Chargezoom AVA.

Small businesses wait weeks to get paid.

For a small business, every extra day an invoice sits unpaid is working capital that can't make payroll, refill inventory, or fund the next hire. DSO — days from invoice to payment — was Chargezoom's headline metric, and the existing playbook for moving it was manual: someone (often the founder) chasing invoices by hand, or a generic dunning sequence firing the same nudge at every customer regardless of history or relationship.

The opening: pair the platform's own A/R data — every invoice, payment, and aging snapshot — with an AI layer that could read context, decide cadence, and personalize tone per customer. Tone personalization was the part that couldn't be validated in theory. If you can't run it on real data, you don't actually know if it works — so we ran it on real data, in an MCP sandbox, before anything shipped.

The four pieces that earned their place.

AVA grew one validated piece at a time. Scope narrow. Ship to the beta customer. Let the dollars decide what to build next. Four months of that loop produced one of the strongest product-market fit signals of my career.

I took AVA from rough idea to working prototype: wrote the specs, defined the prompts and guardrails, worked with engineering on the data model, worked with design on the surfaces, and ran validation with the beta customer directly. The first version was deliberately the smallest thing that could give us a real signal.

$2.7M recovered. DSO halved. The customer kept asking for more.

The numbers landed. The deeper signal was behavioral: 51% of overdue invoices got paid within a week of AVA's first email, and the customer's finance team — previously buried in manual follow-up — moved to forecasting, analysis, and strategic work. Collections didn't go away. It just stopped being a person's job.