UX & Product Designer

Esha
More

Open to Work Rutgers MBS '26 Brooklyn, NY

Experience designer turning complex systems — AI, data, and human behaviour — into interfaces people actually want to use.

Worked on AI-driven products across healthcare, EdTech, and WorkTech, leading research, ideating in Figma, and presenting to stakeholders.

Esha More
Selected Work

Projects

Click a folder to explore projects by domain.

HealthTech
2 projects
WorkTech & Productivity
2 projects
EdTech
1 project
FashionTech
1 project
HealthTech
Altasparq
AI Dashboard · MorphCast AI

Altasparq Aware

Emotion-aware clinical dashboard using real-time AI for patient triage.

View Case Study →
Praan AI
Wellness AI · iOS / iPad / Web

Praan AI

AI yoga and breathwork companion across three platforms.

View Case Study →
WorkTech & Productivity
Sangam
Enterprise Mobile · Tata Power

Sangam Lite

Employee management mobile app for 10,000+ Tata Power employees across India.

View Case Study →
Slingshot
UX Research Project · Infragistics

Slingshot

Multi-method UX research for a data-driven project management platform.

View Case Study →
EdTech
Knight Plan
University Systems · Rutgers

Knight Plan

Redesigning Rutgers' course registration for 50,000+ students.

View Case Study →
FashionTech
The Gift Edit
AI Gifting · Hackathon · 1st Place

The Gift Edit

AI shopping assistant turning vague gifting intent into curated, personalised recommendations.

View Case Study →
FigmaFigJamMiroMazeAdobe CCClaude AIStitchFramerWebflowHTML/CSSJavaScriptLovableCursorFigmaFigJamMiroMazeAdobe CCClaude AIStitchFramerWebflowHTML/CSSJavaScriptLovableCursor
How I Work

Design Process

01

Research

  • User Interviews
  • Surveys & Screeners
  • Competitive Analysis
  • Journey Mapping
  • Usability Testing
  • Heuristic Evaluation
02

Ideate

  • Workshops & Co-design
  • Wireframes
  • Information Architecture
  • User Flows
  • Prototyping
  • Design Critiques
03

UI & Delivery

  • Design Systems
  • Accessibility (WCAG)
  • High-Fidelity Design
  • Developer Handoff
  • Analytics & Iteration
  • Stakeholder Presentation
Creative Work

Play

Click a folder to explore creative work.

3D Modelling
4 renders
3D Animation
1 video
Social Media
7 pieces
Vibe Coding
Coming soon
3D Modelling

Product models designed in Blender — form studies and detailed product renders.

Bosch Hand DrillBosch Hand Drill
Hair StraightenerHair Straightener
Electric GuitarElectric Guitar
LG Garment StylerLG Garment Styler
3D Animation

Tamon dispenser — modelled and animated in Blender.

Social Media Design

Brand identity, event campaigns, and social media visuals.

Social
Social
Social
Social
Social
Social
Social
Vibe Coding

Creative web experiments — interaction design, motion, and generative UI. Coming soon.

About Me

Hi, I'm Esha — an experience designer translating complex systems into clear, intuitive interfaces.

I work at the intersection of UX research, product strategy, and AI — designing for domains where decisions have real stakes: healthcare, education, and enterprise tools.

My approach is research-led and outcome-driven. I run user interviews, build journey maps, test prototypes, and translate findings into design decisions that hold up under scrutiny.

Currently pursuing a Master of Business and Science in UX Design at Rutgers University, building the strategic and systems-thinking vocabulary to work at the business-design intersection.

Career Timeline

2024–Now

Rutgers University

MBS in UX Design · MBS Fellowship Recipient

Feb–Apr 2026

National Science Foundation (NSF)

UX Researcher · I-Corps Northeast Hub · Praan AI

Sep–Dec 2025

MorphCast AI

UX Design Intern · Altasparq Aware

Sep–Dec 2024

Infragistics

Lead UX Design Extern · Slingshot · Honorable Mention Award

Dec 2023–Aug 2024

Enso Group

UX Designer · InfoProfile

Jan–Apr 2023

Tata Power

UX Design Intern · Sangam Lite Employee App

What People Say

Esha consistently brought thoughtful, user-centred perspectives to every design challenge. Her ability to translate complex requirements into intuitive experiences and present confidently to senior stakeholders set her apart.
Shekar Mudaliyar
Design Lead, Tata Power Company Limited
Working with Esha on the Knight Plan project was a masterclass in research-led design. She ran every interview, synthesised every insight, and built a system from scratch that actually fixed the problem — not just on paper, but in testing.
Pratigya
Collaborator, Rutgers MBS Program
Esha has a rare ability to hold complexity without losing sight of simplicity. Her design intuition, combined with how rigorously she defends decisions with research, makes her one of the most complete designers I've worked with.
Vaibhav Maloo
Collaborator, Rutgers MBS Program
Get in Touch

Let's Build Something Meaningful

Open to full-time UX Designer and Product Designer roles. Research rigour, systems thinking, bias toward clarity.

HealthTech · AI Dashboard · MorphCast AI · Sep–Dec 2025

Altasparq Aware

An emotion-aware clinical dashboard that helps therapists and clinicians track patient emotional states using real-time AI — surfacing insights that reduce cognitive load and improve therapeutic outcomes.

