ARPilot - 2026 Pricing, Features, Reviews & Alternatives

ARPilot is an AI powered accounts receivable automation platform that manages the complete invoice collection cycle from follow up through payment reconciliation to installment planning. The solution addresses the needs of small businesses processing fewer than one hundred invoices each month and mid-market organizations with finance teams handling larger invoice volumes under formal processes. Integration options include a direct connection to QuickBooks Online for basic operations and a REST API with webhooks for seamless incorporation into existing enterprise systems. The platform automates follow-up reminders, payment processing, matching of bank deposits to invoices, dispute resolution, and administration of installment plans while learning from historical data to optimize future collections.

The AI engine generates individualized follow-up messages that consider customer profile, invoice amount, payment terms, and aging status instead of relying on generic templates. Message tone adjusts based on how far an invoice is due and scheduling follows configurable cadence rules with previews and editing capabilities before dispatch. Communications can escalate across email, SMS, and voice channels to reach the appropriate customer contact. Each message includes a branded payment portal offering credit card and bank transfer options without requiring login credentials. A scoring algorithm evaluates payment deposits against invoices using amount, payer name, reference data, and transaction date. Matches exceeding a high confidence threshold apply automatically. Matches meeting a moderate confidence threshold route to a review queue. Matches below a low confidence threshold remain unmatched to avoid errors.

When full payment is not possible, customers self-enroll in installment plans via the same payment portal and recurring charges are processed through the integrated payment processor. The system supports optional financing fees, early settlement incentives, and predictive modeling to identify invoices best suited for payment plans and the most effective schedule for each customer. Dispute management allows customers to flag issues from the payment portal or automated call menus, pausing collection activity until resolution. The analytics framework offers transparent formulas for days sales outstanding, collection efficiency, bad debt rates, aging analysis, and engagement metrics that distinguish human interactions from automated activity. Risk assessment tools tier invoices by engagement patterns, model dispute probability, and flag accounts approaching collection failure while intervention remains viable.

A continuous improvement engine analyzes tenant-specific historical data on response timing, send schedules, message performance, and channel preferences, then generates workflow recommendations with accompanying confidence scores, supporting data, and explanatory rationale. Recommendations remain pending until manually approved, ensuring controlled adjustments to process workflows. Security controls include database-level row based access controls, multi-factor authentication on sensitive actions, and audited read-only support access. All analytical formulas and scoring methodologies are published for verification. The tenant-isolated data model guarantees that machine learning insights derive exclusively from each organization’s own data.