UX Designer · Enterprise · AI Products · Marketplaces
Designing for Human Logic.
Hey, I'm Rahul. I build enterprise platforms and AI-first products that prioritise human behaviour over pixel-perfect assumptions. Systems-first thinking. Accessibility by default. Occasional stand-up comedian.
5+
Years
12+
Projects
3
Sectors
Worked with
Microsoft
Byteridge
Optimal Strategix
Terre des Hommes
UX Design
Product Design
User Research
Enterprise Platforms
AI Products
Design Systems
Accessibility
Prototyping
UX Design
Product Design
User Research
Enterprise Platforms
AI Products
Marketplaces
Design Systems
Selected Work
Case Studies
01
↗
iOS ApplicationAccessibility-First4 Weeks
Feed Management System
Sole designer on a mission-critical iOS app for large-scale dairy farms — designing for non-tech-savvy users under extreme real-world constraints: time pressure, low connectivity, language barriers, and the unforgiving cost of human error.
The Problem
Dairy farms lacked any unified system to manage feed operations. Workflows were fragmented — leading to feeding errors, inaccurate cost tracking, and wasted resources. Non-tech-savvy Spanish-speaking feeders worked in high-pressure, early-morning environments. A single wrong input during mixing could have direct animal welfare and financial consequences.
My Role
Sole UX/UI Designer — full end-to-end ownership. I led user and persona research, defined the entire information architecture, designed all interaction flows, built wireframes through to high-fidelity UI, and created the interactive prototype in close collaboration with the client's product and engineering teams.
Who I Designed For
Farm Owners — cost visibility and efficiency reports. Feeders — time-critical daily tasks, often in gloves, outdoors, 4am starts. Nutritionists — define and monitor recipes. The feeder persona was the hardest to design for — and the most important.
Design Psychology at the Core
Recognition over Recall — feeders shouldn't have to remember system states. Error Prevention over Error Recovery — in a farm environment, a mistake caught before it happens is infinitely more valuable than a graceful error message after.
5 Decisions I Defended — and Why
① View-First, Edit-Later Flows
Separated viewing and editing states for all critical entities. Error prevention over micro-efficiency, backed by research showing low-literacy users are more likely to trigger destructive actions via accidental taps.
② Iconography-Led Navigation Over Text
Leaned heavily into icon-led UI — grounded in psycholinguistic research on reading load under time stress. Under pressure, users revert to visual pattern recognition. Every icon validated with real users before it shipped.
③ Always-Visible System Status
Connectivity indicators, sync status, and background operation progress persist across every screen. When you're offline on a farm and don't know it, you make decisions on stale data. Visibility of system state is trust infrastructure.
④ Large Cards, Fewer Actions Per Screen
Research on motor accuracy in fatigued users shows significant degradation in fine-motor control. Feeders start at 4am. We designed for the tired, gloved, outdoor version of the user — not the ideal one at a desk.
⑤ Consistent Patterns Across All Modules
Despite different domain concepts — pens, ingredients, feedings, reports — identical navigation structure across all four modules. Hick's Law in practice — reduce decision load by making the next step always predictable.
User ResearchPersona DefinitionIA DesignWireframesHi-Fi UIPrototypeAccessibility ValidationHandoff
Outcome & Impact
Streamlined end-to-end feed operations. Measurably reduced human error during mixing and distribution. Feeders reported significantly improved confidence in time-critical workflows. Farm owners gained real-time cost visibility they never had before.
Enterprise ApplicationMulti-Brand Analytics2 Years
Unified Analytics Dashboard
A 2-year enterprise engagement with Optimal Strategix Group — unifying fragmented data across multiple brands into one workspace that works for both marketing managers and data analysts, without compromising either.
The Problem
A parent company managing multiple brands had no unified view of their data. Teams switched between disconnected tools, manually stitching together reports. Insights came late or got missed entirely. The problem wasn't a lack of data — it was the lack of a coherent system to navigate it.
My Role
Lead UX Designer across a 2-year engagement. End-to-end design — from user research and persona development through information architecture, interaction design, wireframes, and a full high-fidelity prototype, in close collaboration with product stakeholders and engineering.
The Design Challenge
Sarah (Marketing Manager) — works fast, switches brands constantly, needs visual clarity. John (Data Analyst) — needs control, depth, and precision. The risk: build for the analyst and lose the marketer. Simplify for the marketer and frustrate the analyst.
Research Insight That Changed Everything
In a key user interview, Sarah said she wasn't struggling to find data — she was struggling to trust it. That moved our framing from “show more data” to “make data feel reliable and navigable.” Clarity became the design value, not volume.
Design Leadership — Key Decisions & the Thinking Behind Them
① Progressive Disclosure Across Personas
Designed around progressive disclosure — the surface is simple enough for Sarah, but depth is always one intentional step away for John. Miller's Law and cognitive load theory applied as architecture, not just a UI pattern.
② Predictable Workspace Switching as a Trust Mechanism
Folder and workspace navigation spatially consistent — same position, same behaviour, every time. Grounded in spatial memory research: when UI elements move unpredictably, users lose confidence in the system itself.
