AI systems

Faster Customer Resolutions with RAG

RAG, or retrieval-augmented generation, finds relevant passages in approved policies before AI drafts a cited summary. Combined with current customer records, fixed rules and adviser approval, it helps routine cases move faster without giving up control.

RAG Use Case
A RAG use case for customer operations teams.

The expensive part of a customer problem is often not the decision. It is finding every piece of information required to make that decision confidently.

The problem

Customer service becomes detective work

A simple refund can require an adviser to search an order system, payment records, old emails and a policy document. The customer waits while the adviser reconstructs the history and works out what they are allowed to do.

The same pattern appears in complaints, returns, account changes and policy exceptions. It slows resolution, creates inconsistent decisions and makes experienced staff the only people confident enough to handle unusual cases.

How we would build it

One system that assembles the case, finds the policy and completes the action

We would build a case service connected to the client’s existing order, payment and support systems through their APIs, the controlled software connections systems use to exchange data. When an adviser entered a customer name or order number, it would pull the current records directly from those systems and assemble them into one timeline.

For policies and internal guidance, the assistant would use RAG. It would first find passages relevant to the case, then use that evidence to draft a concise case summary and rationale with links to the original documents. A rules engine, a set of fixed business rules such as refund windows, value limits and approval thresholds, would combine the policy evidence with the customer’s records to recommend a resolution, flag exceptions and identify cases that needed additional approval. The AI would assemble and explain the evidence; it could not approve or move money.

The adviser would review the complete case before anything happened. Once approved, the system would send the refund through the client’s existing payment service and write the decision, policy evidence and payment result back to the customer record, creating a complete audit trail.

How it works From scattered customer information to a controlled resolution

Live records are assembled into one case. RAG finds the relevant policy and drafts a cited explanation. Fixed rules and an adviser control the refund.

The case service pulls live customer facts from the client’s systems. Separately, RAG finds relevant passages in approved policy documents and drafts a cited explanation. Fixed rules check the case before an adviser approves the action. The client backend then processes the refund and updates the customer record. Customer data Decision and action Policy RAG Customer systems Assemble the case Check rules + exceptions Adviser approves Client backend Approved policies Cited policy summary Payment provider Customer record The case service pulls live customer facts from the client’s systems. Separately, RAG finds relevant passages in approved policies and drafts a cited explanation. Fixed rules check the case before an adviser approves the action. The client backend then processes the refund and updates the customer record. Customer data Policy RAG Decision and action Customer systems Assemble the case Approved policies Cited policy summary Check rules + exceptions Adviser approves Client backend Payment provider Customer record

RAG assembles cited policy evidence, while fixed rules and adviser approval control the financial action. No money moves until an adviser approves it.

What this would improve

  • Shorter handling current records and cited policy evidence assembled in one place
  • More consistent every case checked against the same guidance and fixed rules
  • Lower risk exceptions routed correctly and every financial action approved and recorded
The point

The useful automation is the whole chain: assemble the case, find the policy, apply the rules and complete the approved action without losing human control.

How much of each customer case is spent finding information?

We build assistants that bring the full case together and help staff complete routine resolutions safely.

Discuss customer operations