A User Experience Designer with 15+ years of experience across SaaS, enterprise, and AI-driven products working across research, strategy, and design to solve complex problems in a simple, human way. I also bring AI into my UX workflows using tools like Figma Make, ChatGPT, Claude, and Gemini to speed up ideation, streamline processes, and make better design decisions.
Designing a dynamic pricing ecosystem for the open content economy - Terminal, Publish, and Signal powered by LPM.
Designing a simplified AI/ML-based CRM that eliminates data entry friction for modern sales teams.
Mission-critical interface design for A350 and A380 aircraft maintenance and authorisation systems.
Transforming raw call data into actionable insights for sales and marketing teams.
Designing a unified super app for SMBs on Android.
Building a mobile-first sales call management app for small and medium businesses.
Designing enterprise-grade scheduling and workforce optimisation tools for complex operational environments.
Building a three-part ecosystem that gives creators real pricing power and readers frictionless access - through Terminal, Publish, and Signal.
ZZAZZ is building the infrastructure layer for the content economy - a system that replaces today’s broken creator monetisation model with one that’s dynamic, fair, and friction-free on both sides. Three products form the ecosystem: Terminal (consumer discovery and access), Publish (creator publishing and analytics), and Signal (a lightweight embeddable widget). Powering all three is LPM - a Large Pricing Model that sets real-time access prices based on demand, engagement, and context.
My role was to translate this ambitious technical vision into product experiences that felt natural to use, from the first search query to the first payout.
The product team comprised 3 PMs and 4 Product Designers.
Tools used across the project:
AI-integrated workflow I used - Gemini compressed a 2-week research phase into 3 days via competitor teardowns and paywall UX sweeps; Claude converted outputs into PRD drafts, user stories, and API specs within hours, and auto-populated Jira and Confluence via MCP as frames moved through review; Figma Make generated testable Signal widget variants from a single prompt, cutting design iteration from ~2 hrs to under 15 min.
Creators are locked into subscription-or-free binaries that leave money on the table, while readers drown in recurring bills just to follow the writers they like. Neither side wins - the market needed a new pricing primitive.
Where the system breaks - creators can't price their work fairly, and readers won't pay through friction-heavy subscription walls.
ZZAZZ introduces the Large Pricing Model (LPM) - a dynamic pricing layer that replaces the binary free/paid choice with a full pricing spectrum set in real time, based on content demand, reader engagement history, and context signals. Readers pay only for what they actually want to read; creators get a tuned, automated monetisation layer without configuration overhead. Three interconnected products make this work in practice:
Given the novelty of dynamic per-article pricing, research needed to probe both behaviour and mental models - not just what people do, but what they believe about paying for content. I ran four parallel tracks.
The existing landscape locks creators and readers into one of two models: fully free (ad-supported) or fully paywalled (subscription). No major player offers granular, per-access dynamic pricing at scale.
Survey - 142 responses across creators and readers, validating qualitative findings at scale.
Four research tracks converged on the same gap: no platform gave creators pricing control and readers a frictionless way to pay per article simultaneously. That gap wasn't a feature request, it was the entire product opportunity. Everything that followed was designed to close it.
Key insights - five patterns emerged consistently across all research methods.
142 individual observations from 10 interviews were grouped into 6 primary themes, each representing a recurring pain pattern across creator and reader segments.
Five patterns held across every method. Subscription fatigue is about commitment anxiety, not price. Creators want guardrails, not controls. Transparency in pricing signals trust more than the number itself. One pattern, Identity & Continuity, surfaced unexpectedly and expanded the scope beyond what the original brief anticipated.
Three distinct user types emerged from the research. Each maps to one or more products in the ZZAZZ ecosystem.
8,400 subscribers, left his agency job 18 months ago - earnings still don't cover rent.
Pays for 4 subscriptions, opens one. Her inbox is a graveyard of good intentions.
Edits a policy and business publication. AI is eroding search traffic; needs a direct-reader model without hard paywalls.
Following Arjun across six stages - from discovery to the loyalty flywheel.
Five HMW statements focused the ideation phase and prevented scope creep.
Three personas, three surfaces, but one shared constraint: trust. The priority matrix forced a clear order. First-payment flow and pricing legibility ranked above every configuration feature. Anything that added steps before a reader paid or before a creator understood their earnings was deprioritised, regardless of how requested it was.
