UX/UI Design · Design Systems · User Research · B2B SaaS
Pinegap: Transforming equity research for funds with AI insights
90
Reduction in avg. research & analysis time
2x
New companies discovered per month
Team
• Product Design Consultant (Me)
• Co-founder (CEO)
• Co-founder (COO)
• Frontend Engineer
• Head of AI & Research
What I did
• Brainstorming & Planning
• User Research
• Competitive Analysis
• UX Direction
• Design Systems
• End-to-end UI Design
Project Timeline
Feb 2024 - April 2024
//
THE 'WHY'
Investment decisions by mutual funds should be extremely well-informed and research-backed.
Billions of dollars are invested by retail investors every year in active mutual funds and hedge funds.
Equity research analysts and managers of these funds should be empowered with the right technology, automation, and trust to assist them take research-backed investment decisions.
//
Background & Context
Funds employ managers & analysts to invest billions of dollars of public money
Active mutual funds are managed by professional fund managers who actively select stocks, bonds, or other assets to outperform the market. Retail investors or public buy units of the fund to invest long term and generate returns. These funds charge a fees due to the intensive research & active trading strategies involved.
Analysts in the fund analyze economic data, company fundamentals, and market trends to document their research. Based on that, managers make informed buy, sell, or hold decisions, aiming to generate higher returns for investors.
Fund managers & analysts use these analyses in their research workflow
Fundamental Analysis
Focuses on a company's financial health, evaluating metrics like earnings, debt, and market position to determine its intrinsic value.
Technical Analysis
Analyzes statistical trends from trading activity, such as price movements and volumes, to predict future market behavior.
Qualitative Analysis
Examines non-quantifiable data such as company's statements & stance, management team, industry cycles, peers, & brand strength.
//
The Problem Statement
Research & design a SaaS platform for funds to do qualitative analysis of companies
A comprehensive platform for fund managers and equity research analysts to perform qualitative analysis of companies they are invested in or discovering. The solution should aggregate important company activities & insights, management updates, and peer comparison.
This would supplement the existing fundamental and technical analysis workflows, enabling more informed & research-backed investment decisions that responsibly manage retail investors' money.
//
About the Company & Product
Pinegap is a seed-funded company building equity research tools & functionalities that are powered by generative AI.
A comprehensive qualitative research platform for fund managers & analysts
Pinegap's equity research platform would analyse non-quantifiable metrics across various company events and summarise them, that would help equity analysts to always stay in the loop of every new development.
Insights on company activities that aid in investment decision making process
Pinegap's platform would primarily suggest users with qualitative insights like contradictory statements and focus on keywords, and the ability to summarise or go deeper into an event in the context of a particular subject.
Leveraging the power of generative AI to analyse documents & generate insights
At the core of Pinegap is its custom LLM that is trained on thousands of earning call transcripts and shareholder presentations. It specialises in sentiment analysis in the context of equity research, thus powering summaries and insights to be shown in the UI. It can also correctly produce the citations of each insight in the form of a document location, which would be important for trust.
//
How I am solving this
//
Who are we solving for?
The fund manager takes crucial investment decisions based on research
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
The equity research analyst is responsible for researching a handful of companies
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
How do they go about their investments?
Preparing a questionnaire for interviews
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Reaching out to design partners
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Research Insights
//
Major pain-points faced by the interviewees
There's too many things that can be done, but never enough time
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Trust is paramount when it comes to stock market research
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
The bandwidth for another tool simply doesn't exist
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Understanding the market
Existing platforms that make up the fund's toolstack
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Market trends that influence qualitative research
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Analysing the competitors
Identifying competitors & peers in the industry
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Analysing their features, flows & design decisions
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Major user motivations to solve for
Discovering new stocks to invest in through recommendations
Finding contradictory insights based on recent activities
Comparing a company's stance on a topic across quarters
Comparing a company's stance on a topic with its peers
Summarizing a particular document based on a topic
//
Brainstorming possible solutions
Whiteboarding to prioritize things
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Listing down all the entities at play
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
User Flows
Flow #1
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Flow #2
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
Flow #3
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Making sense of this information
Organizing all the information gathered till now
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Formulating a structure for the application
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Iterating my way out
Design element 1
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Design element 2
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Some crucial design decisions
Selected Idea #1
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Discarded Idea #2
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Design Systems
Design Foundations
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Components & Variants
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Feature 2
Functionality #1
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Functionality #2
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Feature 2
Functionality #1
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Functionality #2
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Feature 2
Functionality #1
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Functionality #2
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Conclusion & Learnings
Key Takeaways & Learnings
Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time
Next Steps for the Project
Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.
//
Impact Created
90
Reduction in avg. research & analysis time
2x
New companies discovered per month
//
Project Testimonials
Deepak Sharma
Co-founder & CEO at Pinegap
Mahaveer quickly grasped our vision and thoughts related to creating a SaaS platform for highly professional Wall Street analysts and converted them into a user-friendly experience. He is very professional and efficient. I was also very impressed with his thorough market research, which is evident in the details of his work.
Ankit Varmani
Co-founder at Pinegap
Working with Mahaveer was a game-changer for our equity research platform. His thorough research of competitive tools and independent thinking led to a unique and intuitive UI/UX design that sets us apart.