③ Automated Insight Surface — Removing the Analysis Tax
Pushed the product team to invest in automated trend highlighting — surfacing significant data shifts proactively. Reduce the cognitive effort users must spend just to understand what they're looking at before they can act.
Designed report editing directly inside the dashboard view. Every context switch carries cognitive overhead. Keeping users in flow dramatically lowers the effort-to-insight ratio.
⑤ Consistency as a Scalability Strategy
Consistent navigation, consistent card structures, consistent interaction behaviours. On an enterprise product these aren't aesthetic choices — they're the difference between a tool that scales gracefully and one that becomes UX debt.
User ResearchPersona DevelopmentUser InterviewsIA DesignWireframesHi-Fi DesignPrototypeStakeholder ValidationIterative Delivery
Outcome & Impact
Enabled users to analyse data faster, switch between brands without losing context, edit and publish reports in one place, and connect multiple data sources through a single unified interface. Stakeholders specifically valued how the design balanced power and accessibility.
I'm Rahul Muralidhar — I build enterprise platforms and AI-first products that prioritise human behaviour over pixel-perfect assumptions. With 5+ years of experience, I blend rigorous accessibility standards with a systems-first approach to ensure products don't just look good — they actually solve problems.
I bridge the gap between complex technical constraints and user needs. I'm also a stand-up comedian, so I bring both analytical precision and a necessary sense of humour to high-stakes product teams.
Outside of Figma: obsessing over coffee brewing physics, sketching on napkins, and dissecting why everyday products break.
⚠ Fine Print: Process may involve excessive caffeine and the occasional pun during Sprint Planning.
01
Research First, Always
I don't design from assumptions. Every project starts with understanding real people in real context — their goals, their frustrations, and the gap between the two.
02
Complexity is a Design Problem
If something is hard to understand, that's a design failure. Every moment of confusion is a signal — a gap between what a system does and what a person expects.
03
Inclusive by Default
Accessibility isn't a final checklist — it's a design constraint I embrace from the first sketch. Designing for the edges reliably improves the experience for everyone.
04
Ship, Learn, Iterate
The best design exists in the world and gets improved by real feedback. A prototype shipped to real users always beats a perfect one that never leaves Figma.
05
Collaboration is the Work
Design doesn't happen in isolation. I work closely with product and engineering — not handing off designs but building shared understanding.
06
Details Signal Intent
Error states, empty states, edge cases — these aren't afterthoughts. They're where trust is built or broken. I care about the full experience, not just the happy path.
Tools & Methods
My Stack
Figma
Primary design environment. Components, auto-layout, variables, prototypes, team libraries.
Adobe Creative Cloud
Photoshop, Illustrator, After Effects — visuals, brand work, motion design.
Sketch
Component architecture and pixel-perfect layouts for Sketch-ecosystem clients.
Notion
Research synthesis, design decisions, and project docs shared cross-functionally.
Jira
Sprint alignment, task tracking, design-to-dev handoff across product cycles.
ChatGPT / Claude
Research acceleration, copy refinement, rapid design direction validation.
Zapier
Workflow automation — connecting research tools, survey pipelines, notifications.
HTML / CSS
Front-end fluency to prototype in the browser and communicate precisely with engineering.
Beyond the pixels
Life outside the screen.
Your photo here
Coffee
Your photo here
Stand-up
Your photo here
Sketching
Your photo here
Travel
Your photo here
Reading
Your photo here
Food
Your photo here
Friends
Your photo here
Music
Open to New Projects
Let's build something great.
Available for product design, UX research, and design systems work. I work best with forward-thinking teams who care about craft. Let's talk.
Dairy farms lacked a unified system to track feed costs, ensure correct feeding across pens, and reduce human error during mixing and distribution. Existing workflows were fragmented, error-prone, and heavily dependent on manual processes. This case study is about designing when the cost of getting it wrong is not a bad UX metric — it's a real operational failure.
The Problem
Fragmented Workflows, Real Consequences
The majority of feeders are Spanish-speaking, with limited formal education, working early mornings and long shifts. Key pain points: language barriers leading to mistakes, accidental taps causing incorrect actions, complex navigation, poor visibility of system status, and unreliable internet on farms.
Business Goals
Enable end-to-end feed management in a single mobile app. Reduce feeding errors and operational friction. Surface actionable insights through reports.
User Goals
Complete feeding tasks accurately and without confusion. Understand system status at all times. Access critical information quickly under time pressure.
Design Principles
Psychology as the Foundation
Recognition over Recall
Feeders shouldn't have to remember system states. The UI must always surface what's happening without requiring navigation to find it.
Error Prevention over Recovery
In a farm environment, a mistake caught before it happens is worth infinitely more than a graceful error message after.
Hick's Law
Fewer choices at each decision point means faster, more confident decisions. Each screen designed with one primary action.
Fitts's Law
Targets should be large and close. Given outdoor use in gloves and early-morning fatigue, oversized touch targets throughout.