Three principles governed every decision across Terminal, Publish, and Signal.
One platform, two views. Terminal is the reader-facing app; Publish is the creator dashboard. Both live inside ZZAZZ. Signal is a separate embeddable widget. Terminal is a reader-facing app, Publish is a creator CMS, Signal is a stateful embed widget.
Two core flows shaped the interaction model: the Reader path from discovery to paid access, and the Creator path from signup to analytics. The paywall handoff - where Terminal cedes control to Signal - is the most critical moment in both.
Designing across three products simultaneously made one thing clear: a shared system wasn't optional. Every component had to carry meaning in three different contexts: discovery, publishing, and embedding. The design language that emerged wasn't a style guide; it was the connective tissue that made Terminal, Publish, and Signal feel like one ecosystem rather than three separate tools.
87%
Avg. task completion
78
SUS score (Good)
−34%
Time on task vs. V1
3
Critical issues found
Three critical issues. All three fixed before handoff. The clearest signal from testing: pricing transparency wasn't a trust-builder, it was a prerequisite. Without it, readers stalled before they even reached a payment decision. Small copy and hierarchy changes moved completion rates by margins that no visual redesign alone would have achieved.
Redesigning CRM from the ground up - powered by NLP (Natural Language Processing) to eliminate manual data entry through voice-first interaction so sales teams can focus on what they actually do best.
SalesX is reinventing the CRM experience through NLP-powered voice interaction - allowing sales teams to capture, log, and manage data entirely by voice, and focus purely on selling. Traditional CRMs have evolved into systems that demand excessive manual data entry, often shifting the focus of relationship managers from client engagement to administrative tasks.
The goal: create a self-driving CRM that captures, organises, and analyses sales activities automatically, making manual data entry obsolete.
User research · Personas · Problem statements
Our user research revealed that sales professionals struggle with excessive manual data entry, context switching across multiple tools, and inefficient follow-up management. SalesX addresses these pain points by automating data capture, organising interactions intelligently, and providing AI-driven insights to streamline the sales workflow.
Sales professionals spend excessive time logging interactions, with 82% missing crucial details and 67% delaying note entry, leading to irretrievable information loss.
Managing customer relationships requires juggling multiple tools, causing 73% to lose focus mid-task and 89% to desire automatic activity logging.
Manual tracking leads to 64% missing deadlines, 91% wanting AI-driven reminders, and 78% struggling to maintain accurate customer histories.
Sarah manages large enterprise accounts at a B2B SaaS company, handling deals worth $500K+. Tech-savvy but values efficiency over feature complexity.
of selling time lost to CRM admin - blocking her $1.5M monthly quota
David leads a team of 12 sales reps, focusing on team performance, forecasting, and process optimisation. Data accuracy is his greatest daily blocker.
of CRM data is outdated - forcing 15+ hrs/week on validation instead of coaching
Invisible interface · Contextual intelligence · Minimal interaction
Auto-prioritisation of important emails and seamless interactions. Subtle background updates without user intervention - context-aware recommendations that appear only when needed.
Automatic linking and categorisation of information. Smart relationship tracking and predictive actions. Predictive text and recommended next steps - reducing cognitive load at every turn.
Low-fidelity wireframes · Usability testing · High-fidelity prototype




Calls - the heart of the sales workflow
Intelligent automation handles the logging, leaving reps to focus on building relationships.
A unified component library supporting both web and iOS, enabling consistent shipping velocity.
Measurable improvements in deal logging speed and overall CRM engagement from day one.
Designing an AI-powered analytics feature that transforms raw call data into actionable intelligence - helping sales and marketing teams make faster, better decisions.
AI Insights is an AI-powered analytics feature that enhances sales and marketing by analysing customer call interactions. It provides actionable insights to improve engagement, optimise marketing efforts, and refine sales strategies - helping businesses make data-driven decisions efficiently.
The existing product lacked AI capabilities and offered only a basic analytics dashboard without deeper intelligence. The opportunity: introduce a native AI Insights tab with a customisable dashboard and recommendations that surface critical insights seamlessly.
User research · Competitive analysis · Personas · Problem statements
Stakeholder and customer discussions shaped the direction - covering the right formats and visualisations for insights, how to simplify complex data, alignment with customer workflows, and the role AI should play in improving sales efficiency.