UX Design Intern
MorphCast AI
Sep–Dec 2025
HealthTech / AI

UI Screens

altasparq-aware.morphcast.com
Altasparq
1 / 8
Overview — Home Dashboard
Overview — Home Dashboard Overview — Home Dashboard
Live Session — Real-time Analysis Live Session — Real-time Analysis
Analytics Overview Analytics Overview
Patient Detail — Emotion Mapping Patient Detail — Emotion Mapping
Patient Directory Patient Directory
Clinical Schedule Clinical Schedule
Patient History Patient History
Session History Log Session History Log

Design System

Colour Palette

#FFFFFF
Background
#F8F9FA
Surface
#2563EB
Primary Blue
#1D4ED8
Deep Blue
#10B981
Stable / Safe
#EF4444
Critical Alert
#F59E0B
Fluctuating
#1E293B
Text

Typography

DisplayWelcome back, Aris
H2Emotional Trends Across Base
BodySession shows 12% increase in neutral-to-positive transition
LabelAVG. AROUSAL · TOTAL SESSIONS · VALENCE

Components

● STABLE ● FLUCTUATING ● CRITICAL

Before & After

The original MorphCast dashboard surfaced all emotion data at equal visual weight — overwhelming for clinical staff. The redesign introduces clear hierarchy, role-based views, and clinical-grade status colour semantics.

Before
After

Key Design Decisions

Decision 01 — Information Hierarchy

Demoted emotion indicators to a secondary layer — visible on threshold breach. Vitals and session status remain primary; AI emotion data is context, not command.

Decision 02 — Role-Based Views

Three role-based configurations (therapist overview, patient detail, admin) with single-click toggle. Research showed each role needed fundamentally different data density.

Decision 03 — Trust Signalling

Added explainability via facial action unit labels (e.g. "Corrugator Supercilii: LOW") alongside emotion scores. Showing the reasoning reduced dismissal in testing.

Outcomes

24%
Improvement in emotion recognition accuracy
3→4/5
Usability rating improved across all participants
WCAG AA
Full accessibility compliance achieved
Want to go deeper into the process?
Wellness · AI · iOS / iPad / Web · Feb–Apr 2026

Praan AI

An AI-powered yoga and breathwork companion designed to help practitioners deepen their practice through personalised guidance — without a physical instructor present.

UX Researcher
NSF I-Corps
Feb–Apr 2026
iOS · iPad · Web

UI Screens

Praan iPhone
1 / 13
Landing — Breath. Body. Balance.
Live Session Live Session
Home Home
Browse Browse
Intent Intent
Breath Breath
Circle Circle
Session End Session End
Profile Profile
Settings Settings
AI Feedback AI Feedback
Breath Ring Breath Ring
Summary Summary
Finish Finish
Praan iPad
1 / 10
Home Dashboard
Home Dashboard Home Dashboard
Landing — Breath. Body. Balance. Landing — Breath. Body. Balance.
Camera Setup — Let me see you Camera Setup — Let me see you
Intent Profile — What brings you to the mat? Intent Profile — What brings you to the mat?
Live Session — AI Pose Tracking Live Session — AI Pose Tracking
Circle — Community & Challenges Circle — Community & Challenges
Progress — Consistency Flow Progress — Consistency Flow
Profile — Practice Flow Profile — Practice Flow
Settings — Camera & Preferences Settings — Camera & Preferences
Library — Categories & Trending Library — Categories & Trending
praan.ai
Praan Mac
1 / 10
Home
Home Home
Library Library
Circle Circle
Progress Progress
Live Session Live Session
Studio Dashboard Studio Dashboard
Analytics Analytics
Instructor View Instructor View
Community Community
Settings Settings

Design System

Colour Palette

#0D1117
Background
#161B22
Surface
#C8A97E
Warm Gold
#8B7355
Deep Gold
#E8DDD0
Cream Text
#4A7C6F
Teal Accent
#6B4F9E
Violet Accent

Typography

Displaygood morning, Ananya
H2Practice Insights
BodyInhale · 4 counts · Hold · 4 · Exhale · 8
LabelBREATH RATE · FOCUS · STREAK

Components

● Live Breathwork Restore

The Challenge

The client proposed a smart mirror — expensive hardware, narrow market. The real problem: practitioners needed real-time form feedback and structured breathwork, but not a new device. Validated through 21 user interviews across the NSF I-Corps programme.

Key Design Decisions

Decision 01 — Eliminate the Hardware

Pivoted from $800 smart mirror to camera-based AI on existing iPad. 7 of 8 participants said yes to the app; 6 of 8 said no to the hardware.

Decision 02 — Dual Revenue Model

Designed B2B studio-licence flow alongside consumer subscription. Studios provide recurring revenue; subscriptions provide scale.

Decision 03 — Breathwork as a Gateway

Camera-optional breathwork module led onboarding before pose correction. Trust is earned in stages.