Design System & Screens
Information Architecture & Core Flows
The system is organised into four primary modules — Home Dashboard, Adjustments, Feed, and Reports — each following identical navigation patterns to reduce learning time and cognitive load.
Information architecture — four-module structure and navigational hierarchy
Design Leadership
5 Decisions I Defended — and Why
① View-First, Edit-Later Flows
Intentionally separated viewing and editing states for all critical entities. An accidental edit to a live recipe carries real-world consequences. Error prevention over micro-efficiency, backed by research showing low-literacy users are significantly more likely to trigger destructive actions via accidental taps.
② Iconography-Led Navigation Over Text
Given the Spanish-speaking user base, I leaned heavily into icon-led UI — grounded in psycholinguistic research on reading load under time stress. Under pressure, users revert to visual pattern recognition rather than reading. Every icon validated with real users before it shipped.
③ Always-Visible System Status
Connectivity indicators, sync status, and background operation progress are persistent across every screen. When you're offline on a farm and don't know it, you make decisions on stale data. Visibility of system state isn't chrome — it's trust infrastructure.
④ Large Cards, Fewer Actions Per Screen
Research on motor accuracy in fatigued users shows significant degradation in fine-motor control after extended physical labour. Feeders start at 4am. Designed for the tired, gloved, outdoor version of the user, not the ideal one at a desk.
⑤ Consistent Patterns Across All Modules
Despite very different domain concepts — pens, ingredients, feedings, reports — identical navigation structure and interaction patterns across all four modules. Hick's Law applied at a systemic level — reduce decision load by making the next step always predictable.
Outcome & Impact
Clarity Built into the System
↓
Measurable reduction in human error during mixing and feed distribution
✓
Farm owners gained real-time cost visibility they had never had before
4
Modules with unified, consistent patterns — learned once, used everywhere
↑
Feeders reported significantly improved confidence and autonomy
Reflection
What I Learned
Designing for real-world environments — where users are tired, rushed, and often offline — reinforced that clarity is more valuable than cleverness. Prioritising recognition over recall, designing generously sized touch targets, and surfacing system status at all times.
One Dashboard. Multiple Brands. Two Very Different Users.
A parent company with several brands needed a clear way to view and analyze data across all of them. Teams were constantly jumping between tools, folders, and workspaces. My task: design a unified analytics dashboard — simple enough for marketers, powerful enough for data analysts.
Users & Research
Two Personas, One Shared Space
Sarah — Marketing Manager
5+ years experience. Handles multiple brands. Tracks many metrics. Prefers visual, simple data. Switches between brands often.
John — Data Analyst
2+ years experience. Digs into detailed analytics. Connects data sources, builds dashboards. Wants flexibility, accuracy, and customisation.
Key Research Insight
In a user interview, Sarah said: “I'm not struggling to find the data. I'm struggling to trust it.” That moved our framing from “show more data” to “make data feel reliable and navigable.” Clarity became the design value, not volume.
User flow and scenario mapping for the multi-brand dashboard
Dashboard Design
A Dashboard That Guides, Not Overwhelms
The final dashboard structure was built around four core capabilities — workspace switching, report editing, insight discovery, and data source management — each designed to reduce a specific type of friction identified in research.
The unified dashboard — all brands and data sources in one coherent view
Design Leadership
5 Decisions I Defended — and Why
① Progressive Disclosure Across Personas
Rather than building two separate interfaces, I designed around progressive disclosure — the surface is simple enough for Sarah, but depth is always one intentional step away for John. Miller's Law and cognitive load theory applied as architecture, not just a UI pattern.
② Predictable Workspace Switching as a Trust Mechanism
Folder and workspace navigation designed to be spatially consistent — same position, same behaviour, every time. Grounded in spatial memory research: when UI elements move unpredictably, users lose confidence in the system itself.
③ Automated Insight Surface — Removing the Analysis Tax
Pushed the product team to invest in automated trend highlighting — surfacing significant data shifts proactively. The principle: reduce the cognitive effort users must spend just to understand what they're looking at before they can act on it.
Designed report editing directly inside the dashboard view. Every context switch carries cognitive overhead. Keeping users in flow dramatically lowers the effort-to-insight ratio.
⑤ Consistency as a Scalability Strategy
Consistent navigation patterns, consistent card structures, consistent interaction behaviours — the difference between a tool that scales gracefully and one that becomes a UX debt liability every six months.
Outcome & Impact
Power and Simplicity in Balance
↓
Significant reduction in tool-switching and manual report stitching
2+
Years of iterative engagement — reflecting depth of trust and complexity delivered
Client satisfaction and decision-making confidence across both personas
Reflection
Clarity Over Complexity
This project reminded me that clarity is sometimes more important than complexity. A tool can be powerful, but if people are intimidated by it, they won't use it well. Listening carefully to users — especially people like Sarah — helps shape not just features but the feeling of the interface.
Microsoft
Byteridge
Optimal Strategix
Terre des Hommes
