Meet Marvin: the AI layer that turns technical depth into fast answers

Munu is a Nordic-built restaurant and hospitality platform that connects POS, booking, analytics, inventory, and payments in one cloud-based system, helping restaurants, bars, cafés, and hotels run smoother, faster, and more profitably.

For Munu’s customers, the support team is the relationship — the people on the other end when something needs fixing. Hospitality doesn’t pause, so neither does the team: they’re on hand around the clock for critical issues, whenever a venue is mid-service.

What lands in the queue spans the full range: a simple power issue with a piece of hardware one minute, a complex software customisation or an advanced user query the next. Whatever its size, every ticket has a business waiting on a fast, accurate answer.

Moving between hardware faults, software questions, and a range of customised setups, team members lean on documentation that is both deep and highly technical. And the detail they need is rarely in one place; it sits across several sources, to be pulled together by hand, in the moment, while a customer waits. That combination, depth plus scatter, is what slowed even straightforward cases down.

The bottleneck was access, not expertise.

Marvin connects documentation from across the team’s online sources into one place, and lives where the work already happens — built into a Chrome extension, right beside the system the team already uses every day, so there’s no new tool to adopt. Open it, and the answer and the full context of the case are right there. The headline is the AI layer on top: it reads dense, highly technical material and hands back something a team member can use straight away.

Across diagnostic cases, comparing 60 days before and after rollout, the picture was clear. Answers came faster, landed right more often, and needed fewer hands to resolve, including on the hardest, most technical tickets.

Faster answers, fewer reopens, less back-and-forth.

Based on an independent analysis of roughly 9,000 support tickets. Figures cover diagnostic cases, where Marvin is built to help.

Marvin is a foundation, not a finish line; next it moves toward a customer-facing layer that can take action on its own. For Munu and Nuto, this is step one of building AI into the everyday.