PORTFOLIO / 2026 · HYDERABAD, INDIA

UDAYKUMAR
KOTIPALLI

DATA ANNOTATION × AI EVALUATION × ANALYTICS

“Reliable data doesn't happen by accident.
It happens by checking the details.”

反REVIEW
REFINE
REPEAT
INDEPENDENT BUILDER / DETAIL-DRIVENSCROLL TO EXPLORE ↓
PROFILE FILE: UK-001

›› A little about me_

I turn messy inputs into clear, checked, usable data.

I'm a Computer Science Engineering graduate and freelance Data Annotator at Multimango AI. My work focuses on annotation, reviewing AI-generated outputs, validating records, and following detailed project guidelines with consistency.

BASEHyderabad, India
EDUCATIONB.Tech · CSE · 2025
WORK MODERemote / Independent
“BUILD SMALL. VALIDATE HARD. IMPROVE CONTINUOUSLY.”
✿
LOADOUT

SKILL GRIMOIRE

TOOLS & CAPABILITIES

DATA & QUALITY

Data AnnotationData LabelingData CategorizationData ValidationQuality CheckingQA

AI EVALUATION

AI Output EvaluationContent EvaluationHuman FeedbackError IdentificationGuideline Adherence

TOOLS & TECH

PyPythonBasic
XExcelSpreadsheets
▦Google SheetsData review
⌘Git & GitHubVersion control
{ }CSV / JSONStructured data
⌕Internet researchInformation review

WORK STYLE

Attention to DetailAccuracy & ConsistencyTime ManagementRemote WorkAdaptability
✿
SELECTED WORK

PROJECTS THAT SHOW THE PROCESS

ALL REPOSITORIES ↗

Small, practical utilities built around repeatable checks. These are independent portfolio projects—not employer tools or client deliverables.

DATA / 001
quality_report.csv
ROWSTATUSCHECK001PASSclean002REVIEWmissing003PASSvalid
QUALITY REPORT↗
PYTHONPANDASCSV

Data Quality Inspector

A lightweight CSV checker for missing cells, duplicate rows, empty strings, email-format issues and numeric conversion problems.

ANNOTATION / 002
ANNOTATION REVIEW ● LIVE
✓Required fieldsPASS
!Allowed labelsCHECK
✓Confidence rangePASS
!Duplicate IDsCHECK
REVIEW REQUIRED02 ISSUES
PYTHONCSVRULES

Annotation QA Toolkit

Rule-based validation for required fields, allowed labels, empty annotations, confidence scores and duplicate record IDs.

AI REVIEW / 003
RESPONSE EVALUATION 01
Relevance
4.5
Accuracy
4.0
Clarity
5.0
SAMPLE UI · ILLUSTRATIVE SCORES
PYTHONJSONEVALUATION

AI Output Evaluation Lab

A structured scoring example for reviewing AI-generated answers against explicit criteria on a 1–5 scale.

FOUNDATION

EDUCATION & LANGUAGES

学

B.Tech — Computer Science Engineering

GIET Engineering College · Rajahmundry, JNTUK

GRADUATED 2025
TeluguEnglishHindi
OPEN CHANNEL

LET'S MAKE THE
DETAILS COUNT.

Interested in data annotation, AI evaluation, data quality, or analytical projects? Get in touch.

STILL
LEARNING.
ALWAYS
BUILDING.

“My magic is never giving up.”

— ASTA · BLACK CLOVER