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ZVYA --- AI

A developer built ZVYA AI, a local-first business assistant that uses a locally hosted Ollama model to convert informal customer chat messages into structured orders for small sellers and home businesses. The Streamlit app shows extracted fields for human review before saving, persists confirmed orders in SQLite, and tracks manually recorded payments separately from advance amounts mentioned in messages. It requires no hosted AI API key and exports orders to CSV.

by read1 min views3 publishedOct 17, 2026

I built ZVYA AI, a local-first AI business assistant for small sellers, independent makers and home businesses. Many small businesses receive orders as informal chat messages, then manually copy product details, quantities, prices and delivery requests into notes or spreadsheets. ZVYA AI helps turn those messages into structured orders and keeps the seller in control of the final record.

A seller can paste a customer message, ask a local AI model to extract order details, review and edit the result, then save the confirmed order. The app also supports order status updates, manually recorded payments, outstanding balances, business summaries and CSV export.

GitHub: [https://github.com/ashandeepkaur27/ZVYA---AI.] The app does not need a hosted AI API key. In the default setup, the prompt is sent to the Ollama service on the same computer. The extracted fields are shown for review before the seller saves an order. This human-review step matters because AI can misunderstand prices, dates or quantities.

I also kept a distinction between an advance amount mentioned in a customer message and a payment that the seller has actually received. Only manually recorded receipts count toward the payment summary.

Small sellers deserve practical digital tools without needing a complicated subscription-based business platform. ZVYA AI explores how a local AI model can reduce repetitive order entry while keeping data and the final decision with the seller.

Building this project helped me connect a local language model to a working Streamlit interface, validate model output, persist records with SQLite, and think about the difference between AI-extracted information and verified business data.

Potential next improvements include customer follow-up message drafts, reminders for upcoming delivery dates, and more robust date parsing.

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