AI-Driven Payment Settlement

A multinational manufacturing-solutions company engaged Taller to replace its legacy batch settlement process with a real-time, state-machine-driven workflow.

The client’s legacy settlement process was batch-oriented, where payments were processed on a schedule and their status was visible only after the whole batch finished. For a global manufacturer’s customer base, that lack of real-time visibility carried a real operational cost. Every customer-service inquiry about a payment had to wait for the next batch, and every disputed settlement had to be reconciled against a snapshot of the world that was already out of date. The client engaged Taller to replace that batch process with something real-time and reliable.

Taller built the state-machine settlement workflow that replaced the batch process (a state machine models a payment as it moves step by step through clearly defined stages), integrated the risk-evaluation system that scored transactions automatically before they reached human review, and supported the backend re-platforming with database redesign, API development, and deployment updates. The team built the workflow on the Temporal framework, chosen for its durability: a payment-settlement workflow had to survive an upstream API timeout, a downstream bank failure, or even a mid-flight code deployment without losing its place, and Temporal preserved that state across all of them. Java and Spring provided the service-tier code the workflow coordinated, MySQL held the durable state Temporal relied on, REST endpoints exposed the workflow to internal systems, and GitHub Actions handled the automated build-and-deployment pipeline.