End Close
End Close is a payment reconciliation platform that automates the matching of financial transactions for high-volume fintechs, marketplaces, and banks. It combines rules-based matching with AI agents that investigate and resolve exceptions in real time, achieving 99.5% automation from day one. The platform ingests data from sources like Stripe, Snowflake, and bank feeds, and provides a complete audit trail for every action.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Financial Technology
- Software Only
Funding
Founders
Product
Problem
High-volume payment companies struggle with manual reconciliation processes that are time-consuming, error-prone, and require significant engineering and operations resources. Existing solutions often fail to handle the complexity of real-world financial data, leading to unresolved exceptions, delayed financial closes, and a lack of trust in the numbers.
Solution
End Close provides an end-to-end automated reconciliation platform that ingests financial data from multiple sources, normalizes it, and matches transactions using exact, fuzzy, and many-to-many logic with tolerances for FX and rounding. The platform uses AI agents to investigate and resolve straightforward exceptions automatically, while maintaining a complete human-review trail for all actions. Every action is logged and auditable, providing finance and engineering teams with numbers they can trust and an audit trail that is always ready. The platform is designed to be live in weeks, not quarters, and scales with transaction volume without requiring additional engineering effort.
Target Audience
Primary customers are fintechs, marketplaces, and banks that process high volumes of payments and need to automate reconciliation workflows, reduce manual exception handling, and maintain a reliable audit trail.
Features
- Ingest data from a wide range of sources including Stripe, Snowflake, BigQuery, Postgres, Google Sheets, Databricks, Hyperwallet, NACHA, bank feeds, CSV, and API
- Automated matching with exact, fuzzy, and many-to-many logic, including tolerances for FX, rounding, and other real-world data inconsistencies
- AI agents that resolve straightforward exceptions on their own, with a human-review trail for transparency and control
- 99.5% transaction automation from day one, reducing exception load by up to 99%
- Full audit trail with every action logged, supporting SOC 2 compliance and audit-ready exports
- Role-based access control (RBAC) and integration with identity management providers for secure access