Existing platforms present raw data without clear insights, making it difficult for businesses to extract meaningful information from call interactions.
Competitors offer basic analytics but fail to provide AI-driven recommendations that help businesses optimise their sales and marketing strategies.
Many analytics tools require technical expertise, making it challenging for non-technical sales and marketing users to navigate and leverage insights.
Without a centralised AI-powered dashboard, businesses struggle to make quick, data-driven decisions that improve engagement and conversions.
Rajesh is a sales executive lead at an auto dealership who handles a high volume of customer calls daily. He relies on call data to track leads and close deals, but the current system gives him raw numbers with no context or follow-up guidance.
manual analysis time wasted per week - missing high-potential leads and slowing decision-making at a critical point in the sales cycle
Four sections · Seamless navigation · AI-driven structure
AI Insights structures data into four key sections: Dashboard (sales metrics overview), Potential Opportunities (lead likelihood scoring), Models (product-level insights), and Call Log (interaction-level analysis). This IA-driven approach enables seamless navigation, making AI-powered insights more accessible, actionable, and intuitive.
High-level sales metrics - total calls, AI-processed calls, sales intent score, potential opportunities breakdown, top dealers, top keywords.
Lead likelihood cards (Likely / Neutral / Not Likely) with top reasons, top questions asked, and trend data for prioritising follow-up actions.
Product-specific insights including transmission, fuel type, and colour preferences with variant-level details to inform inventory and marketing decisions.
Interaction-level analysis with AI call summaries, keyword tags, and a detail overlay for reviewing individual call recordings and transcripts.
Wireframes · Draft UI · Stakeholder review
Dashboard & Models
Dashboard detail
Potential Opportunities
Call Log
Usability study · Before & after · Hi-fi prototype
Users found the date picker controls unclear. Simplified the filter row to make time-period selection faster and more intuitive.
The Models section contained too much data density. Reorganised the layout with clearer hierarchy and tab-based navigation by model variant.
The left-hand navigation lacked clear active states and section separation. Refined with stronger visual hierarchy and consistent active indicators.
The call detail overlay was difficult to scan. Enhanced the slider layout with better structured call summaries, keyword tags, and recording controls.








Intuitive data accessibility, improved sales conversions, and a competitive differentiation through native AI.
The design established a foundation for future AI feature growth across the Waybeo platform.
Data depth, personalised insights, and better end-user guidance were identified as the next priorities for the product.
AI Insights has completely transformed how we analyse customer interactions - it’s intuitive, actionable, and has directly boosted our sales conversions.Tata Motors Regional Sales Head
Designing enterprise-grade scheduling, optimisation, and insight tools that balance operational complexity with everyday usability.
Verint's Workforce Management (WFM) Suite is a comprehensive enterprise solution designed to optimise scheduling, improve customer and employee experiences, enhance flexibility through mobile tools, and provide actionable insights via scorecards.
The product serves contact centres and large operations teams where scheduling errors carry significant business consequences. Every design decision had to balance the rigour demanded by enterprise users with the clarity needed for day-to-day efficiency.
WFM users are experts in their domain - they think in shift patterns, coverage metrics, and service level agreements. The design had to honour that expertise while eliminating unnecessary friction and surfacing the right controls at the right moments.
Accessibility was a non-negotiable requirement, with compliance standards built into the design process from the outset rather than retrofitted at the end.
Working as an embedded designer within Verint's product team, I participated in user research sessions, design reviews, and cross-functional collaboration with engineering and product management.
The iterative design process involved regular usability reviews, accessibility audits, and close collaboration with developers to ensure design intent was preserved through implementation.
The redesigned suite improved scheduling efficiency, reduced training time for new users, and achieved compliance with accessibility standards. The design system established during this project became the foundation for future WFM product development.
Designing mission-critical interfaces for A350 and A380 aircraft maintenance, and a secure authorisation platform for enterprise services.
ArGO is a specialised application for managing the maintenance and operation of commercial aircraft on the ground and in flight - primarily for Airbus A350 and A380 models. The service is provided by Airbus to airline companies worldwide.
The stakes here are extraordinarily high. Every interface decision - a label, an information hierarchy, a confirmation flow - has safety implications. Designing in this domain required extreme rigour, deep collaboration with domain experts, and continuous testing with real maintenance engineers.
