You will get Data Cleaning, Validation & Deduplication for Your Contact List (CSV/Excel)
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Project details
Send the list you already have (CSV or Excel) and get it back trustworthy: clean, validated, and deduplicated. Most business lists look fine and are quietly full of duplicates, dead emails, and broken phone numbers, and you only find out when an outreach campaign bounces.
You send the raw file: a CRM export, a list your team exported from a tool you use, or a hand-built spreadsheet. I normalize every field, validate email, phone, and url, and deduplicate on a key computed in code, including the same business entered under a name variant. You get back a clean CSV or Excel with a fixed column order, plus a brief that accounts for every record: delivered, deduplicated, or held back, each with the reason.
Why me: validity is decided by tested Python, not trusted by eye. The pipeline behind this carries 41 tests scored against a known answer, so what it catches is measured, not assumed.
How it works: you send the file, we agree the column order once, and I deliver the clean file, a method note, and the brief. The default cleaning runs fully offline.
You send the raw file: a CRM export, a list your team exported from a tool you use, or a hand-built spreadsheet. I normalize every field, validate email, phone, and url, and deduplicate on a key computed in code, including the same business entered under a name variant. You get back a clean CSV or Excel with a fixed column order, plus a brief that accounts for every record: delivered, deduplicated, or held back, each with the reason.
Why me: validity is decided by tested Python, not trusted by eye. The pipeline behind this carries 41 tests scored against a known answer, so what it catches is measured, not assumed.
How it works: you send the file, we agree the column order once, and I deliver the clean file, a method note, and the brief. The default cleaning runs fully offline.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$120
|
Standard
$300
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Frequently asked questions
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SB
Steve B.
Aug 6, 2026
Reddit Data Collection: r/adjusters Complete Thread and Comment Scrape
Vinicius was fantastic. I had a fairly complicated project for him involving data-scraping (one that I did not fully understand) and Vinicius worked with me on focusing the project scope before carrying out seamlessly. His communication was fantastic, he was extremely responsive, and he delivered exactly what was promised extremely quickly.
I could not recommend him more highly.
I could not recommend him more highly.
EC
Eric C.
Jul 6, 2026
Market Research - pricing
A real pleasure working with Vinicius. Clear, intelligent communication. Detail oriented and I can tell Veinicius takes pride in his work and wants to ensure you are happy with the results. The deliverables on this project exceeded my expectations both in turnaround time and format. I am already planning my next project with Vinicius.
BM
Brandon M.
Jul 5, 2026
Python Developer — Real Estate Market Intelligence Data Pipeline (Playwright + Apify + Airtable)
Vinicius was an absolute pleasure to work with. His communication was excellent every step of the way, and he made sure to communicate potential issues, and solutions before moving forward with any steps in order to ensure our project was built properly. Throughout the project he made several suggestions that not only made our project more efficient, but will help us in the long run as well. If you are looking for a developer, and want someone who will get exactly what you need and exceed every expectation then dont pass up on Vinicius. Also, he finished a project that many quoted weeks in less than a week, and didnt take any shortcuts. Incredible work. I look forward to bringing him more work, and will continue to use him for future projects!
PK
Paxley K.
Jul 3, 2026
Python Data Engineer for Data Cleansing
Vinicius delivered excellent work on a complex data-engineering project.
He built a reusable, profile-driven Python data cleansing framework with strong architecture, clear documentation, automated tests, and disciplined output controls. The project required careful handling of procurement, payment, supplier, government, and retail datasets, with strict boundaries around neutral data-quality signals and no fraud, legal, audit, or enforcement conclusions.
Vinicius understood the brief quickly, respected the architecture, communicated clearly, accepted detailed review feedback, and patched issues fast. He also added thoughtful improvements such as validator auto-discovery, captured-vs-authored guardrails, clean CLI behaviour, and a full pytest suite.
The final delivery was professional, well-structured, tested, and ready for the next productisation phase. I would confidently work with Vinicius again.
He built a reusable, profile-driven Python data cleansing framework with strong architecture, clear documentation, automated tests, and disciplined output controls. The project required careful handling of procurement, payment, supplier, government, and retail datasets, with strict boundaries around neutral data-quality signals and no fraud, legal, audit, or enforcement conclusions.
