UX/UI Design · Design Systems · User Research · B2B SaaS

Murochat: A ChatGPT Alternative for enterprises that's secure

92%

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

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THE 'WHY'

Employees should get their best work done with Generative AI, without affecting company's security & data privacy.

The use of ChatGPT & Generative AI by employees is indispensable & it's now an assistant in all their tasks, be it programming, writing, or research.

An enterprise should be able to get the best productivity out of employees, while keeping their confidential data and user information protected from LLMs.

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Background & Context

Employees can't function without Generative AI & LLMs

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.

There are security concerns with ChatGPT

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Companies have sensitive & confidential data that just can't be compromised

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

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The Problem Statement

Research & design a secure, enterprise-grade LLM chat interface for employees

A secure, enterprise-grade LLM chat interface that employees of an enterprise can use as a ChatGPT alternative. The enterprise can plug and play any LLM model they want, like GPT-4, Mistral, or LLama.


The platform will safeguard sensitive user data and confidential company information through a "message firewall", ensuring that data is automatically redacted before being shared with the LLM model.

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About the Company & Product

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Plug-and-play any LLM model

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.

A Message Firewall

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.

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How I am solving this

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Who are we solving for?

The Fund Manager

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 Research Analyst

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.

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How do they go about their investments?

User Research Questionnaire

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Reach-outs

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

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.

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Major pain-points faced by the interviewees

Pain-point 1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Pain-point 1

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.

Pain-point 1

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.

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Understanding the market

Existing Tools

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Market Trends

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.

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Analysing the competitors

Identification

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

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.

Analysis

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.

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Major user motivations to solve for

User Motivation #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

User Motivation #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.

User Motivation #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.

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Brainstorming for possible solutions

Initial Whiteboarding

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Entity Relationship Diagram

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.

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Making sense out of these entities

Information Architecture

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Sitemap

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.

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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.

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Iterating my way out

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.

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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.

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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.

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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.

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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.

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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.

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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.

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Impact

Impact Metrics

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Testimonials

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.

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Project Testimonials

Prashant Mahajan

Project Lead at Murochat Co-founder & CTO at Privado

Collaborated with Mahaveer during his time at Privado and on Murochat. He showed great ownership and dedication, completing projects under strict timelines. He is a sincere & hardworking individual who always brings fresh ideas to the table. He goes beyond his assigned tasks to ensure the success of the project. I would highly recommend him for any team or project in need of someone who is dedicated & creative.

Get in Touch

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