OCR extracts business names, formation dates, and addresses from uploaded documents automatically. But users still need to trust what the system found before anything gets submitted.

We designed the confirmation flow around editable fields that surface extracted data clearly. Users review what the system found and correct where needed, rather than typing everything from scratch. The design choice was to make the AI output feel transparent and controllable.

The design choice was to make the AI output feel transparent and controllable. The result is a flow that reduces data entry to the minimum while keeping users fully in control of what gets submitted.

The editable-fields approach is smart, letting people verify OCR output instead of trusting it blindly probably saves you a ton of support tickets down the line.

In this project, I worked on cleaning raw sales data and transforming it into meaningful insights using Microsoft Excel. I applied pivot tables and interactive filters to organize information by city, product, payment method, and customer. The analysis highlighted key performance metrics such as total quantity sold, total revenue, and profit across five major cities.

Mumbai emerged as the top-performing city with the highest revenue and profit, while Chennai showed the lowest figures. The final dashboard included a 3D bar chart for clear visualization, making it easy to compare city-wise performance at a glance.

This project demonstrates my ability to:

Perform data cleaning and structuring

Build pivot tables for multi-dimensional analysis

Apply filters for dynamic exploration

Create professional charts for business insights

Impact: The analysis provided actionable insights into regional sales performance, helping identify strong markets and areas for improvement.

How can I work with clients?

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