Four open-source repos put Jev into business queues
Four demo repos for sales, support, document classification and bookkeeping show Jev handling routing, urgency scoring and document-type checks.
GIVE JEV THE JOB EVERY BUSINESS OWNER HATES: “SORT THIS SH*T BEFORE IT REACHES ME.” A demo request lands in the support inbox. A billing complaint gets forwarded twice. Another PDF needs someone to open it just to work out what it is. The work starts before anyone actually solves the problem. Four public repos put Jev into exactly those decisions. Each tackles a specific queue you can inspect, replay and test: → SALES / jev-playground Lead routing, deal risk, reply triage and duplicate-account checks. Pick the experiment that matches the mess in your CRM. https://github.com/deepdave98/jev-playground → SUPPORT / jev-demo-router Choose a department, judge urgency and score negative tone. Uncertain department routing goes to a human queue. Assigning a ticket is a different permission from issuing a refund. https://github.com/Zafer-Liu/jev-demo-router → DOCUMENTS / jev-doc-classification Extract text from financial PDFs and check document types. An invoice marked PAID can also look like a receipt, so the independent type checks can both score highly. A scanned file with no extracted text produces an error; it needs OCR first. https://github.com/lucassarcanjo/jev-doc-classification → BOOKKEEPING / jev-clerk A desktop experiment: Jev chooses the next action, a separate model updates the playbook, and an independent check inspects the saved accounting record. Clicking Save only counts when the supplier, date and amount actually match. https://github.com/stas4000/jev-clerk The useful part is how small the question becomes. Give it the current case and the permitted choices: SALES / SUPPORT / REVIEW. INVOICE / RECEIPT / OTHER. Code handles the route or the desktop action. Missing context gets its own path instead of being quietly guessed away. These are demos and experiments, with API costs and setup work. The attached clip is my scripted illustration of the pattern, not a live API run. A confidence threshold is a routing policy; it does not prove an answer is correct. Start with one queue and old cases you already know the answers to. Count wrong routes and missed escalations. For a workflow that changes records, check what was actually saved. That tells you whether automation is removing work or creating a second queue of mistakes. My Jev article below explains the decision layer behind this. Which queue would you test first: sales, support or paperwork?
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