Outcomes

21
User interviews for problem-solution fit
4.75/5
Prototype concept screening score
90%+
Task completion in concept testing
Want to go deeper into the process?
EdTech · University Systems · Rutgers University · Sep–Dec 2024

Knight Plan

Redesigning Rutgers' course registration experience for 50,000+ students — with an AI advisor, prerequisite clarity, and full WCAG AA accessibility.

Lead UX Researcher
Rutgers University
Sep–Dec 2024
50,000+

UI Screens

knight-plan.rutgers.edu
Knight Plan
1 / 11
Welcome
Welcome Welcome
AI Advisor — Start AI Advisor — Start
AI Advisor — Registration AI Advisor — Registration
Course Search Course Search
Course Search Grid Course Search Grid
Course Planner Course Planner
Quick Search Quick Search
Registration Confirmed Registration Confirmed
Student Profile Student Profile
Settings Settings
Help & Support Help & Support
Live Build
Explore Knight Plan →
Built with Lovable · Rutgers Course Registration Redesign
Open Live App ↗

Design System

Colour Palette

#CC0033
Rutgers Red
#F5F5F5
Background
#1A1A1A
Text
#2563EB
AI Accent
#16A34A
Success
#D97706
Warning
#E2E8F0
Border

Typography

H1Course Registration
H2AI Academic Advisor
BodyPrerequisite met · 3 credits · Tue & Thu 10:20am
LabelFALL 2024 · SECTION 01

Components

✓ Prerequisite Met ⚠ In Progress ✕ Not Met

Key Design Decisions

Decision 01 — AI Advisor Integration

AI advisor pulling from degree audit data for personalised course recommendations. Handles routine queries, freeing advisors for complex cases.

Decision 02 — Prerequisite Clarity

Course cards surface prerequisite status — met, unmet, in progress — before add. Error prevention beats error recovery.

Decision 03 — WCAG AA Compliance

Rebuilt design system from scratch with accessibility as constraint. System failed 14 of 26 WCAG 2.1 AA checkpoints before; passes all 26 after.

Outcomes

90%
Task completion in moderated usability testing
84/100
SUS score (up from estimated 54)
WCAG AA
Full accessibility compliance
Want to go deeper into the process?
UX Research Project · WorkTech · Infragistics · Sep–Dec 2024

Slingshot

A comprehensive UX research project for Slingshot — Infragistics' data-driven project management platform. Led a team of 4 researchers to evaluate 50+ features and deliver actionable design recommendations that fed directly into the product roadmap.

Lead UX Design Extern
Infragistics
Sep–Dec 2024
🏆 Honorable Mention
Slingshot
About Slingshot

Slingshot is a digital workplace platform by Infragistics that differentiates through embedded data analytics — combining project management, team dashboards, and AI-powered insights in a single tool.

Research Overview

50+
Features evaluated across Slingshot + 3 competitors
12+
Cross-platform use cases from competitive benchmarking
20%
Projected increase in task management efficiency

Key Design Decisions

Decision 01 — Heuristic-First Evaluation

Applied Nielsen's 10 heuristics across 50+ features on web and mobile, producing a prioritised gap analysis that directly guided Infragistics' product roadmap.

Decision 02 — Analytics Discoverability

Analytics features discovered by accident 70% of the time. Recommended contextual prompts surfacing insights at the moment of data entry — context beats discoverability.

Decision 03 — Stakeholder Communication

Structured deliverable as an opportunity-severity matrix. Framing research in effort vs. impact terms made it immediately actionable for the product team.

Outcomes

50+
Features evaluated, gap analysis delivered
Roadmap
Research fed into Slingshot product prioritisation
🏆 Award
Honorable Mention Team Lead — Rutgers MBS Externship
Want to go deeper into the process?
FashionTech · AI Gifting · Ellis Anti-Slopathon · March 2026

The Gift Edit

An AI shopping assistant built as a feature within Bloomingdale's digital experience — turning vague gifting intent into curated, personalised recommendations. 1st Place at the Ellis Anti-Slopathon Hackathon 2026.

UX Designer
Ellis Hackathon
March 2026
🏆 1st Place
Anti-Slopathon Hackathon 2026 Team at Anti-Slopathon
Live Product
Try The Gift Edit →
Built on Stitch · Inspired by Bloomingdale's
Open Live App ↗

UI Screens

bloomingdales.com / gift-edit
The Gift Edit
1 / 8
Entry Point

Design System — Organic Brutalism

Inspired by Bloomingdale's editorial aesthetic — confident minimalism, extreme white space, zero border-radius. The AI stylist feels like a personal shopper at a flagship store, not an algorithm.

Surface Hierarchy — "Stacked Paper" Model

#F9F9F9
Surface (Base)
#FFFFFF
Cards (Lifted)
#F4F3F3
Container Low
#E2E2E2
Nav / Inactive
#000000
Primary CTA
#5E5E5E
Secondary
#8ADB52
Sustainability

Typography

DisplayThe Perfect Gift
H2 (Serif)Curated for Her · Birthday · Under $200
Body (Inter)Tell me about the person you're gifting…
LabelEXCLUSIVE · LIMITED EDITION

Components — Zero Border-Radius Rule

Birthday Anniversary

The Challenge

Finding the right gift is overwhelming — endless scrolling, generic suggestions, decision fatigue. We chose gifting as the target domain. Instead of giving users more noise, The Gift Edit delivers clarity: curated, meaningful gift options tailored to the person, occasion, and context.

