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Python Business Automation & Reporting Solutions :
Recurring operational tasks involved collecting and processing data, generating Excel or CSV reports, moving files, sending scheduled emails, and maintaining database records. I developed reusable Python workflows and scheduled jobs to execute these processes reliably
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A realtor or a business in need of python and LLM AI document processing? Here are some recaps of my previous internship I did.
Just completed some advanced level python and LLM from extern for a mortgage company.
Check out some of the project I've worked on
Engineered AI-powered document processing pipelines for 200+ page mortgage files using OCR technologies including Tesseract OCR, PaddleOCR, and PyMuPDF, enabling scalable document extraction and parsing.
Developed a Retrieval-Augmented Generation (RAG) system using LlamaIndex for intelligent document search, classification, and contextual retrieval workflows.
Improved retrieval accuracy and system performance through embedding optimization, chunking strategies, vector search tuning, and intelligent routing logic.
Delivered technical documentation, demo UI, and evaluation reports validating OCR accuracy, retrieval precision, and system readiness for production deployment.
github.com
GitHub - Kyl67899/Outamation_Externship_internship
Contribute to Kyl67899/Outamation_Externship_internship development by creating an account on GitHub.
The OCR plus RAG pipeline for 200-page mortgage files sounds like a serious test of retrieval quality. Which document type caused the toughest extraction or chunking issue?
Developed an AI-assisted lead qualification and conversion system that evaluates inbound leads, applies scoring logic, triggers automated responses, and routes opportunities into the appropriate pipeline path.
The system was designed to reduce response time, improve qualification consistency, and create a repeatable conversion process across multiple acquisition channels, including forms, messaging platforms, and CRM workflows.
Nice separation between scoring, response, and routing. For production, I’d retain the scoring inputs, rule/model version, final decision, and any manual override on the CRM record. That keeps routing explainable and makes re-scoring safer when the logic changes. How are you handling re-score history and overrides?
Automating my dropshipping growth reports
I recently worked on a project that automated my dropshipping growth reports using n8n, Google Sheets, and Groq AI. It's been a huge help, I no longer have to spend hours tracking my metrics manually
Follow for more: https://go.phonezait.de/
Combining n8n, Sheets and Groq for the analysis step is a smart way to keep this cheap to run compared to a full BI tool. How long did the workflow take to build end to end, and has it needed much maintenance since?
