You will get AI-Powered NLP Matching & Data Deduplication | Smart Entity Resolution
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Project details
Do you have two product databases and need to identify duplicates or equivalents, even if the names are written differently? This project uses advanced NLP (TD-IDF, Fuzzy Matching, Embeddings - depending on Tier) and Machine Learning techniques to intelligently match product names across datasets.
I’ll develop a Python script that analyzes text, measures similarity, and returns matched items with confidence scores. Perfect for cleaning, deduplicating, or merging product catalogs.
Deliverables include reusable code, a match report with scores, and the option to adapt it to new datasets. Save hours of manual work with a smart, automated solution.
I’ll develop a Python script that analyzes text, measures similarity, and returns matched items with confidence scores. Perfect for cleaning, deduplicating, or merging product catalogs.
Deliverables include reusable code, a match report with scores, and the option to adapt it to new datasets. Save hours of manual work with a smart, automated solution.
Machine Learning Tools
BERT, NLTK, Python Scikit-Learn, scikit-learn, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$45
|
Standard
$250
|
Advanced
$475
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 8 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 1 | 1 | 1 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $250
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About Santiago
Machine Learning Engineer | LLM Workflows, Data Pipelines, SQL, VBA
100%
Job Success
Buenos Aires, Argentina - 7:03 pm local time
Over the past 7+ years I’ve worked across machine learning, LLM workflows, data pipelines, and Excel VBA automation, building systems that connect models, databases, APIs, and business tools into practical solutions that run in real environments.
Many companies have valuable data but lack the infrastructure or automation to fully use it. My role is usually to design and implement systems that transform messy processes into reliable, scalable workflows.
Clients typically hire me to:
• Build or improve machine learning models
• Develop LLM-powered workflows and AI automation
• Design scalable data pipelines for analytics and ML
• Clean, structure, and analyze complex datasets
• Automate operational workflows and reporting
• Build advanced Excel VBA automation tools
Machine Learning & AI
I build predictive models that help businesses make better decisions from their data and automate parts of decision-making processes where appropriate.
Depending on the use case, these models can support human decisions or operate as part of automated workflows that detect patterns, generate predictions, and trigger actions. My goal is always to ensure these systems are reliable, interpretable, and safe to use in real operational environments.
My focus is always on models that deliver measurable value and can run reliably in production environments.
LLM Workflows & AI Automation
I design systems that use large language models to automate complex tasks involving text, documents, or decision processes.
Examples include automated classification systems, information extraction pipelines, document processing workflows, and AI agents that interact with APIs and databases.
The goal is always to turn LLMs into structured, reliable workflows rather than isolated prompts.
Data Pipelines & Data Engineering
Successful AI systems depend on solid data infrastructure.
I design and implement pipelines that ingest data from APIs, databases, and external sources, clean and transform it, and make it ready for analytics or machine learning.
This includes ETL pipelines, analytics datasets, and systems designed to handle growing volumes of data efficiently.
Excel VBA Automation
Many organizations still rely heavily on Excel for operational processes.
I build advanced VBA automation tools that eliminate repetitive work, streamline reporting workflows, and connect Excel with databases or APIs.
These solutions are often used by operations, finance, and analytics teams to replace manual processes and significantly reduce time spent on data-heavy tasks.
Example projects I’ve built include:
• Machine learning models predicting customer churn and business KPIs
• LLM workflows that classify and process thousands of documents automatically
• Data pipelines ingesting API data into analytics databases for reporting and modeling
• Automation systems replacing manual reporting workflows
• Excel VBA tools used to automate complex reporting and operational processes
I focus on solutions that are clean, reliable, and easy to maintain.
Most of my projects involve connecting advanced AI systems with real business infrastructure — databases, APIs, dashboards, Excel tools, and automated workflows.
In most cases these systems translate directly into measurable impact: reducing hours of manual work, automating operational decisions, improving forecasting accuracy, or enabling teams to act on data that previously wasn’t usable. The goal is always to turn data and automation into tangible business value: saving time, improving efficiency, and supporting better decisions at scale.
Tech stack I commonly work with:
Python, SQL, BigQuery, PostgreSQL, Pandas, Polars, machine learning frameworks, LLM APIs, ETL pipelines, and Excel VBA.
If you're looking for someone who can bridge machine learning, data engineering, automation, and Excel-based workflows, I’d be happy to discuss your project.
Steps for completing your project
After purchasing the project, send requirements so Santiago can start the project.
Delivery time starts when Santiago receives requirements from you.
Santiago works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis
Quick data analysis to understand what kind of data you have.
Data Cleaning (for Standard and Advanced Tiers only)
Clean both databases and extract different features from the products to make matching easier for the algorithm.