Spaces:
Sleeping
Sleeping
| # VoiceLedger Field Notes | |
| ## Problem | |
| VoiceLedger is built for an informal seller who records business activity in short spoken notes instead of spreadsheets. The recurring jobs are simple but easy to lose track of during a busy day: sales, expenses, customer dues, payments, and stock. | |
| The anonymized target user is a local seller who needs to answer practical questions quickly: | |
| - What did I sell today? | |
| - Who still owes me money? | |
| - How much stock do I have left? | |
| - Did I make a profit today? | |
| - Can I share a short summary without opening a spreadsheet? | |
| ## Small-Model Fit | |
| The app intentionally keeps the bookkeeping workflow narrow. Voice and messy text are converted into a small transaction schema, then deterministic ledger code handles balances, inventory, reports, and exports. | |
| NVIDIA Nemotron is used through the Modal backend for transaction parsing when available. The local rule parser remains the fallback so the app stays reliable during a live demo or on a basic Hugging Face Space. | |
| This is a good small-model problem because the output space is constrained: a transaction type, item, quantity, price, amount, customer, payment status, notes, and confidence. The model does not need to run a general accounting system; it only has to normalize everyday seller notes into structured records. | |
| ## System Shape | |
| - Gradio Space for the product UI. | |
| - SQLite for local ledger persistence. | |
| - faster-whisper for speech-to-text. | |
| - Modal endpoint for speech and Nemotron parsing. | |
| - Rule parser fallback for deterministic demo safety. | |
| - Customer and inventory tables are derived from saved transactions and rebuilt after edits/deletes. | |
| ## Demo Flow | |
| 1. Seed demo transactions from the first-screen Judge Demo Flow. | |
| 2. Record or type: `Sold 12 mangoes, 20 each`. | |
| 3. Show the parse source, review card, confidence, and warning badges. | |
| 4. Show price memory when a known item is missing price, then correct one inline review field before saving to show that sellers can fix model or rule-parser mistakes immediately. | |
| 5. Save the transaction and show the receipt with bookkeeping side effects. | |
| 6. Show Dashboard totals, the Insight Coach, sales/expense timeline, Ledger row, Customer Credit detail, and Inventory detail. | |
| 7. Open Field Test to show the seller checklist and anonymized feedback notes. | |
| 8. Edit or delete a transaction and show balances rebuilding. | |
| 9. Run Daily Closeout, then export CSV, PDF report, and WhatsApp daily summary. | |
| 10. Open Demo Health to show Modal, backend version, NVIDIA Nemotron parser status, SQLite, PDF, and endpoint checks. | |
| 11. Open Submission Story to show the visual AI pipeline and why the constrained transaction schema fits small models. | |
| ## Submission Freeze | |
| The hackathon version is intentionally frozen around the complete bookkeeping loop rather than adding more features: | |
| - Voice and text capture. | |
| - Modal/Nemotron parsing with local rule fallback. | |
| - Seller setup for business name, currency, low-stock threshold, and language style. | |
| - Currency presets and translated WhatsApp summaries for sellers who demo in INR, USD, EUR, GBP, MXN, BRL, English, Spanish, French, or Portuguese contexts. | |
| - Hinglish/Hindi-lite/Gujarati-lite/Spanish/French/Portuguese fallback examples for common seller notes. | |
| - Guided Judge Mode, first-run onboarding, multilingual examples, language/confidence chips, price memory, human-friendly review cards, inline review correction, mistake logging, warning badges, receipts, and save feedback. | |
| - Command Center, dashboard timeline, seller-day timeline, debt reminder queue, customer follow-up, inventory reorder intelligence, ledger correction, Daily Closeout, PDF, CSV, and WhatsApp exports. | |
| - Field Test Mode for the seller checklist and anonymized “who/tried/changed” feedback evidence. | |
| - Richer Field Test Evidence for pain point, before/after workflow, useful moments, and changed-after-feedback notes. | |
| - Demo Health for backend observability. | |
| - Submission Story for the AI pipeline and small-model fit rationale. | |
| This freeze keeps the demo reliable and makes the value proposition easier to judge. | |
| ## What We Learned | |
| The strongest product behavior is not the model call itself. It is the reliable loop around it: voice input, readable review, price memory, warning badges, save receipt, correction, reminders, restocking, and export. Informal sellers need speed, but they also need a way to spot mistakes and fix them. The correction log makes those mistakes measurable during field tests, while edit/delete, detail drilldowns, Daily Closeout, and CSV export make the app credible as a bookkeeping tool instead of a parser demo. | |
| The small-model constraint helped keep the app honest. The model handles the fuzzy human input; the code owns the accounting state. | |
| ## Screenshot Moments | |
| - Record flow: Guided Judge Mode, first-run onboarding, multilingual examples, language/confidence chip, voice command shortcuts, price memory, review card, inline corrections, warning badges, source/status, save receipt. | |
| - Dashboard: daily totals, Insight Coach, outstanding credit, sales/expense timeline, top seller, low-stock inventory. | |
| - Field Test: workflow checklist, correction log, and anonymized feedback evidence. | |
| - Reports and exports: Daily Closeout, PDF download, WhatsApp summary, CSV ledger export. | |