Decision 01 — Built-in Feature, Not a Separate App

Designed as a tab within Bloomingdale's existing navigation. Users already trust the brand's curation — the AI builds on that equity rather than asking them to trust something new.

Decision 02 — Three Layered Questions

Who is this for → What's the occasion → What's the vibe. Each narrows possibility space without feeling like a form. Conversational, not transactional.

Decision 03 — Virtual Try-On Integration

For wearable gifts — lets the recipient visualise the gift on themselves before purchase. Reduces return friction and increases recommendation confidence.

Outcomes

🏆 1st
Place — Ellis Anti-Slopathon Hackathon 2026
8
Screens designed and built end-to-end in one day
Live
Product deployed and accessible at the link above
WorkTech · Enterprise Mobile · Tata Power · Jan–Apr 2023

Sangam Lite

A complete redesign of Tata Power's employee management mobile application — from pilot study and 30 field interviews across 4 user groups, through MoSCoW prioritisation, card sorting, and wireframes, to a high-fidelity prototype that unified 10+ enterprise services into a single, accessible mobile experience.

UX Design Intern
Tata Power
Jan–Apr 2023
Enterprise / WorkTech

UI Screens

Sangam
2 / 15
Home Dashboard
Login Login
Home Dashboard Home Dashboard
AI Task Manager AI Task Manager
Attendance Attendance
Leave Management Leave Management
Approvals Approvals
Employee Services Employee Services
Safety Repository Safety Repository
Social Feed Social Feed
Voice Assistant Voice Assistant
Notifications Notifications
Office Locator — Map Office Locator — Map
Office Locator — Search Office Locator — Search
Office Locator — Details Office Locator — Details
Profile & Settings Profile & Settings
Live Build
Explore Sangam Lite →
Built with Lovable · Tata Power Employee App
Open Live App ↗

Design System

Colour Palette

#FFFFFF
Background
#F2F6FF
Surface
#1560BD
Primary Blue
#3B82F6
Action Blue
#10B981
Success
#F59E0B
Warning
#EF4444
Alert
#1E293B
Text Primary

Typography

DisplayGood morning, Alex
H2Task Manager · Analytics
BodyShift Status: Checked In · Arrival 08:45 AM
LabelHIGH PRIORITY · TASKS · DUE TODAY

Components

● Checked In ● Pending ● Absent

The Challenge

Tata Power had 10+ separate intranet applications for HR, finance, safety, and approvals — all inaccessible on mobile. On-site workers and field engineers couldn't perform basic tasks (marking attendance, requesting leave, filing safety reports) without going back to a desktop. The business goal: consolidate everything into one mobile app that actually gets used.

Key Design Decisions

Decision 01 — Unified Single Login

Research revealed that employees were abandoning the existing app because each feature required a separate login. Designed a single SSO-based entry point that authenticates once and grants access to all services — removing the most-cited reason for non-use.

Decision 02 — Personalised Home Dashboard

Card sorting with users showed that Payslip, Leave, Attendance, and Safety were the four features accessed daily. These became persistent shortcuts on the home screen. Features used once a year (office locator, retirement docs) were moved to a searchable services directory — reducing cognitive load without removing functionality.

Decision 03 — NFC Attendance + AI Task Manager

On-site workers were manually punching attendance after long shifts — a significant pain point. Proposed NFC-based tap-to-clock using existing office infrastructure, reducing commute time for attendance by ~60 minutes. The AI Task Manager was added to address the finding that employees spent the first 30 minutes of every day planning tasks manually in notebooks.

Decision 04 — Accessibility-First Design

The user group included employees aged 20–58, many with limited tech literacy. All tap targets were increased to 44px minimum, text sizes were increased across the board, and a Voice Assistant mode was added for users who found typing difficult — directly addressing the most-requested feature from the 30 interviews.

Outcomes

30
User interviews across 4 user groups and 4 locations
60 min
Reduction in attendance commute time via NFC proposal
10+
Enterprise services unified into one mobile application
Want to go deeper into the process?
Altasparq Aware · Deep Dive

Research & Process

A full walkthrough of how I approached redesigning a complex AI dashboard for clinical use — from heuristic evaluation through to stakeholder presentation, with the design decisions that shaped the final product.

Phase 1 — Heuristic Evaluation

I started by running a structured evaluation of the existing MorphCast platform against Nielsen's 10 Usability Heuristics. Every screen was audited individually: I logged violations, rated severity (1–4), and estimated the frequency of user exposure to each issue.

12 violations identified, with 5 rated severity 3 or 4. The most critical: inconsistent system status (users couldn't tell if the AI was actively analysing), clinical language that didn't match how nurses speak ("valence" instead of "emotional tone"), and no error recovery path when a face scan failed mid-session.

This gave me a prioritised list before speaking to a single user — which meant my interviews could focus on lived experience rather than cataloguing obvious interface problems.

Phase 2 — Contextual Interviews (n=5)

I recruited 5 professionals across three user types: educators using MorphCast in classroom settings, clinicians in therapeutic contexts, and researchers running controlled studies. Sessions were 45–60 minutes, combining a semi-structured interview with observation of their current workflow.

