Smart Personalised SaaS (UI/UX) web apps designed for NEC Corp

1. e-Market Intelligence Platform Adobe XD Prototype: Click anywhere on the prototype to view next screens or use left/right arrow keys to view screens. Reload browser page for Login screen.

Brief The user journey follows a structured, step-by-step data intelligence pipeline designed for clarity and progressive task completion. It begins with the Web Scraper, where users select marketplaces and product categories to initiate data collection. The system then transitions into Data Cleaning, allowing users to standardize and refine raw data through selectable preprocessing actions, with immediate visibility of cleaned outputs.

Next, in Top Product Features, users define key comparison parameters, ensuring relevance in downstream analysis. The flow then advances to Insights, where visual dashboards present aggregated metrics, enabling quick interpretation through charts, KPIs, and filters.

Users can further explore Product Similarity, comparing datasets across platforms with highlighted matches and configurable filters, before proceeding to Price Comparison for final evaluation.

Final UX Output: A guided, modular, and data-centric experience that transforms raw multi-source data into actionable insights through a seamless, low-friction, and visually progressive workflow, enabling efficient analytical decision-making.

2. Customer Segmentation Adobe XD Prototype: Click anywhere on the prototype to view next screens or use left/right arrow keys to view screens. Reload browser page for Login screen.

Brief A comprehensive, step-driven UX flow is designed to simplify the complex problem of customer segmentation using machine learning for non-technical and business users. The journey begins with a clean login interface, followed by a guided dashboard experience that introduces the solution as a one-stop platform. Users are led through a structured pipeline—starting with data upload, validation, and tabular preview to ensure accuracy and trust.

The flow progresses into data preparation, where missing value handling is streamlined to reduce preprocessing friction. Interactive insights through visual analytics (charts and geo-maps) enable users to understand patterns before modelling. Feature engineering options such as RFM and normalization are presented with simplified controls, effectively abstracting technical complexity.

Model selection (K-Means, DBSCAN, etc.) is integrated with clear decision points, allowing users to run clustering without deep ML expertise. Finally, cluster-wise segmented outputs and visual graphs deliver actionable insights for faster decision-making.

This end-to-end UX flow was designed and prototyped in Adobe XD, where component-based layouts, repeat grids, and interactive prototyping were used to simulate real user journeys. Micro-interactions, transitions, and step indicators were carefully crafted to ensure a guided, intuitive, and scalable user experience.

3. Driver Risk Profiling

   Adobe XD Prototype: Click anywhere on the prototype to view next screens or use left/right arrow keys to view screens. Reload browser page for Login screen.

Brief

A guided, step-by-step UX flow is designed to simplify driver risk profiling using data and ML models. The journey starts with a login screen, leading to a visual pipeline where users progress through stages like data upload from multiple sources, ensuring flexibility. Users then access data preview and schema validation, followed by data cleaning with options for handling missing values, outliers, and duplicates.

Next, data exploration dashboards provide insights through charts, helping users understand driving patterns. A model comparison step allows selection between clustering techniques, reducing decision complexity. Finally, risk-based clustering outputs categorize drivers (e.g., high/low risk) with visual graphs, enabling actionable insights for insurance or safety decisions through an intuitive, guided experience.

4. Product Demand Forecasting

   Adobe XD Prototype: Click anywhere on the prototype to view next screens or use left/right arrow keys to view screens. Reload browser page for Login screen.

Brief

A structured, end-to-end UX flow is designed to simplify product demand forecasting using AI/ML for business users. The journey begins with a guided data ingestion layer, where users can upload datasets from local systems, cloud sources, or databases, ensuring flexibility and accessibility. Upon successful upload, a data preview and validation step builds confidence by allowing users to review both data and schema.

The flow then transitions into data cleaning, where missing values and inconsistencies are handled, ensuring data quality. Users proceed to interactive insights dashboards, where visualizations uncover patterns across regions and categories.

Next, the model selection stage offers intuitive choices like Linear, Ridge, and Lasso regression, reducing technical complexity. Finally, the system generates forecast outputs with performance metrics (R², MSE, RSS) and visual comparisons of actual vs predicted values.

This guided, step-by-step experience ensures clarity, trust, and faster decision-making, transforming complex forecasting workflows into an intuitive product journey.