Zippy — AI-native Accounts Receivable

The problem

Finance and Accounts Receivable teams do not lack data. They lack a trustworthy answer, every morning, to five questions:

1. What is outstanding?
2. What needs attention today?
3. Why has cash not been collected yet?
4. What action should happen next?
5. What money has been received but not yet correctly applied?

Zippy sits inside the Order-to-Cash lifecycle, focused on Accounts Receivable: collections, disputes, deductions, and cash application.

The operating model

Data → Policy / Intelligence → Work → Resolution → Metrics

On top of that sits an AI layer — Ask Zippy — designed as a natural-language interface over the AR data graph, not a generic chatbot:

• Ask — retrieve or analyse information (“Which customers should I chase today?”).
• Recommend — suggest the next best action, with reasons.
• Act — execute, subject to permissions, approval rules, and a decision and audit trail.

Questions like “Why hasn’t this customer paid?”, “What cash can we realistically expect by Friday?”, and “Which disputes are blocking the most cash?” only become safe to answer when the underlying data, policies, and provenance are in order. That is the part I build.