Umaima Iqbal
I build offline AI tools that make documents talk.
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Umaima is ready for their next project!
AuraExtract — Intelligent Invoice & Receipt Data Extractor
The extraction engine uses intelligent regex pattern matching that handles real-world invoice layouts — column-per-line PDF formats, inline tabular formats, and plain text documents. It detects 10 fields automatically and parses up to 20 line items per invoice.
Supports PDF, TXT, and DOCX formats. Includes a raw text preview panel so users can verify exactly what the engine is reading. CSV export includes both the summary fields and full line items table — ready to open directly in Excel.
Pure Python. Zero external dependencies beyond pypdf for PDF reading.
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AuraSort scans any folder and automatically sorts files into named subfolders by type — Documents, Images, Videos, Audio, Code, Archives, and more.
Files are renamed to clean, consistent lowercase format. Every operation is logged live on screen as it happens.
Built with a Dry Run mode so users can preview exactly what will move before anything is touched.
Full undo restores every file to its original location with one click. An HTML report is generated after each sort showing every file moved, every category created, and total time taken.
Pure Python. Zero external libraries. Works on any machine without installation.
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A fully offline document summarizer built in pure Python. Uses TF-IDF scoring, position weighting, and Jaccard deduplication to extract the most important sentences from any PDF, DOCX, or TXT file — each labeled with a relevance percentage.
The result looks like this:
[1] [100% relevance] The algorithm achieved 94% accuracy on benchmark tests.
[2] [81% relevance] Training was performed on 50,000 labeled samples.
[3] [67% relevance] Results were validated using 5-fold cross validation.
Supports PDF, Word, and TXT files. Saves summaries to your computer. Runs completely offline. No subscriptions, no API keys, no internet required.
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Built AuraChat v3.0 — a fully offline Document Intelligence desktop app in pure Python. Users upload any PDF, Word, or TXT file and ask questions in plain English. The system returns cited answers with confidence scores instantly.
Technical highlights:
— Custom NLP engine using TF-IDF scoring + hybrid token overlap analysis
— 1,700× faster indexing than baseline on 500-page documents
— Multi-threaded processing — UI never freezes during heavy indexing
— Supports PDF, DOCX, and TXT file formats
— Zero external APIs — runs completely offline on the user's machine
— 23 production-grade bugs identified and resolved before delivery
This is not a demo. This is production-ready software built with clean architecture, full error handling, keyboard shortcuts, chat export, source citations, and confidence indicators
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