Benchback: From parts shelf to paid back

An old alternator on a shop shelf can still be worth money. Repair shops pay refundable core deposits when buying remanufactured parts, but recovering them requires the right purchase, packaging, deadline, and supplier credit. Benchback connects those steps. A technician talks to the AssemblyAI Voice Agent API while holding the part. The agent checks job, invoice, purchase line and part identifiers, asks about condition, and reads the supplier policy attached to that purchase. It checks approved packaging alternatives and records reported evidence. Typed tools create an inspection draft; only the human can confirm the match, approve a return, or post a credit. The demo follows a fictional $240 deposit. A $200 credit leaves $40 unresolved. Only another credit or an explicitly accepted deduction closes the balance. Return packets, CSV imports, correction history and a credit ledger make this a working operations desk. Built by Shivam Gupta with React, TypeScript, Firebase Authentication, Firestore, Cloud Run and AssemblyAI. The voice stack uses short-lived tokens, audio streaming, interruption handling and server-validated tools. Transactions protect against duplicate credits and competing edits. The hardest lesson was keeping reported observations separate from verified financial evidence. The business hypothesis is $99/month for independent repair shops with an unreliable core-return process. Existing shop platforms already track cores; our proposed advantage is hands-busy capture and clear credit follow-up. Pricing and demand need customer validation. Next: timed shop pilots comparing voice with a short form, then supplier-specific imports. Test at benchback-ai.web.app: Sign in, Try a private guest workspace, then Load practice purchases. Open WO-418 and use voice or the form. All example companies and amounts are fictional. Benchback does not contact suppliers, buy shipping or guarantee refunds.