Your AI Says the Refund Is Done. Where Is the Money?

A convincing reply can hide unfinished work. Your support history may hold the evidence AI evaluation needs.

A finished conversation can conceal unfinished work.

Imagine receiving a chatbot message that says, “Your refund of $84.99 has been processed.” Three days later, the customer is still waiting, and the payment system shows no refund.

The response sounded complete, but the actual business task was never carried out.

A company’s customer support history can help identify this gap. SourceX offers eligible businesses the opportunity to earn up to $4 million by licensing suitable records for AI training and evaluation. Actual offers depend on eligibility, data rights, quality and buyer demand, so not every collection qualifies.

Check the Action Behind the Answer

The original 2024 tau-bench study evaluated tool-using AI agents by checking the resulting database states. Its tested function-calling agents, including GPT-4o, completed fewer than half of the tasks. These findings reflect historical results rather than the performance of current AI systems.

The underlying principle remains important. When an AI agent handles a refund request, the task is not complete simply because it generates a convincing response. The correct payment must actually be processed according to the applicable policy.

Businesses evaluating AI systems should therefore examine the actions taken and the resulting records, rather than relying solely on the wording of a conversation.

Keep the Awkward Exceptions

Real customer service requests do not always follow a straightforward path. An existing refund may need to be checked before another payment is issued. A missing approval could require a supervisor's involvement, while a failed payment tool should lead to an honest explanation of the problem.

Connected customer support, policy and transaction records can help explain these different outcomes. For effective AI evaluation, it is important to preserve what was known before a decision separately from the final result used to assess the system.

Missing information and failed attempts should also be documented instead of being removed or rewritten to create a more convenient outcome.

These operational details can be valuable when assessing a dataset. A large archive of support tickets may appear useful, but its real value depends on whether it captures the decisions, exceptions and outcomes that show what actually happened.

Give the History a Commercial Review

Businesses with substantial customer support or service records may have an opportunity to explore the commercial value of their data.

SourceX begins its initial suitability assessment using company information rather than requiring businesses to upload raw records immediately. Suitable suppliers generally have reached at least 50 full-time employees at their peak, maintained several years of historical records and met the relevant location and data-rights requirements.

For approved collections, SourceX manages the agreements, data preparation and payment process. The supplier retains responsibility for approving the scope and authorising the release of the records.

Standard terms include a one-time payment and may request exclusivity for AI training. Businesses should carefully review these conditions before signing an agreement, particularly when considering how licensing could affect future uses of their data.

The assessment should consider not only the quantity of available records but also their quality, completeness, permissions and potential usefulness for AI development.

Businesses should also ensure that any information shared or licensed is handled in accordance with applicable privacy obligations and contractual requirements.

Start With Records That Prove the Result

Does your business have several years of customer support or service records? It may be worth checking whether your company qualifies to work with SourceX.

Begin with a specific case where the underlying system can establish whether the work was completed. For example, a refund request should include enough information to distinguish between a message promising payment and a transaction record confirming that the refund actually occurred.

This approach can help businesses understand the practical value of their historical records while supporting more reliable AI training and evaluation. For context on how AI is changing information workflows, read How Artificial Intelligence Is Rewriting Search Engine Visibility.

About the Founder

Noah Loul is the founder of SourceX, a company that helps businesses monetise their proprietary data for AI training and development. His ambition is to build the world's largest data infrastructure, supporting the development of today's artificial intelligence systems and future superintelligence.