The most important insight didn't come from what users said — it came from watching. Clinicians were running two monitors simultaneously: MorphCast on one screen, patient notes on the other. They'd glance at emotion data, then manually type what they saw into a separate notes field. The tool was creating work instead of reducing it.

After affinity mapping across all sessions, three themes emerged: (1) information overload at the point of decision, (2) lack of trust in AI scores without explanation, and (3) role-specific needs being ignored — a charge nurse and a bedside nurse are doing completely different jobs but seeing the same screen.

Phase 3 — Visual Design Decisions

Live Session UI

The redesign moved from a dark, data-dense aesthetic to a clinical-grade light UI. Key visual decisions:

Colour semantics: I introduced a strict traffic-light severity system — green (#10B981) for stable, amber (#F59E0B) for fluctuating, red (#EF4444) for critical. This replaced arbitrary colour use in the original and gave nurses an immediate visual vocabulary they could read without reading text labels.

Patient Detail UI

Information hierarchy: Emotion data was visually demoted — smaller, secondary positioning. Critical clinical metrics (name, session status, most recent alert) occupy the top-left quadrant of every card, matching the direction nurses said their eye naturally moves.

Explainability layer: The AI emotion score is now accompanied by a micro-tooltip showing the specific facial action unit contributing to the reading (e.g. "Corrugator Supercilii: LOW"). This was one of the highest-impact decisions — during testing, it was the single feature that shifted nurse responses from sceptical to engaged.

Typography: Switched from a display-weight custom font to Inter — chosen for legibility at arm's-length from a monitor under low-light conditions. Font sizes were bumped up 2pt across the board based on observation that many clinical staff are 40+ and working in dim rooms.

Phase 4 — Prototype & Usability Testing (n=5)

I built a high-fidelity Figma prototype covering the three primary workflows: patient overview scan, individual session deep-dive, and alert configuration. Testing used a think-aloud protocol with task completion measurement and SUS administered post-session.

Key results: usability rating improved from 3/5 to 4/5 across all participants. The role-switcher (a new feature I introduced) had 100% discoverability in testing — all participants found it without prompting. The explainability tooltip reduced "I don't trust this" responses from 4/5 participants to 1/5.

One critical finding: participants initially missed the alert threshold configuration because it was nested under a settings icon. I moved it to a persistent sidebar element in the final iteration, and re-tested with 2 participants to confirm discoverability improved.

Phase 5 — Stakeholder Presentation

I delivered a 45-minute research readout to the MorphCast AI product and executive team. The format was: problem framing → research evidence → design decisions → prototype walkthrough → prioritised recommendations matrix.

The recommendations were structured as a 2×2 (impact vs. effort) so the product team could immediately see what to address in the next sprint vs. what to plan for roadmap. Three of five top recommendations were incorporated into the next development sprint, including the role-based dashboard configuration and the explainability tooltip.

Praan AI · Deep Dive

Research & Process

From 21 user interviews to a cross-platform design system — how research-driven pivots and a trust-first AI interaction model shaped every design decision in Praan AI.

Phase 1 — NSF I-Corps Customer Discovery

The project began inside the NSF I-Corps Northeast Hub at Rutgers Propelus. The I-Corps methodology is built around one principle: get out of the building. Before designing anything, I conducted 21 in-depth user interviews with yoga practitioners (beginner through advanced), yoga instructors, and studio owners — one of the highest interview counts in the cohort.

Interview structure: each session was 30–45 minutes, semi-structured, focused on jobs-to-be-done. I wasn't asking about product features — I was asking about the last time they had a frustrating yoga experience, what they wished they'd known, and what feedback they most wanted during a session but couldn't get.

Key finding from discovery: the barrier to feedback isn't the absence of a teacher — it's the vulnerability of being corrected. Practitioners wanted guidance that felt like observation, not instruction. This became the central AI interaction principle.

Phase 2 — Assumption Testing & Hardware Pivot

The original concept was a smart mirror — a hardware product with embedded camera and display. Before investing in the concept, I designed a rapid assumption test:

I asked participants two questions in sequence: (1) "Would you pay $800 for a smart mirror that gives you real-time yoga feedback at home?" — 6 of 8 said no. (2) "Would you use a camera-based AI coach built into an app on your existing iPad?" — 7 of 8 said yes.

The pivot wasn't a gut decision — it was a research output. Removing the hardware barrier without losing the core value proposition (real-time, personalised, judgment-free feedback) was the key insight that shaped the entire product architecture.

Phase 3 — Cross-Platform Design System

Live Session iPad

Designing across iPhone, iPad, and web required a strict component hierarchy. The system was built mobile-first but iPad-primary — research showed that iPad was the dominant use context for live sessions (stable surface, large viewport for pose visibility).

Visual language decisions: The dark, near-black (#0D1117) background was a deliberate choice. Yoga is an intimate, low-stimulation practice. A dark UI reduces visual noise and keeps attention on the practitioner's body, not the interface. The warm gold (#C8A97E) accent was chosen over blue or green specifically because it doesn't read as medical or clinical — it feels earthy, embodied, intentional.