Large volumes of highly sensitive data are transferred between aircraft and ground systems through ArGO, accessible only within secured Airbus networks. Security and clarity had to coexist throughout.
The project began with extensive field research - understanding how maintenance engineers actually work, the cognitive load they operate under, and the failure modes of existing tools. User journeys were mapped in granular detail before any interface work began.
Prototypes were tested iteratively with actual ArGO users, incorporating feedback from both technical stakeholders and end users in airline maintenance operations.
Core Elec is the authorisation platform underpinning the broader Airbus digital ecosystem - managing access to APIs, UI surfaces, and user roles across multiple interconnected products.
Designed for a large-scale solution where multiple products work in concert, the platform required a clear, consistent model for permissions, roles, and access states that technical administrators could operate confidently.
The ArGO interface shipped to airline partners and has been in active use for A350 and A380 fleet management. Core Elec established a reliable access-control foundation for Airbus's growing digital product suite.
Building a mobile-first sales call management app for small and medium businesses using Material Design principles.
Tring Partner is a mobile-based application for small and medium businesses to manage sales calls within a team or across multiple teams. The product targets the growing SMB segment that needs CRM-like capabilities without the enterprise overhead.
The design challenge was building something powerful enough to be genuinely useful for sales teams while remaining simple enough for adoption across non-technical users in small businesses.
The project used Material Design as the foundational design language, collaborating closely with the development team to ensure the implementation matched the intended experience.
I worked across UX flows and UI execution - designing the information architecture, interaction patterns, and visual components that made the app feel native and intuitive on Android devices.
Tring Partner shipped with a clean, consistent Material Design implementation that gave SMB sales teams a reliable tool for call logging, team coordination, and performance tracking - all from their mobile devices.
Designing a unified super app for small and medium businesses on Android.
Onne App is a super app for small and medium businesses, built natively for Android. The product - Onne App and Onne Business - aimed to consolidate team communication into a single, reliable mobile experience.
For SMBs operating without dedicated IT infrastructure, a communication tool needs to be intuitive from day one, reliable under real business conditions, and simple enough that the entire team adopts it.
The project required careful information architecture work to organise the app's features - messaging, calls, team management, and business tools - into a hierarchy that felt natural rather than cluttered.
The design balanced the breadth of functionality with a clean, uncluttered interface, ensuring that everyday communication tasks required minimal steps and cognitive effort.
Onne App launched on Android with a structured, navigable interface that brought together the key communication needs of SMB teams. The information architecture established a clear foundation for future feature expansion across Onne Business.
Great design solutions don't exist in isolation - they are part of larger systems. Understanding the full context, the people, their needs, and the environment they operate in is where meaningful design begins.
Design decisions grounded in data, research, and rational insights rather than intuition alone. Every choice answers a question, informs a decision, or validates an assumption.
The best design fades into the background, letting people focus on what matters. Technology should be a quiet enabler - seamless, intuitive, and unobtrusive.
Using user journeys and service blueprints to map the interconnectedness of different parts of the system, creating cohesive experiences across physical and digital touchpoints.
Design as exploration. Prototyping quickly, testing assumptions, and iterating based on what's learned - embracing uncertainty as a path to better solutions.
The best work happens at the intersection of disciplines. Facilitating workshops, bridging engineering and design, and building shared understanding across teams.
Guiding teams through ambiguity, establishing design processes, and creating the conditions where great work can emerge. Design leadership is about enabling others.
I've spent that time learning how to navigate complexity, adapt quickly, and build with intent - largely focused on making systems easier to understand and use, while balancing business needs and user expectations.
Beyond product design, I've explored different paths early in my career - experimenting with building communication solutions, working on branding initiatives, and even attempting to create a community platform for filmmakers and reviewers. Not everything worked, but each experience shaped how I approach problems today - with curiosity, resilience, and a bias toward action.
I enjoy collaborating with people who challenge ideas and push for better outcomes. Whether it's mentoring designers, working closely with cross-functional teams, or refining workflows, I care about creating environments where good design can thrive.
Outside of work, travel plays a big role in how I see the world. I genuinely love exploring new places, experiencing different cultures, meeting people, and gaining fresh perspectives wherever I go. I've travelled to over a dozen countries so far, and I'm always looking for the next experience.