How Claimlane’s commercial team used AI to accelerate growth

About the company

Claimlane is a Copenhagen based software company providing a returns and warranty claims management platform for brands and retailers selling physical products.

The company was founded in 2019 and the platform is now used by more than 45,000 businesses all over the world, including iconic brands such as Black Diamond, GANNI and Marc Jacobs.

In 2026 Claimlane launched the first AI Agent purpose-built for warranty claims and returns, reading photos and videos at intake, applying brand and supplier rules, and auto-resolving cases.

Challenge

Claimlane is a venture backed startup with the ambition to grow faster than most companies. AI gives them the possibility accelerate, both by building AI into their product and create more value for their customers and by using AI internally to give their team new tools to improve their work and output.

And that’s exactly the opportunity Claimlane was going after when they first engaged us. They had just launched their first new AI product: The world’s first AI Agent for automated handling of warranty claims. So Anders Sommer, a technical CEO, knew first hand the potential AI had for their customers and the effect it had already had on his own work and that of his developers.

It was this impact that Anders and Thomas Æbelø, CCO, wanted to realise on their commercial function. Specifically, to accelerate their growth by implementing a faster pace and leveraging AI throughout the commercial function.

Approach

We started with a simple plan: First, understand how growth actually works at Claimlane today. Then set goals with management and agree on the strategy. Finally, execute together with the team on the plan.

We began with a two-week discovery process interviewing the team and crunching the data. This resulted in a understanding of status quo and a growth plan for the rest of the year. Then we started execution.

Our work span across three primary tracks:

1. Run the growth team

We built the strategy. Implemented a biweekly sprint and experimentation process. And ran the process with the existing team.

1. Build AI leverage into the workflows that drive growth

We identified the key levers in the growth plan. Helped optimize these processes and built systems and AI agents to grow accelerate growth.

1. Enable the commercial team to use AI themselves

We set up the growth team on Claude, set guardrails and configured system connections. Facilitated training workshops to help the team get their first wins.

Results

We deployed as the interim growth lead, delivered the 2026 growth plan after two weeks of discovery, and executed alongside the team.

After the first 2 months, new monthly leads (MQLs) had grown 38% and new pipeline generation hit new highs. We continue supporting the team in executing across the growth function to improve process and build and implement tools that help the company grow.

Here are a few highlights from what we built together:

An SDR Prospecting Agent that centralises and automates list building

Like most B2B companies, Claimlane needs to find and reach many relevant companies. But prospecting was a manual effort with each salesperson searching for target accounts and contacts by hand. Anyone who's done this knows how time-consuming it is. Also, it meant that sales reps ended up reaching out to slightly different companies and personas, some less qualified than others.

So we refactored the process. Now list building sits centrally with a Revenue Operations person. It means sales people now get a prequalified list of companies and people and no longer need to spend time on finding those.

In order for the centralised list building to work, we needed more leverage. So we built a solution that automatically runs bulk searches for relevant companies, enriches them with metadata, and qualifies them against Claimlane's ICP. All programmatically.

More specifically, an n8n workflow orchestrates the searching and qualification, then hands off to Clay for enrichment. AI agents in Clay then surface the details a human SDR needs to personalise their outreach: e.g. the software systems a company uses, its returns and warranty policies, sentiment from recent customer reviews. Really anything that can be looked up and evaluated from a company's online presence that before a person had to check manually.

People search runs automatically too, finding relevant contacts along with their LinkedIn profiles and contact information.

Because the Clay table's metadata maps directly to HubSpot, new qualified companies arrive complete in HubSpot with all their metadata and are automatically scored for relevance. As such, the system runs end to end. From search to qualified company and contact.

The team has surfaced thousands of new relevant companies this way. And this system serves as the foundation for their new outbound model, where list building and prospecting sit with Revenue Ops instead of individual SDRs, freeing SDRs to focus on the value-adding work of crafting thoughtful, personal outreach.

A ghostwriter agent that turns team insight into content

Being active on LinkedIn with useful content is central to the growth strategy, but the best ideas usually live with the product and sales people who face customer problems daily. Naturally, writing a lot of content takes long time and the blank page can be daunting.

We set up a ghostwriting process where the team's content creator interviews key stakeholders for 10–15 minutes to surface ideas. The interviews are recorded, transcribed, and summarised into content ideas for blogs and LinkedIn posts, with the agent drafting in the desired tone of voice. For a small time investment, the content person now has ideas straight from the trenches, plus drafts to build on.

A sales coaching agent: a lightweight Gong at a fraction of the cost

We built an agent that transcribes and summarises sales meeting notes onto the HubSpot deal, then reviews sales performance against the team’s sales methodology (SPICED). The agent pulls together what's known for deal reviews, highlighting gaps, and suggesting which questions to cover in the follow-up or next meeting. Now every sales call is QC'd, and the sales team gets more reps on the sales methodology.

Conclusion

In the first two months, Claimlane went from an ambition to grow faster to a commercial function that runs on a faster cadence and is measurably compounding. New monthly leads grew 38%, pipeline generation hit new highs.

The team now has a centralised, automated prospecting engine, a content process that turns internal insight into published thought leadership, and every sales call quality-checked against their methodology.

Most importantly, the team sees the effect AI can have. For their own work and the team’s output.

We're glad to keep supporting Anders, Thomas, and the team as they accelerate from here.