Introducing the Claimlane AI Agent: Your New Returns & Warranty Assistant

After years of building Claimlane we've seen the same challenges across every customer: support teams spend too much time on repetitive work, decisions vary between agents, knowledge is not properly documented & shared and extensive training is required for new joiners.

We’re launching a feature to help with that, our own AI Agent.

The Problem

The root cause remains the same, support teams are overwhelmed when handling returns and warranty claims. A few of the causes are:

Agents spend too much time reading long descriptions. Customers write extensively about their problem and it takes time to read and figure out what the problem is and what resolution to offer.

Inconsistent decisions across the team. Different departments may have different views over the same issue which prolongs the time until the ticket is closed.

In most cases, experienced agents have a lot of knowledge about the brand, the product and could observe patterns over time. This becomes a problem if they decide to leave and, oftentimes, this is not documented for everyone to access. Moreover, suppliers have different rules and it can prove difficult to make them well known across the team.

Known common issues with a product can be identified faster by using our new AI Agent, knowledge that a human agent might not have without prior research.

Fraud. This seems to be an ever evolving issue in our society and retail makes no exception. Oftentimes, customers with fraudulent behavior patterns are hard to detect. Our AI Agent has an overview over the ticket history of a customer and can identify abusive behavior and same time and money for the brand.

Our Solution

The Claimlane AI Agent will live inside our portal, on every ticket. It will understand the context, know your products, suppliers and customers. This knowledge is used when suggesting the correct outcome for your customer service agent.

When your team opens a ticket, it is instantly analyzed and key information is shown:

Customer summary - full history, behavior patterns and previous tickets

Suggested action - refund, replace or manual review

Reasoning - why this action makes sense based on your rules and data

Execution at the click of a button - if your agent agrees with what is being suggested, a button can simply be pressed and the action made in the background

An example based on a real situation would be:

A customer reports their electric toothbrush won't charge. The AI shows:

Customer Summary:

"The customer has only one previous ticket, reported for the exact same product with the same charging-related failure. Their history is very limited and focused on a single item, with no broader pattern of claims or inconsistent story."

Suggested Action:

"Approve the claim and send a replacement toothbrush including a new charger."

Reasoning:

"This model has a very consistent history of charging and power-failure defects, and the customer's description aligns exactly with those recurring issues. Because the failure type is already well-documented and highly predictable for this product, additional troubleshooting or back-and-forth is unnecessary."

How it works

We have built the AI Agent on three core concepts:

1. Rules

These are your policies for handling claims, returns, shipping damage, delivery issues, etc. The guidelines you'd normally tell agents during onboarding. You write these in plain markdown, making them easy to read and edit. The AI uses them to guide every decision.

2. Knowledge bases

The AI automatically builds three knowledge bases from your historical Claimlane data:

Customer Knowledge:

Tracks claim history for every customer. Identifies behavior pattern.

Detects fraud signals: frequency of claims, inconsistent stories, suspicious patterns across multiple customers.

Product Knowledge:

Records what problems each product has and which solutions worked. This captures the knowledge that is usually known by experienced agents.

Supplier & Brand Knowledge:

Learns each supplier's requirements and preferences.

A key details to note here is that these knowledge bases build themselves. As you handle tickets, the AI learns in the background. Based on the decisions you take and rules you set, it automatically adapts the output.

3. Actions

Based on your rules and knowledge, the AI suggests one of three outcomes:

• Refund - Issue a full or partial refund
• Replace - Send a replacement product
• Manual Review - Complex cases require human intervention

This allows you to stay in control, but saving time on each ticket. We make the suggestions, you decide the outcome.

The business impact

Building your own AI takes 6-12 months and requires data science expertise. You need to integrate with your systems, create prompts for every scenario, train on your data, and maintain it as things change. Our AI Agent does all of this out of the box, allowing you to be live in days, not months.

What makes us different?

It's a copilot, not a chatbot.

We're not adding a chat interface. The AI Agent works inside your existing ticket workflow - analyzing every case and suggesting the right action based on your rules and knowledge bases.

It learns from YOUR data, automatically.

The knowledge bases build themselves from your historical Claimlane data. Every ticket you've handled teaches the AI about your customers, your products, and your suppliers.

You control the intelligence through rules.

Want to change how the AI behaves? Update your rules (written in plain markdown).

The rollout will happen in three phases across 2026

Phase 1: Summary & Suggestions (Q1 2026)

This is our starting point. The AI Agent will be present and make suggestions, but a human will stay in full control.

The AI will:

Summarize cases and suggest actions

Automatically build&update knowledge bases

Phase 2:  Reasoning & Actions (Q1 2026)

In this second phase, the AI will execute actions automatically (with your permission). It will be based on patterns observed across tickets. At the same time, the AI will continuously update its internal knowledge base with rules.

Integration with your tech stack will be available, we will continuously work on expanding that.

Phase 3: Agentic Workflows (Q2-Q3 2026)

By this phase, the AI Agent will reach its maturity and can handle complete case resolution. Full end-to-end automation for standard cases, with humans overseeing exceptions.

Alongside it, you will be able to build workflows across departments (customers service, finance, suppliers)