I, Malik Farooq, developed a full suite of 11 AI and MCP-based automation tools to simplify and secure large-scale Excel and CSV file operations. These tools were designed for business analytics, CRM datasets, and real-estate information systems, where merging, cleaning, mapping, and extracting data are daily challenges. Built entirely offline using Python, Pandas, NumPy, and Machine Learning logic, they ensure complete privacy while delivering enterprise-grade performance.
Each tool automates a different stage of the data-engineering pipeline—from merging bulk property records and client lists to mapping inconsistent headers and generating analytics-ready outputs. Together, they save hours of manual effort and bring AI-driven accuracy to everyday data workflows.
Key Highlights:
→ Offline and privacy-first architecture
→ AI and ML logic for smart data alignment and deduplication
→ Optimized for business, analytics, and real-estate domains
→ Integrated with RPA-style task automation
→ Built and maintained entirely by Malik Farooq
Description:
An advanced offline AI-based tool that merges multiple Excel and CSV files into one unified dataset while maintaining data quality and structure. It automatically detects headers, aligns mismatched columns, and removes duplicates.
Working:
Uses Python (Pandas, NumPy) for file scanning, header mapping, and data merging. It reads all files in a folder, aligns columns intelligently, merges them, validates data types, and exports a single clean file.
Key Features:
→ AI semantic column matching
→ Header validation and cleaning
→ Duplicate record detection
→ Unified structured output
→ Fast and privacy-safe merging
Future Improvements:
Will include online LLM-powered merging, data anomaly detection, and integration with cloud APIs.
Description:
A file automation tool that separates large combined files into multiple smaller datasets based on category, date, or user-defined conditions.
Working:
Written in Python using Pandas and OS libraries. It scans and splits large Excel or CSV files by rules such as region, client, or product type, ensuring faster analytics handling.
Key Features:
→ AI-based file categorization
→ Flexible conditional separation
→ Auto directory creation
→ Error-free data partitioning
→ Optimized for massive datasets
Future Improvements:
Will support cloud separation and RPA-driven scheduling for automatic file organization.
Description:
A smart mapping tool that aligns inconsistent column names between multiple datasets automatically.
Working:
Built in Python using fuzzy matching (fuzzywuzzy) and Pandas to detect similar headers like “Phone” and “Contact Number” and standardize them.
Key Features:
→ Intelligent header recognition
→ Machine learning-based mapping
→ Supports custom column dictionaries
→ Fast renaming and alignment
→ Improves merge accuracy
Future Improvements:
AI-driven column context learning and web-based mapping UI.
Description:
This AI-assisted tool automatically detects missing columns across multiple files and fills them with placeholders or calculated values for consistency.
Working:
Developed using Pandas and NumPy; it reads files, identifies missing columns, and aligns them with reference templates.
Key Features:
→ Template-based alignment
→ Smart placeholder generation
→ Data consistency enforcement
→ Works across Excel and CSV
→ Ideal for analytics preparation
Future Improvements:
AI-based column prediction and cloud automation for online dataset alignment.
Description:
An automation utility that collects and consolidates records based on filters or matching criteria from different datasets.
Working:
Uses Python’s Pandas query engine and filtering logic to extract relevant rows based on user-defined rules (e.g., all clients from a specific region).
Key Features:
→ Multi-file record extraction
→ Keyword-based filtering
→ Supports partial matches
→ Fast data collection engine
→ Offline and secure
Future Improvements:
Integration with AI query systems for natural language record collection.
Description:
A tool to extract target data from large Excel/CSV files using AI-based pattern detection.
Working:
Utilizes Python’s re (regex) and Pandas to identify and extract records containing key patterns or attributes (e.g., emails, IDs).
Key Features:
→ Pattern recognition
→ Regex + ML hybrid filtering
→ Batch extraction mode
→ Lightweight offline execution
→ Exports refined records only
Future Improvements:
Advanced NLP pattern extraction and API integration for live record lookup.
Description:
A smart data combiner that unites datasets of different formats (Excel, CSV) while preserving relational links.
Working:
Built using Pandas merge and join functions, with ML-powered structure detection for smooth integration.
Key Features:
→ Cross-format compatibility
→ Auto key mapping
→ Duplicate removal
→ Data normalization
→ Optimized export engine
Future Improvements:
Online relational mapping dashboard and SQL integration.
Description:
Automates the subtraction of records (e.g., removing duplicates or filtered data) between datasets.
Working:
Developed in Python using Pandas set operations. It compares two files and subtracts overlapping records efficiently.
Key Features:
→ Intelligent record comparison
→ Bulk file handling
→ Supports exact and fuzzy matches
→ Generates clean difference files
→ RPA-style task automation
Future Improvements:
Add version control tracking and AI-based data conflict resolution.
Description:
Designed for property or land data operations, this tool combines multiple data sources to build a unified land registry dataset.
Working:
Developed with Pandas and open data libraries, it standardizes property data and removes duplicate plots or coordinates.
Key Features:
→ Land record normalization
→ Address cleanup
→ Duplicate removal
→ GIS-ready dataset output
→ Works with CSV and Excel
Future Improvements:
Integration with maps API and AI-based property validation.
Description:
This AI-driven tool aggregates resident or demographic data from multiple sources for local analytics or reporting.
Working:
Built using Python, Pandas, and scikit-learn preprocessing for cleaning demographic fields and building structured outputs.
Key Features:
→ Auto data alignment
→ Demographic consistency checks
→ Duplicate cleanup
→ Secure local processing
→ Analytics-ready results
Future Improvements:
AI verification for demographic consistency and online dashboards.
Description:
A fast automation tool that merges, cleans, and validates large contact or lead lists for marketing and CRM operations.
Working:
Uses Pandas and ML-based deduplication for combining multiple lead lists. Performs regex-based phone/email validation and exports a clean final list.
Key Features:
→ Bulk merging and cleanup
→ AI-based validation
→ Duplicates removal
→ CRM-ready formatting
→ Private offline execution
Future Improvements:
LLM-assisted data enrichment and cloud lead scoring dashboard.
All these AI MCP Data Operation Tools were built offline by Malik Farooq to ensure privacy, data integrity, and automation efficiency. They combine AI, Python (Pandas, NumPy, Scikit-learn), and RPA-like workflows to simplify data engineering tasks across Excel and CSV files.
