You will get Analyst-Validated Data Extraction & Cleaning (Full Audit Trail)
Project details
Cleaning a spreadsheet is easy. Proving it's actually clean is the hard part — and that's the part most data cleanup services skip. In my day job, I audit AI-generated analysis against ground truth, catching errors before they reach a client. I apply that same discipline here: every "cleaned" value in your data is a live formula referencing your original file, not manually retyped, so you can verify exactly how each number was transformed. Duplicates and missing fields are flagged automatically, not eyeballed — including near-duplicates a simple exact-match check would miss (e.g., the same customer entered with different capitalization). You get a validation summary alongside the clean data: a data quality score, not just "trust me, it's done." A sample workbook is attached showing the full process, raw import through QA audit trail.
Data Entry Type
Data Cleansing, Document Conversion, Error Detection, Online Research, Word ProcessingData Entry Tool
Google Docs, Google Sheets, Microsoft Excel, Microsoft Office, Microsoft WordWhat's included
| Service Tiers |
Starter
$175
|
Standard
$450
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Formatting & Clean Up | - | - | - |
Graph & Table Creation | - | - | - |
About Liudmila
AI Output Validation & Data QA Specialist
Sayreville, United States - 3:53 pm local time
My core work spans data quality assurance, custom data pulls, and research design. I build validation frameworks that catch structural and logical errors, write SQL queries and Python scripts to surface anomalies, and turn messy, high-volume datasets into clean outputs that decision-makers can use with confidence.
Lately, a growing part of my work is AI output validation — reviewing AI-generated analyses and reports for factual accuracy, logical consistency, and methodology gaps. As AI tools produce faster than most teams can QA, I've become the human check that catches what the automation misses.
Remote, reliable, and responsive. If you're not sure whether your data — or your AI's data — is telling you the truth - lets connect.
Steps for completing your project
After purchasing the project, send requirements so Liudmila can start the project.
Delivery time starts when Liudmila receives requirements from you.
Liudmila works on your project following the steps below.
Revisions may occur after the delivery date.
Order & Requirements
Client purchases the project and sends the raw file, desired output format, and any specific cleaning/validation rules.
Initial Review
I scan the raw data for structural issues (format inconsistencies, likely duplicate patterns, missing-field prevalence) and confirm scope before cleaning begins.