We've all been there. A warning light on your dishwasher panel, and when you open the door, a pool of water that won't drain away. Or, perhaps, your new speakers fail to connect to your wifi network. You open a manual (or turn to Google), but you can't do it alone. Then you spend an hour on the phone (half on hold) with a poorly-equipped customer service rep, only to wait days for a technician to show up in a 4-hour window. If you're lucky, they come early and with the right part. If not ... grrrr.

Troubleshooting is troublesome. Shahan Lilja and Gintas Miliauskas understood this problem acutely and knew they could better equip individuals to diagnose and fix their own devices. Their insight and tenacity crystallized in Mavenoid, and we are thrilled to become their business partner as the Series A lead.

Tech support for physical products is a huge market. Any enterprise that ships a manual with their product is a potential Mavenoid customer, as are companies that employ field service engineers to repair hardware and software. White goods, consumer electronics, industrial machinery, agriculture, construction, metalworking, computers and A/V equipment...the list goes on.

So, why now? Improved technology in recent years allows for dynamic products like Mavenoid to be built - it literally could not exist before. Historically, the best approaches were typically a static, manually-written decision tree that evolved rarely and glacially. Mavenoid has built nifty software (a NLP powered Bayesian symptom map) to intelligently parse customer queries and troubleshoot via an organic knowledge graph. Think of it as a dynamic manual, constantly improving as more customer info and queries are fed in, and therefore better than a static rules-based approach.

Why does this matter? For the user, manuals are challenging; if a problem requires a call, the majority of us hang up in the IVR or on hold. For the enterprise: of the billions of annual support calls globally, over half go unresolved or require costly field visits. As a result, customer advocacy, and correlates like NPS, plummet, not to mention the massive cash expense. Beyond these obvious customer service cost savings, Mavenoid promises to aggregate and organize a company's otherwise disparate knowledge, and to enable manufacturers to deepen insights into customers' needs and how their products are used.

With this realization, we understood that Mavenoid's opportunity is even bigger than the theoretical TAM research suggests. Most broadly, there is whitespace to potentially map millions of products, guiding individuals through complex solutions for problems they may not articulate or be trained even to identify. In 5-10 years, we envision Mavenoid substituting for millions of calls to call centres. We envisage an opportunity to build the platform for product troubleshooting.

Market opportunities of this scale and impact deserve a great team. We have enjoyed getting to know Shahan and Gintas over the past months, and impressed by their tenacity. Former colleagues with whom we spoke were effusive in their recommendations. We have also been encouraged by customer referencing - including from companies that we might otherwise expect to be conservative about working with early stage startups. It is a testament to the maturity of Mavenoid's product and sales capabilities that early customer wins include leading white goods manufacturing brands, and a global IT electronics player (that had once upon a time built a service broadly in the same space, spun it out years prior, and then recently switched to Mavenoid).

Mavenoid was seeded in Stockholm in 2018 by some great local angels, Point Nine (Christoph Janz), and Creandum (Staffan Helgesson): two VCs that bring lots of relevant experience and with whom we are delighted to join for the next chapter of the journey.

The $8M Series A we are leading, with significant participation from both these funds, will power the company's expansion for the next two years.