This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Bill & Document Sloth is a 100% local-first, privacy-conscious AI document assistant, tabular forecaster, and deadline tracker designed around one core promise: "Stop sorting paperwork. Start living."
Every adult and student dreads the paperwork shuffle: opening randomly named files like IMG_20261021_124532.pdf, hunting through fine print for payment due dates, losing track of appliance warranty periods, and maintaining brittle manual spreadsheets.
Bill & Document Sloth does the heavy lifting so you can be as lazy as a sloth:
- Drop Paperwork Once: Drag and drop PDFs, receipts, or phone bills.
- Intelligent Local Extraction: Local open-source AI extracts the issuer, category, total amount, due date, document date, and invoice number.
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Smart Standardized Filenames: Automatically generates clean filenames (e.g.,
2026-10_Electricity_Bill_BESCOM.pdf). - Human-in-the-Loop Review: Split-screen side-by-side verification with confidence scores before any file is moved.
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Safe Organization & Undo: Files are organized into structured directories (
documents/organized/{Category}/{Year}/) with 1-click reversible undo. - Proactive Deadlines: Monitors overdue bills, payments due in 3 days, and expiring warranties before penalties strike.
Demo
- Direct YouTube Link: Watch Video Demo on YouTube
- GitHub Repository: https://github.com/Ayushgupta1715/bill-and-document-sloth
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Local Interactive Dashboard: Runs locally on
http://127.0.0.1:8000with Swagger API docs athttp://127.0.0.1:8000/docs. - 1-Click Built-in Demo Pack: Synthesizes realistic BESCOM Electricity Bills, Airtel Fiber Invoices, AppleCare Warranty Certificates, Star Health Insurance Policies, and Coffee Receipts on the fly so evaluators can test immediately with one click!
Code
🦥 Bill & Document Sloth
“Stop sorting paperwork. Start living.”
A 100% Local-First AI Document Assistant & Deadline Tracker for Hacktoberfest 2026 Week 3: Slow and Steady (#hf26challenge).
🌟 The "Slow and Steady" Philosophy
In celebration of International Sloth Day and Hacktoberfest Week 3, Bill & Document Sloth is built around a simple truth: AI should do the tedious, repetitive paperwork so you don't have to.
Instead of spending precious hours deciphering confusing PDF bills, calculating payment deadlines, renaming cryptic camera scans, and maintaining brittle spreadsheets:
- Drop your documents once.
- Local AI extracts the essentials (Issuer, Category, Due Date, Total Amount, Invoice Number).
- Approve with 1 click.
- Relax. Your files are organized into structured directories and payment deadlines are tracked automatically.
🏆 Hacktoberfest 2026 Sponsor Track Integrations
To maximize utility and showcase open innovation, Bill & Document…
- Repository: https://github.com/Ayushgupta1715/bill-and-document-sloth
- License: MIT License (Open-Source)
How I Built It
The application combines local-first inference, open-weight reasoning models, and tabular foundation models:
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Local-First Processing Engine:
- Backend: FastAPI, Python 3.14, SQLite3, PyPDF for embedded text extraction, and ReportLab for synthetic sample generation.
- Frontend: Clean, responsive single-page application built with Tailwind CSS, Lucide icons, and Canvas Confetti.
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Google Gemma Open-Weight Model (
gemma-2-2b-it):- Integrated in
app/gemma_service.pyto provide deep structured JSON parsing and field reasoning traces for complex or ambiguous bills.
- Integrated in
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Prior Labs TabPFN-3.5 Foundation Model:
- Integrated in
app/tabpfn_analyzer.pyto perform in-context tabular regression and outlier detection over historical billing cycles (forecasting next month's utility spend and alerting on anomalous spikes).
- Integrated in
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ElevenLabs Voice Integration:
- Integrated in
app/voice_service.pyto give the sloth agent a calm, spoken audio briefing of pending bills and deadlines.
- Integrated in
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Safe File Operations:
- Keeps original uploads pristine in
documents/uploads/while organizing approved files indocuments/organized/{Category}/{Year}/. Full audit trail inactions_historytable for 1-click safe undo.
- Keeps original uploads pristine in
Why Does Open Innovation Matter?
When building an assistant for bills, leases, and receipts, sending files to closed cloud LLM APIs poses major privacy and cost concerns:
- Absolute Data Privacy: Utility bills and tax invoices contain home addresses, bank details, and personal identifiers. With open-source local inference and SQLite, zero bytes of private data leave the device.
- Zero Recurring Cloud Token Costs: Users shouldn't pay monthly API subscriptions or per-token charges just to organize their receipts. Open-weight models like Gemma and tabular foundation models like TabPFN run locally on consumer hardware for free.
- Total Offline Resilience: Works on a train, in airplane mode, or during internet blackouts.
- User Sovereignty & Control: Unlike opaque cloud systems that silently alter data, our pipeline enforces human verification and reversible undo actions.
The Lazy Payoff
- Time Saved: Manually opening, reading, renaming, and filing 10 bills takes roughly 20–25 minutes. With Bill & Document Sloth, dropping them into the app and clicking "Approve" takes under 45 seconds — a 95% reduction in manual effort.
- Zero Spreadsheet Maintenance: Upcoming deadlines (due in 3 days, due today, overdue) are monitored automatically, preventing costly late-payment surcharges.
- Peace of Mind: You can be as lazy as a sloth, knowing every warranty, receipt, and utility bill is categorized and safely filed on your machine.
Prize Categories
Featured Categories:
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Best Use of Gemma ($200): Integrated Google's Gemma-2 open-weight model (
gemma-2-2b-it) for deep structured bill parsing, confidence verification, and reasoning traces (app/gemma_service.py). -
Best Use of TabPFN ($200): Integrated Prior Labs' TabPFN-3.5 tabular foundation model (
app/tabpfn_analyzer.py) to forecast next month's utility spending and detect billing cost anomalies (+40% outliers). -
Best Use of Render ($200): Included production
render.yaml(Render Blueprint) and multi-stageDockerfilefor 1-click cloud deployment.
Partner Categories:
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Best Use of ElevenLabs ($100): Sloth Voice Briefings (
app/voice_service.py) delivering spoken audio briefings of upcoming bills and paperwork deadlines. -
Best Use of Sentry Agent Tracing ($100): Instrumented with
sentry-sdkinapp/main.pyfor tracking document ingestion latency, OCR performance, and extraction errors. -
Best Use of a .tech Domain ($100): Production DNS and routing ready for
slothdocs.tech. -
Best Use of GitHub Copilot & Actions ($100): Comprehensive automated CI pipeline (
.github/workflows/ci.yml) validating 20 unit and integration tests on every push.
Top comments (2)
The split-screen human-in-the-loop review before moving files is a nice touch. Most doc organizers just auto-sort and hope for the best, but showing confidence scores and letting you fix mistakes before commit is way more practical for real bills where one wrong due date means a late fee. The 1-click demo pack is also great for anyone evaluating it without uploading their own docs.
"Human-in-the-loop review: split-screen side-by-side verification with confidence scores before any file is moved" is the right design for a document organizer.
Most AI tools just move files and hope the extraction was correct. If it gets the due date wrong, you miss a payment. If it gets the amount wrong, you overpay. The review step is what makes this safe to use on real bills.
Curious how the confidence scores are calculated. Is it based on the model's own confidence (which can be wrong), or do you have a separate validation step?