Impact intelligence before expanding payment automation

An agent analyzed an existing payment-recovery workflow before a market expansion, showing where automation reduced support load and where rollout risk remained.

99.99%+

reported reprocessing accuracy

Near-zero

payment rerouting support calls

Pre-rollout

market impact review

The challenge

Customers sometimes paid to the wrong account number, creating manual queues and call-center proof workflows.

A successful rerouting algorithm existed in one market, but the team needed evidence before releasing it elsewhere.

Manual analysis would have required code knowledge, historical payment behavior, Jira context, and database interpretation.

What Kognita ran

The agent traced the rerouting logic, including account-number similarity checks and historical payment behavior.

It paired code context with read-only data queries to compare the workflow across existing markets.

It surfaced gaps and rollout considerations as an impact report rather than a raw dashboard export.

Business outcome

The team could validate adoption and support impact before expanding the feature.

The agent turned an engineering-heavy investigation into a repeatable business intelligence workflow.

Feature decisions moved from anecdotal confidence to system-grounded rollout evidence.

Evidence basis

Built from a real enterprise deployment, anonymized for confidentiality.

The deck describes a payment-rerouting feature using account-number similarity and historical payment behavior, then notes that an impact report was run before release in another market. Exact client names, market names, private system identifiers, internal ticket IDs, and sensitive implementation details have been removed.

Source reference: Slide 9 from the internal case-study deck.