AI Revenue Recovery Agent

The Operational Bottleneck

Each dispute involved a repetitive chain of decisions: identify the transaction, determine eligibility, select the appropriate response, submit the claim, monitor the result, and update internal records.

At small volumes this could be handled manually. At thousands of restaurant locations, the same operating model would require substantial additional headcount.

The system also needed to recognize when automation should stop and a human should take over.

Agent Design

SF AI Labs structured the agent around the full recovery lifecycle:

Identify → Evaluate → Prepare → Execute → Monitor → Learn

Rather than automate isolated tasks, the architecture gave the agent responsibility for coordinating the workflow while routing exceptions and sensitive actions to human operators.

Authorized platform integrations were prioritized as the long-term execution layer for operating at scale.

Agent Capabilities

• Identify, classify, and prioritize eligible disputes

• Prepare evidence and appropriate dispute responses

• Execute approved workflow actions through platform integrations

• Escalate exceptions and sensitive cases to human reviewers

• Track outcomes and learn which recovery strategies perform best

How We Built the Roadmap

• Mapped each decision and handoff in the existing recovery process

• Converted operational rules into agent tools and decision logic

• Designed exception handling and human approval workflows

• Assessed integration and platform-access requirements

• Defined analytics for agent actions, recoveries, and outcomes

Expected Impact

• Converted a multi-step manual workflow into a coordinated agent architecture

• Designed human oversight around exceptions rather than every transaction

• Created a scalable model for operating across thousands of locations

• Established an outcome feedback loop for improving recovery decisions

• Modeled reducing a ~40-minute monthly workflow per location to ~30 seconds of automated processing

The time reduction is a modeled automation target from the strategy work rather than a measured production result.

Commercial Value

The agent creates a path to increasing recovery volume without increasing operations headcount at the same rate.

Human teams can shift from repetitive execution toward supervising exceptions, while the agent handles the repeatable workflow.

Over time, each resolved dispute also contributes to a proprietary intelligence layer—showing which dispute types, evidence, and response strategies generate stronger recovery outcomes.

Why SF AI Labs

SF AI Labs builds AI agents around measurable business outcomes rather than isolated model interactions.

For this engagement, that meant combining reasoning, deterministic workflows, integrations, human oversight, and analytics into one system designed around a clear objective: recovering revenue at scale.