RobotiFAI provides an AI‑driven procure‑to‑pay platform that automates invoice capture, data extraction, posting, and reconciliation directly into ERP and accounting systems. The solution runs continuously, reduces processing time and errors, and delivers real‑time dashboards and audit trails, allowing finance teams to focus on analysis rather than manual data entry.
Funding
Funding not disclosed
Founders
Product
Problem
Finance teams spend a large portion of their time on manual procure‑to‑pay tasks such as invoice data entry, posting, and reconciliation, leading to slow processing, high error rates, and limited capacity for strategic analysis.
Solution
RobotiFAI offers an AI‑driven procure‑to‑pay platform that automates the entire invoice workflow from capture to posting and reconciliation. The system continuously processes documents, extracting data with machine‑learning models and posting entries without human intervention, achieving near‑zero errors. By operating 24/7, it dramatically reduces processing time and eliminates the need for manual data entry. The platform integrates with existing ERP and accounting systems, providing real‑time updates and audit‑ready records. This automation frees finance staff to focus on analysis, reporting, and value‑adding activities rather than repetitive tasks.
Target Audience
Primary customers are finance and accounting departments of mid‑size to large enterprises that manage high volumes of procure‑to‑pay transactions and seek to reduce manual effort and errors.
Features
- AI-powered document capture and data extraction for invoices, purchase orders, and receipts
- Automated posting and reconciliation directly into ERP/accounting systems via native connectors
- Continuous 24/7 processing engine that handles high‑volume document streams without downtime
- Built‑in validation rules and error‑prevention logic to achieve near‑zero posting errors
- Real‑time dashboards and audit trails for full visibility into transaction status
- Scalable architecture that supports enterprise‑level document volumes