AromVision
Quality you can see. Decisions you can trust.
Problem statement
Sima Arome is flying blind: manual QC relies on human eyes, operators re-enter data three times, lot histories live in chats instead of systems, and there is no audit trail for quality decisions.
Use case & business challenge
Operators, managers, and admins use role-based dashboards to capture batches, run YOLO-based grading, review AI rationale, approve or reject results, receive notifications, and audit every decision.
Proposed solution
A three-role, end-to-end AI quality control platform that replaces manual inspection and fragmented spreadsheets with real-time batch grading, database-enforced immutable audit trails, and RBAC-controlled operations.
What's next
Start with mango grading for one commodity at one site, expand to multi-commodity spice and grain grading with mobile capture in Q3 2026, then build a marketplace model for third-party QC providers and future edge deployment for offline factory-floor grading.
Screenshots & visual references
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