Home Dashboard iPad

Typography: Light-weight (200–300) DM Sans headings with tight tracking for the session UI — minimal text surface so the practitioner's attention stays on movement, not reading. The "good morning, Ananya" personalised greeting was a deliberate warmth signal: the app knows you, not just your account.

AI feedback design: The skeleton overlay (teal joint tracking lines) was the most technically constrained design problem. It needed to be visible on all skin tones and backgrounds without obscuring the practitioner's form. I tested 6 colour options and contrast ratios before landing on the teal-on-dark combination.

Phase 4 — Onboarding as Trust Architecture

Camera Setup

The onboarding flow was designed around a single principle: earn camera access, don't demand it. Practitioners who were hesitant about AI surveillance during intimate practice needed to experience the value of breathwork guidance (no camera required) before the camera ask.

The Camera Setup screen ("Let me see you") was designed to feel like a studio setup moment, not a permissions prompt. The setup checklist (Good lighting ✓, Full body in frame, Clear floor space ✓) frames camera use as a collaboration, and the Privacy First note — "Your camera feed is processed locally on this device. No video data is ever uploaded or stored in the cloud" — directly addressed the most common concern raised in interviews.

Phase 5 — Concept Testing

The prototype was tested with 5 participants in a moderated concept screening session. Metrics: value proposition clarity (did they immediately understand what the product did?), task completion rate, and desirability score.

Results: 4.75/5 concept screening score. 100% of participants immediately grasped the core value proposition. 90%+ task completion across all primary flows. The most-praised element was the AI coach feedback tone — "It felt like it was noticing, not judging" was a direct participant quote.

Knight Plan · Deep Dive

Research & Process

A complete breakdown of the Contextual Design methodology I used to redesign Rutgers WebReg — from 6 stakeholder interviews through affinity mapping, journey modelling, and WCAG auditing to a tested, accessible redesign.

Phase 1 — Stakeholder Interviews (n=6)

I recruited 6 stakeholders across the registration ecosystem: 3 undergraduate students (first-year, sophomore, senior), 1 academic advisor, 1 registrar staff member, and 1 IT developer who maintained WebReg. Sessions were 20–30 minutes, semi-structured, using Contextual Design interview technique — observing participants in their natural environment (at their desk, screen sharing the actual WebReg interface).

This mixed stakeholder sample was deliberate. Students told me what was broken from the front end; the registrar told me why certain constraints existed on the back end; the IT developer explained what was technically feasible to change. Understanding all three perspectives meant my recommendations were grounded in system reality, not just user preference.

Direct quote from a first-year student: "What aspects of WebReg did I find most challenging? Almost everything on my first session — there's no guidance at all."

Phase 2 — Affinity Mapping & Contextual Models

Interview data was organised using Affinity Diagramming in Figma — I converted 120+ data points from interview notes into individual sticky notes and grouped them into themes through iterative clustering. The affinity wall surfaced 4 primary themes: System Fragmentation, No Real-Time Feedback, Lack of Guidance, and Demand for AI.

I then built four Contextual Design models: a Sequence Model (every step a student takes from identifying a course to confirming registration), an Identity Model (how students see themselves relative to the registration system — mostly confused and anxious), a Day-in-the-Life Model (what else is competing for their attention during registration week), and a User Environment Design (the full information architecture as currently experienced).

The most striking finding: students were navigating an average of 5+ separate platforms during a single registration session (WebReg, the course catalog, degree audit, RateMyProfessors, and a group chat). The system fragmentation wasn't just a UX problem — it was causing real decision fatigue at a high-stakes moment.

Phase 3 — Heuristic Evaluation & Accessibility Audit

I ran a dual evaluation: Nielsen's heuristics and WCAG 2.1 AA. The heuristic evaluation identified 12 violations, with the most severe being: no system status visibility (students couldn't tell if WebReg was processing their request or frozen), error messages that identified what went wrong but not how to fix it, and no help documentation that was actually findable.

Knight Plan Welcome

The WCAG audit found colour contrast ratios as low as 2.1:1 on error messages (WCAG requires 4.5:1), form fields without accessible labels, and session timeout with no warning. 14 of 26 applicable checkpoints were failing. For a public university system legally required to be accessible, this was a critical finding — I framed it as a compliance risk in the stakeholder presentation, not just a design issue.

Phase 4 — Visual Design Decisions

AI Advisor

Colour system: Rutgers Scarlet (#CC0033) anchors the brand throughout — it's the one colour every Rutgers student immediately recognises. But the error and status system uses a separate semantic palette: green (#16A34A) for success, amber (#D97706) for warnings, blue (#2563EB) for AI Advisor interactions. Keeping AI interactions visually distinct from core registration actions was a deliberate trust decision — students should always know when they're talking to a system vs. an AI.

Prerequisite status system: The three-state badge (✓ Prerequisite Met / ⚠ In Progress / ✕ Not Met) was the highest-impact visual design decision in the project. In the original system, prerequisite information was buried in a text description. Moving it to a persistent, colour-coded badge on the course card eliminated the most common error type entirely — students stopped adding courses they couldn't enrol in.

