WebCase Study 06

AI TRADING / QUANT PLATFORM

QntAIfy

Timeline

Product website & platform concept

Role

Product Designer

Focus

AI Research UXStrategy BuilderTrading DashboardWebsite Design

Outcomes

AI-powered researchNo-code strategy builderPortfolio intelligence

QntAIfy is an AI-powered trading platform designed to help users research markets, build strategies, test ideas, execute trades, and track portfolio performance through one connected experience.

Synopsis

The problem worth solving

Trading platforms often feel complex, data-heavy, and intimidating for users who want faster insights and easier decision-making. The challenge was to design a product experience that makes advanced trading features like AI research, strategy building, execution, and portfolio analytics feel clear, structured, and accessible.

Design Process

Discover

Studied trading workflows, market research behaviour, strategy-building needs, and dashboard patterns.

Define

Identified the key product pillars: research, strategy creation, execution, and portfolio intelligence.

Ideate

Explored layouts for AI insights, strategy cards, chart modules, broker actions, and performance dashboards.

Design

Created a clean, modern interface with large typography, strong visual hierarchy, animated data cards, and product-focused sections.

Review

Checked the experience for clarity, trust, feature understanding, and decision-making flow.

Ship

Prepared website sections, product visuals, responsive layouts, and developer-ready UI direction.

Research Insights

Users need insights faster, but raw market data can feel overwhelming.

Strategy building should feel guided, not limited to only technical users.

Execution features need strong clarity because users are dealing with financial decisions.

Portfolio analytics should help users understand performance, risk, and allocation at a glance.

Opportunity Map

Research

Help users convert market data into quick, usable insights.

Build

Allow users to create strategies through both code and no-code workflows.

Execute

Support paper trading and broker-linked execution with clear action states.

Track

Give users a smarter view of portfolio performance, allocation, and risk.

User Persona

Arjun Mehta

32 · Active Trader / Market Enthusiast

Goals

Research stocks faster
Test trading strategies
Reduce manual analysis
Track portfolio performance clearly

Frustrations

Too many tools for one workflow
Complex strategy-building platforms
Raw charts without context
No clear link between research, strategy, and execution

Their words

“I don’t want another complicated trading tool. I want one place to research, test, and act with more clarity.”

User Stories

#01

As a trader, I want AI-powered research so I can understand market opportunities faster.

#02

As a strategy builder, I want code and no-code options so I can create strategies based on my comfort level.

#03

As a user, I want to paper trade before live execution so I can test ideas safely.

#04

As an investor, I want portfolio insights so I can understand performance, allocation, and risk better.

User Journey Map

Discover

neutral

Actions

User lands on the website

Understands what QntAIfy offers

Pain points

Needs a fast grasp of product value before exploring deeper

Explore

neutral

Actions

Reviews the four core platform features

Scans research, strategy builder, execution, and portfolio intelligence

Pain points

Feature complexity can still feel heavy without strong structure

Evaluate

neutral

Actions

Checks product visuals

Reviews use cases, platform benefits, and broker integration possibilities

Pain points

Needs trust that the product is advanced but still understandable

Try

positive

Actions

Moves toward sign-up

Requests demo or early access

Pain points

Needs enough confidence to take the first product action

Use

positive

Actions

Researches markets

Builds a strategy

Tests it

Executes

Monitors portfolio performance

Pain points

Research, execution, and tracking must stay connected without friction

How Might We

How might we make advanced trading intelligence feel easier to understand?

How might we connect research, strategy building, execution, and portfolio tracking into one flow?

How might we show AI and automation without making the product feel risky or overpromising?

Problem Statement

Traders often depend on multiple disconnected tools for research, strategy testing, execution, and portfolio tracking. QntAIfy needed a clearer product experience that explains its AI-powered workflow while making complex trading features feel organized and approachable.

Hypothesis

If the platform is structured around four clear value pillars, users will understand the product faster and feel more confident exploring advanced trading features.

Value Propositions

AI-Powered Research

Instant insights, market intelligence, and deep analytics to support faster decision-making.

Strategy Builder

Code and no-code tools to design, test, and automate trading strategies.

One-Click Execution

Paper trade or execute strategies through broker integrations with clear action controls.

Portfolio Intelligence

Smarter portfolio tracking with optimization, rebalancing, and performance analytics.

Competitive Scan

TradingView

Powerful charts and community, but strategy workflows can feel technical.

Zerodha / Kite

Strong execution experience, but limited AI-led research and strategy automation.

Smallcase

Good portfolio-led investing experience, but not built for custom strategy creation.

User Flows

Website Flow

Home

Platform Features

Product Preview

Use Cases

About

Contact / Join Waitlist

Product Flow

Research

Build Strategy

Backtest / Paper Trade

Execute

Track Portfolio

Behind the work

Design decisions

Challenge

Trading platforms often feel complex, data-heavy, and intimidating for users who want faster insights and easier decision-making.

Approach

I simplified the experience around four clear product pillars and used visual cards, charts, dashboard previews, and simple messaging to explain the platform.

Result

The final direction made QntAIfy feel more modern, intelligent, and easier to understand without overwhelming users with technical depth.

Deliverables

01Landing Page Design

02Product Feature Sections

03AI Research UI Concepts

04Strategy Builder Concepts

05Dashboard Visuals

06Portfolio Intelligence Sections

07Responsive Web Layouts

08Developer-ready UI Direction

Focus areas

AI Research UXTrading DashboardStrategy BuilderProduct StorytellingData VisualizationResponsive WebsiteTrust & Clarity

Project type

AI TRADING / QUANT PLATFORM

Timeline

Product website & platform concept

Deliverables

8 items

Platform

Web

Final Output

Final UI Screens

The final interface brings together AI research, strategy building, paper trading, execution, and portfolio intelligence into one connected trading product experience.