From a 200-row spreadsheet to a SaaS platform driving $10M ARR
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Here’s how I redesigned Intropic’s product, cutting workflows from a day to 5 minutes , expanding into 5 global markets and driving $10M ARR.
Background
Intropic is a fast-paced fintech start-up that forecast changes in stock market by Index Rebalance. In simple terms, it provides tools to predict how stock markets will move, so investment firms can stay ahead of trends. The platform is a B2B solution that empowers index specialists with insights and forecasts to support better decisions.”
The Problem
Users were spending over a day finding data. The workflows were slow, error-prone, and hard to scale.
Analysts & Research were hard to find data in the "product".
My Role
As Staff Product Designer, I led the end-to-end design that transforming a spreadsheet based product into a scalable platform that enabled index analysts to move 10× faster, with greater confidence and consistency.
Led product strategy, UX, and design execution
Partnered with CPO, PMs, and Engineers
Mentored 1 junior designer on core feature delivery
Contributed to early-stage design system patterns for charts, data cards, and filters
Impact
200%+
ARR
100+
B2B Users
32%
DAU/MAU ratio (B2B)
The Goal
Design and create a fast, scalable, and intuitive tool to help analysts explore, compare, and act on complex market data.
Discover & Insights
To create a better data product, I need to understand the complex data by studying the index methodology and identify the most valuable insights for our users through thorough user research.
I led user interviews and found out the followings:
Users are interested at the forecasted data first.
Then they want to know What drive behind it and Why.
They also want to spot the change quickly.
What users do on the product
Suggestion from users
User interview sessions
Conclusion:
The product was a web-based spreadsheet with over 200 columns and 1,000 rows, making it time-consuming for users to find the information they are interested in.
Define & Align
Aligning the design direction with stakeholders is the key to achieving a successful design.
We quickly defined the product’s north star:
A data platform where users can easily discover trading opportunities, search specific tickers in users mind, and quickly understand the rationale behind forecasts.
Product's North Star from the workshop
I led the workshop to map out the North stars
the new information architecture and the structure
Design Strategy & System Thinking
I structured the platform around scalable, reusable interaction patterns which match with our users flow and summarised into three main categories:
The Three Pillars
1. Searchability
• Need: Users want to quickly find what they are looking for.
2. Discoverability
• Need: Users want to discover new and interesting trades or opportunities that may not have thought of themselves.
3. Knowledgeability
• Need: Users want to quickly understand what is driving a forecast.
These three categories can guide the design and functionality to better meet user expectations and improve overall satisfaction.
Searchability
Type, find, done
Need: Users want to quickly find what they are looking for.
Goal: Enhance the quick-find experience with an universal search bar, allow users to search across the universe within the product.
Solution: Universal Search bar with forecast previews and Deep-Dive Capability
The Search Experience
The Craft
Result:
Search became the first action on landing; adoption from 14% → 33%, outperforming B2B SaaS benchmarks.
Discoverability
The market in a glance
Need: Users want to see all related information easily.
Goal: Centralize scattered data, make scanning fast, surface opportunities.
Solution:
Data Table design system → cell alignment, row/column rules, layered hover states.
Ticker Card → compact snapshot of market cap, cutoffs, proforma values.
The Data Table Experience
The Craft
The Ticker Card
compact snapshot of the data
Result:
Discover became a core workflow, with users spending over 20% of their session time.
Knowledgeability
Why forecasts change
Need: Users want to quickly understand what is driving a forecast.
Goal: Provide concise, easily digestible explanations of forecast drivers with clear summaries & visualizations
Solution: Providing a standardized reasoning label that summarizes the core reasons for the forecast with a easy-to-understand data visualizations across the product with the AI explaination.
The Reasoning Label
Result:
Reduced time spent interpreting data tables — analysts now understand forecast reasoning in seconds.
Cut support tickets related to “why forecasts changed” by 68%, boosting user confidence.
Designing the Ticker Page
To address users biggest pain points, I led the design of the Ticker Page — a centralized view of a company
This page became the “single source of truth,” combining multiple components into one seamless workflow.
1. Searchability
Deep linking from Search Bar brought users directly into the ticker page.
Universal search rows connected straight to this hub, eliminating the need to jump between spreadsheets and files.
2. Discoverability
Integrated Ticker Cards summarize market cap thresholds at a glance.
Data Tables (with standardized cell, row, and column rules) made scanning and comparing trades intuitive.
Related indices, similar trades, and historical rebalances surfaced as quick insights.
3. Knowledgeability
Embedded Reasoning Tags explained why forecasts changed inline.
AI-generated plain language explanations gave immediate clarity without needing support.
Result:
Users gained a 360° view of each company without context-switching.
The Ticker Page became one of the most visited areas of the platform, accounting for 35% of total session time.
UI Showcase
Responsive design of the ticker view
Timeline design with dummy forecast data
The time series distribution design chart
Users feedbacks
“Finds the interface nice and likes GMSR. We use it mainly for checking NOC changes and running their own model. Currently, they use it for spot checks and monitoring, but we can see it becoming more integrated into their workflows in the future. ”
One of the hedge fund manager
“Amazing products you are delivering. Very happy.”
Quant Researcher
“Index intelligence is useful as it has been easy to look at all the params in one place - distances to cutoff, NOC, free float. Good GUI to get a lot of information very quickly. ”
Portfolio manager from pension fund
"That's straight from the methodology, it's spectacular to see it like that. It's obviously a great product."
portfolio manager
Challenge
As I mentioned, Intropic is a fast-paced startup where we need to build and ship quickly.
As the design lead of the product, beyond solving user problems, my biggest question is:
How do we balance design and business needs while maintaining speed and efficiency?
We prioritize design based on key user needs, break down large design into smaller MVPs for quick user testing, and consistently work closely with stakeholders across product, engineering, and business teams to ensure alignment.
For example, we categorize important data sets into different groups to help users quickly spot interesting opportunities with minimal effort from the engineering team. This approach allows us to buy time for future design iterations.
Highlighting the newly investable, promotion demotion and shadow of the market, which is the most interesting data.
Learning
Gain a deep understanding of the data—know what it represents and why users engage with it—before initiating the design process.
Break large design concepts into smaller MVPs to quickly trial, test, and iterate based on feedback.
Collaborate closely with stakeholders to ensure alignment and maintain clear communication throughout the process.
Other interesting project
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Brought a smart coffee experience from concept to MVP and a successful Kickstarter launch, combining physical product design with a mobile companion app.
UX • Concept • MVP • Data visualisation • User research • Mobile App • B2C
Years: 2020-2022
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