Course Search

AI Advisor visual language: The advisor uses blue (#2563EB) exclusively — distinct from the Rutgers Red — with a conversational, left-aligned chat pattern. The "Talk to an advisor" escape hatch is present on every AI touchpoint. This was an ethical design decision: the AI handles recommendations, but a human advisor is always one tap away.

Phase 5 — Prototype & Usability Testing (n=8)

I built a high-fidelity Figma prototype covering all primary registration flows: course search, adding to cart, the AI advisor conversation, and registration confirmation. Testing used a think-aloud protocol with 8 participants: 4 undergrads, 4 graduate students, split between in-person and Zoom sessions (30–45 minutes each).

Registration Confirmed

Results: 9 of 10 participants completed all tasks without assistance. Average SUS score: 84.2 (up from an estimated 54 on WebReg — based on a retrospective SUS I had participants complete after the session). The one failure case: a participant found the prerequisite indicator label text ambiguous — "In Progress" could mean either "currently enrolled in the prerequisite" or "prerequisite completion is in progress." I revised the label to "Prerequisite In Progress" and re-tested with 2 participants to confirm.

Slingshot · Deep Dive

Research & Process

How I led a 4-person UX research team to evaluate 50+ features across Slingshot and 3 competitors — and turned the findings into a product roadmap recommendation that earned an Honorable Mention at the Rutgers MBS Externship.

Phase 1 — Cross-Functional Team Lead

I was the Team Lead for a group of 4 UX researchers across a 12-week externship with Infragistics. My responsibilities went beyond individual research tasks: I owned the research plan, assigned workstreams, set quality standards, ran weekly team syncs, and was the primary contact for the Infragistics product team.

The team was split into two parallel tracks: competitive analysis (2 researchers) and heuristic evaluation (2 researchers, including me). I designed a shared evaluation rubric so findings across both tracks could be directly compared — critical for the final synthesis stage.

Phase 2 — Competitive Analysis (50+ Features)

We evaluated Slingshot against 3 direct competitors: Asana, Monday.com, and Notion. The scope was 50+ features across both web and mobile platforms, mapped against a consistent feature taxonomy I developed at the start of the project.

Every feature was scored across 4 dimensions: availability (does the competitor have it?), implementation quality (how well is it done?), discoverability (how easily can a user find it?), and differentiation (does it create competitive advantage?). This gave us a 200-point comparison matrix — the first time the Infragistics team had seen their product positioned this systematically against competitors.

Key gap identified: Slingshot's embedded analytics were more powerful than any competitor — but scored the lowest on discoverability. The feature that should be winning them deals was invisible to most users.

Phase 3 — Heuristic Evaluation

I led the heuristic evaluation personally, applying Nielsen's 10 Usability Heuristics across every major screen of the Slingshot platform on both web and mobile. Each violation was rated for severity (1–4) and tagged with the affected user type (project manager, team member, admin).

Most critical finding: notification overload. Slingshot's default notification settings generated an average of 34 notifications per working day for a typical project manager — compared to 12 for Asana in equivalent project complexity. This wasn't a minor UX issue — it was causing users to mute all notifications, which meant they missed genuinely critical updates.

Second critical finding: the analytics dashboard, Slingshot's core differentiator, was hidden behind a navigation item labelled "Data." Competitive products used "Analytics" or "Insights" — words that signal value. "Data" signals raw information, not intelligence.

Phase 4 — Synthesis & Prioritisation

I synthesised findings across both research tracks using an Affinity Diagram, clustering 80+ data points into 6 primary themes. I then mapped each theme onto a 2×2 opportunity matrix: user pain severity (x-axis) vs. business impact (y-axis).

Three themes landed in the "high pain, high impact" quadrant: analytics discoverability, notification overload, and calendar integration friction. These became the basis of the roadmap presentation — framed not just as UX problems but as churn risks and conversion barriers.

Phase 5 — Stakeholder Presentation & Impact

I presented a 12-slide research readout to the Slingshot PM and design leads, plus the Rutgers MBS programme directors. Every slide followed the same structure: problem → evidence → recommendation → success metric. This format was a deliberate choice — product teams work in metrics, and framing research findings as measurable outcomes made them immediately actionable.

The analytics discoverability recommendation — rename "Data" to "Insights", add contextual prompts after data entry, surface key analytics on the project overview page — was adopted for an upcoming sprint. Estimated engineering effort: 2–3 weeks. Projected impact: 40%+ increase in analytics feature engagement based on the competitive benchmark.

The team earned an Honorable Mention Team Lead Award from the Rutgers MBS Externship programme — specifically noted for research delivery quality and stakeholder communication.

Sangam Lite · Deep Dive

Research & Process

A full walkthrough of the research-led redesign of Tata Power's Sangam Lite — from business stakeholder alignment through 30 field interviews, pilot study, card sorting, MoSCoW prioritisation, ideation, wireframes, and usability testing to high-fidelity prototype.

Phase 1 — Business Alignment & Desk Research

I started by meeting with the Tata Power business team to understand their primary objective: increase engagement with the existing Sangam app, which had low adoption despite being rolled out to 10,000+ employees. The business hypothesis was that the app was underused because it was hard to navigate — but user research would tell a more nuanced story.

