complex project characteristics. However, we also encountered limitations in data availability and granularity that prevent full implementation today.

To guide future development, I documented a roadmap of potential visual mappings that could unlock richer storytelling and decision-making once the underlying data becomes accessible:

visual parameter

Mapped to

What’s needed to implement

Planet size

effort invested (time or money)

Actual effort metrics based on e.g. time and money

Star Brightness

Impact or visibility

quantitive impact score, or stakeholder priority

Rotation speed (within the trail or self-rotation)

Update frequency within stages, major updates

More granular way of documenting updates in projects

Split off trails

if multiple spin-off projects emerged from an original one

More relationship data to indicate relationships between projects

Satellite (Level 3 Category)

work (tech evaluation, partner, spin off) that supported that specific projects

Evaluation of DataOS

Planet shape

Entity type

Project, tech evaluation, partner

Domain star cluster

Product sub concepts

product line-features

Personal Dashboard

An evolving, modular dashboard that personalizes to your role and intent.

An evolving, modular dashboard that personalizes to your role and intent.

An evolving, modular dashboard that personalizes to your role and intent.

Our approach enables adaptive interfaces through conversational feedback. Users can directly express what they want to track or explore.

By entering their goal or question into a prompt input box, the system performs intent tagging to categorize the query and automatically surfaces the most relevant UI components.

Step 1: Intent Tagging: Understanding the ask

On interpreting the user input side, I defined 5 parameters to tag a questions.

On interpreting the user input side, I defined 5 parameters to tag a questions.

On interpreting the user input side, I defined 5 parameters to tag a questions.

For example ☝️

How did I come up with these parameters? Why?

How did I come up with these parameters? Why?

How did I come up with these parameters? Why?

During the user interview, I asked each interviewee: " If you can ask the galaxy page any question, what would you ask?" and looked for recurring patterns in what they were fundamentally asking for.

While an LLM could infer UI responses directly, defining a parameter tagging framework adds a structured, interpretable layer between user intent and system response. Putting LLM on rails as oppose to letting it range free.

During the user interview, I asked each interviewee: " If you can ask the galaxy page any question, what would you ask?" and looked for recurring patterns in what they were fundamentally asking for.

While an LLM could infer UI responses directly, defining a parameter tagging framework adds a structured, interpretable layer between user intent and system response. Putting LLM on rails as oppose to letting it range free.

During the user interview, I asked each interviewee: " If you can ask the galaxy page any question, what would you ask?" and looked for recurring patterns in what they were fundamentally asking for.

While an LLM could infer UI responses directly, defining a parameter tagging framework adds a structured, interpretable layer between user intent and system response. Putting LLM on rails as oppose to letting it range free.

Step 2: UI Matching: How to Answer with Different UI Components

On the system output side, for each of the potential intent, I have a set of corresponding UI components to answer.

On the system output side, for each of the potential intent, I have a set of corresponding UI components to answer.

On the system output side, for each of the potential intent, I have a set of corresponding UI components to answer.

A white and gray background with a gradient of black dots

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A white and gray background with a gradient of black dots

01

A white and gray background with a gradient of black dots

01

A white and gray background with a gradient of black dots

02

A white and gray background with a gradient of black dots

02

A white and gray background with a gradient of black dots

02

A white and gray background with a gradient of black dots

03

A white and gray background with a gradient of black dots

03

A white and gray background with a gradient of black dots

03

A white and gray background with a gradient of black dots

04

A white and gray background with a gradient of black dots

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A white and gray background with a gradient of black dots

04

For example, for "Discover" intent, an UI component like this be fetched☝️

Step 3: Curate Dashboard Space with Feedback

If the result is satisfactory, user can pin or collect the UI component for future to gradually customize their dashboard to be most useful.

If the result is satisfactory, user can pin or collect the UI component for future to gradually customize their dashboard to be most useful.

If the result is satisfactory, user can pin or collect the UI component for future to gradually customize their dashboard to be most useful.

Evaluation of potential third-party integration

Evaluation of potential third-party integration

Evaluation of potential third-party integration

During the process of brainstorming how to leverage the power of LLM to empower better search, I talked to a start up company called glean, which provides a software that combines all source of information across platforms and domain and fuse them into smart search that's specific to the company.

Reflection

Designing beyond the interface

  • Curate for clarity: I turned my workspace into a “source of truth” so anyone—PM, engineer, VP—could instantly understand the design story.

  • Zoom out of the designer box: I thought not just about the UI, but about how Radar fits into the company’s innovation strategy.

  • Design with evolution in mind: I didn’t stop at v1. I set up feedback loops and data pathways for future iterations.

  • Vision + execution: I learned how to dream big and build smart—anchoring a North Star in a concrete MVP.