Expense Manager
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The Challenge: Updating a large volume of product data sheets for Heskins LLC. The task involved changing Copyrights, Emails, and converting SKUs specifically for US, Canadian, and Mexican markets without altering other technical data.
⚡ My Solution:
Region-Specific Logic: Updated SKUs to US versions for US files, and Canadian/Mexican versions for French files.
Brand Compliance: Replaced “Heskins Ltd” with “Heskins LLC” across all footers.
Precision Editing: Only touched specific sections (SKUs, Contact info) while keeping the rest of the content intact.
The Challenge: Converting a massive 176-page PDF (40k words, 110MB) into a fully editable MS Word document. The biggest challenge was maintaining the exact original layout, fonts, and formatting which auto-converters usually destroy.
⚡ My Solution:
Manual Formatting: Retyped and adjusted complex sections where software failed.
Layout Preservation: Ensured headers, footers, and paragraph styles matched the original exactly.
Optimization: Created a lightweight Word file that was easy to edit further.
The Challenge: The client provided a list of consulting firm URLs but lacked key reputation metrics. They needed to find specific social proof data like Yelp reviews, Google ratings, and testimonials for each firm.
⚡ My Solution:
Digital Investigation: Researched each firm to locate their specific Yelp IDs and Google Business Profiles.
Data Aggregation: Extracted specific star ratings, review counts, and testimonial text.
Mapping: Organized all social proof data back to the original firm URLs for easy comparison.
The Challenge: Compiling and verifying detailed data for 4,000+ healthcare companies to support a strategic outreach campaign. The client needed accurate founder details, revenue estimates, and verified LinkedIn profiles.
⚡ My Solution:
Deep Research: Used platforms like Crunchbase and LinkedIn to verify company founders and roles.
Data Enrichment: Extracted estimated revenue, websites, and founding years.
Quality Control: Followed a strict framework to ensure every LinkedIn URL and data point was active and valid.
The Challenge: The client needed to extract structured data from complex academic PDF syllabi into a clean spreadsheet.
Goal: Capture specific details like course names, reading lists, authors, and page counts—data that automated tools often miss or mess up.
⚡ My Solution:
Manual Precision: Manually extracted course details and university names to ensure 100% accuracy.
Data Structuring: Organized reading lists with author names and online source URLs into a strict format.
Verification: Cross-checked page counts and source links for every entry.
The Challenge: A client provided 4 separate spreadsheets containing over 4,000+ mixed customer records. The data was heavily duplicated, with inconsistent names (e.g., “Raj S.” vs “Raj Sahu”) and unformatted phone numbers.
Goal: Create a single, 100% clean Master Sheet for CRM import within 48 hours.
⚡ My Automated Solution: Instead of manual sorting, I engineered a custom Excel workflow:
Smart Merging: Consolidated all 4 sources into one raw database.
Advanced Logic: Applied formulas to identify duplicates based on “Name + Mobile Number” while preserving unique Email IDs.
Standardization: Auto-formatted all phone numbers to include country codes (+91) and fixed name casing.
Error Trapping: Used conditional formatting to highlight potential conflicts for a final manual check.