Desk research covered three areas: (1) an IA and user journey audit of the existing Sangam app, (2) a competitive analysis of enterprise employee apps (Workday, ServiceNow Mobile, SAP SuccessFactors, Microsoft Viva), and (3) a heuristic evaluation of the existing interface against Nielsen's 10 heuristics. Key heuristic failures: no shortcuts for database searches, poor hierarchy, and inconsistent design patterns between sections.

Competitive analysis surfaced a critical insight: the best-performing enterprise apps (Workday, Viva) were unified platforms that replaced multiple logins with one. Sangam was still a collection of separate mini-apps requiring individual authentication. That fragmentation was the root cause of abandonment — not poor navigation.

Phase 2 — User Research: Pilot Study + 30 Field Interviews

Pilot Study: Before running the full interview programme, I conducted an informal pilot study with 5 employees to test the interview guide and understand which daily activities they actually performed on Sangam. This shaped the questionnaire significantly — I removed 6 questions that generated no useful signal and added 4 that surfaced richer behavioural data.

User Groups: I defined 4 distinct groups for the main research: On-site workers (10 interviews, Trombay plant), Corporate users (10 interviews, Dharavi office), Remote workers (5 telephonic interviews, Mpl and Bhira), and Non-users (5 interviews with employees who had never opened the app). Each group received a tailored questionnaire — 4 versions total — covering their specific workflows and pain points.

Key findings across 30 interviews: Union staff preferred WhatsApp over the app for group communication. On-site workers couldn't mark attendance without going to a desktop (a daily friction point). Corporate users only opened the app when away from their laptops — meaning the app needed to be useful during travel and leave, not during office hours. Non-users cited data privacy and battery drain as barriers. Employees aged 45+ found typing difficult and asked for voice input.

Phase 3 — Synthesis: Personas, Insights & HMW

I synthesised 30 interviews into 4 user personas representing the key archetypes: Aditya (Lead Engineer, on-site, 34), Eshwaran (retiring manager, 58, low tech literacy), Rohan (graduate engineer trainee, 22, power user), and Kajal (HR manager, 42, form-heavy workflows).

Six primary actionable insights emerged: need for singular login, need for a personalised/flexible home screen, need for automated task management, need for mobile attendance marking, need for simplified overtime and leave approval flows, and need for a searchable safety and policy repository.

From these insights, I generated 6 How Might We statements to frame the ideation phase — including "How might we simplify attendance tracking for on-site employees via mobile?" and "How might we enable users to mark features as favourites to reduce cognitive load?"

Phase 4 — MoSCoW Prioritisation & Card Sorting

MoSCoW: Working from interview data, I categorised all 25+ requested features. Must Haves: Payslip, Leave request/approval, GRC approval, BenefitMe (medical claim), Form 16, PMS, HR Policies, Reimbursement. Should Haves: Attendance, Task Manager, Voice Assistant, HR forms, PO approval, Safety section. Could Haves: Gyankosh, Sodexo, Solar Hero, Do Green. Will Not Haves (out of scope): Manager Connect, CPDC, Office Locator, Stationery Portal.

Card Sorting: Before building the IA, I ran an open card sort with 8 users (mix of on-site and corporate) to understand how employees mentally grouped the 25+ services. The sort revealed a consistent 5-cluster model: Attendance & Tasks, My Services (HR/Finance/Manager), Social & Community, Profile & Settings, and Search/Notifications. This became the navigation backbone.

Phase 5 — Ideation & Visual Design Decisions

I ran a Round Robin ideation exercise with 6 employees and interns to generate concepts for the three most complex features: the AI Task Manager, the Voice Assistant, and the Social Updates feed. This surfaced ideas I wouldn't have generated independently — including the gamified task manager with weekly performance rewards, which employees responded to most enthusiastically.

Visual Design: The design language was anchored to Tata Power's brand: blue (#1560BD primary, #3B82F6 action), white background with light blue surface (#F2F6FF for cards), and semantic status colours (green for success, amber for pending, red for alert). The decision to use rounded cards rather than the flat list view in the original app was driven by touch target research — card-based layouts increase tap accuracy on small screens by ~30% for users with lower digital literacy.

The home screen hierarchy was informed directly by the MoSCoW analysis: Attendance status (check in/out) sits at the very top because 22/30 interviewees said it was the first thing they did in the morning. Quick-access service tiles (Tasks, Leave, Payslip, Safety) are immediately below, with the social feed and recommendations at the bottom.

Phase 6 — Wireframes → Prototype → Usability Testing

I built wireframes in Figma covering 5 primary flows: attendance, task management, leave request, services directory, and voice assistant. Each flow was tested on paper (5 participants) before moving to high-fidelity — a step that caught 3 major IA issues before a pixel was designed.

Usability testing of the high-fidelity prototype surfaced two improvements: icons were too small for older users (increased from 20px to 28px across the board) and users wanted a user manual accessible from the home screen (added as a "Help & Benefits" section). Both were resolved before the final handoff.

NFC attendance was the highest-impact innovation from the research. By proposing tap-to-clock using NFC readers already installed at Tata Power facilities, the redesign eliminated the need for on-site workers to travel back to a desktop solely to mark attendance — reducing that friction by an estimated 60 minutes per affected employee per day.