Vinicius understood the brief quickly, respected the architecture, communicated clearly, accepted detailed review feedback, and patched issues fast. He also added thoughtful improvements such as validator auto-discovery, captured-vs-authored guardrails, clean CLI behaviour, and a full pytest suite.
The final delivery was professional, well-structured, tested, and ready for the next productisation phase. I would confidently work with Vinicius again.
JL
Jordan L.
Jun 27, 2026
Excel Macro for Form Filling
He is great. His work was excellent. I will definitly be looking to him for future needs.
About Vinicius
AI Automation & Web Scraping Engineer | Python, LLMs, Data Extraction
100%
Job Success
Niteroi, Brazil - 3:27 am local time
Paid client work delivered on Upwork (names withheld):
- Regulatory and professional registers turned into verified, deduplicated datasets across multiple jurisdictions, thousands of rows, every field traceable to its source
- Healthcare provider and referral directories built to a verification standard the client audited row by row
- A paid signup flow over a phone line: Stripe checkout, signed webhooks, SMS credential delivery, built idempotent so a duplicate purchase never issues a duplicate account
- Market intelligence pipelines with cross-source corroboration and a scheduler, delivered with the QA brief
- Document and form extraction for medical billing
Production systems of my own, not demos:
- A B2B SaaS I built and run, used by more than 1,000 businesses
- A published iOS app on the App Store
- A verified local-services marketplace in pilot, running Claude in production for identity and listing review
Background: Mechanical Engineering at PUC-Rio, which shaped a systems approach. I think in inputs, transformations, validation, and observability, not ad-hoc scripts. Author of a free 84-page handbook on applied AI (LLMs, local models, RAG, agents, MCP, evals).
Public code, all on my linked GitHub:
- bedrock: NL-to-SQL agent plus a stability harness that runs each question K times against a defended answer key and fails CI when reliability regresses
- confidence-gate: the trust gate between LLM output and production. Scores confidence from external signals, never the model's self-report, and routes to auto-accept, human review, or abstain
- grounding-probe: ablates the retrieved evidence and re-runs the generator to prove a RAG answer used the context instead of just agreeing with it
- doc-eval: per-field precision and recall for document extraction, with a release gate that blocks regressions
- lead-quorum: distributed multi-agent qualifier on Google ADK and the A2A protocol. Two models read every lead independently as separate services, code scores it with a reason that provably sums to the number, and it abstains when they disagree. Deployed on Cloud Run
- scrape-sentinel and crosswatch: change detection and cross-source corroboration, the layer that keeps a scraper trustworthy over months
- Also: record-refinery, pdf-extract, mcp-listings, rag-quality, langgraph-production, token-ledger, make-failsafe
Security research: I also work in offensive security. A paid vulnerability bounty from Anthropic, two published GitHub security advisories (Directus and MCP Inspector), and a merged fix in the crun container runtime. It is why I read a system by asking what breaks it first.
Strongest at:
- AI agents and multi-agent systems with Claude and Google ADK/A2A: tool use, forced schemas, self-correction loops, cross-model corroboration, grounded answers
- LLM reliability and evaluation: golden sets, precision/recall gates, stability harnesses, confidence routing, human-in-the-loop queues
- MCP servers connecting Claude to your database, CRM, or internal API
- Web scraping including anti-bot handling (curl_cffi, patchright stealth browser, Cloudflare-protected directories)
- Document extraction: PDFs, invoices, forms, contracts via OCR plus LLM structured output
- Python backends (FastAPI), SQL data work, REST integrations, scheduled pipelines on Cloud Run and GitHub Actions
I use AI coding assistants deliberately. It is a methodology I have invested in, and it ships cleaner systems faster. Every line still goes through tests I wrote and a review I did.
I take on projects where clean, verifiable output matters more than the lowest bid. If that is what you are after, we will work well together.
Steps for completing your project
After purchasing the project, send requirements so Vinicius can start the project.
Delivery time starts when Vinicius receives requirements from you.
Vinicius works on your project following the steps below.
Revisions may occur after the delivery date.
Send file and columns
You send the raw CSV or Excel and we agree the exact fields and column order.
Normalize and validate
I shape every field to a consistent form and validate email, phone, and url.