We are proud to announce Galileo’s continued support backing Relevance AI in their US$10M (AUD $15M) Series A round led by King River Capital alongside Peak XV Partners (formerly Sequoia India & SEA), and Insight Partners. 

Read the announcement in The Sydney Morning Herald or visit their website to learn more.

Galileo backed Daniel Vassilev and Jacky Koh, co-founders of Relevance AI, from the very beginning, investing in their pre-seed round in 2020 when awareness of vector databases and LLMs was low. 

The latest round of funding will fuel Relevance AI's global team expansion, product releases and customer growth for its low-code platform that lets companies build and deploy custom AI agents to automate repetitive tasks.

In just three months, over 6,000 companies have eagerly embraced Relevance AI, utilising the platform to execute a staggering 250,000+ tasks.

Relevance AI has a big vision. "Every team will have hired at least one AI agent by 2025, and by 2030 have a full-fledged AI team supporting them”, says Daniel. “Our mission is to enable teams to only be limited by their ideas, not their size – from the seasoned industry player to the ambitious newcomer.” 

Paving the way in the new product category of Multi-Agent Systems, Relevance AI has attracted an impressive group of leading global VC firms across Australia, SE Asia and the USA.

Our journey with the team so far

A Q&A with James Alexander, Co-founding Partner, Galileo.

There are a lot of GenAI [Generative AI] startups getting funded right now, what makes Relevance AI so compelling? 

James: It’s now obvious that conversational chatbots, such as OpenAI's ChatGPT, are the winning product use-case for LLMs, but even 12 months ago i.e. early 2022 it was very unclear how the latest AI technologies (including LLMs) would be used. 

Sure, we had APIs into LLMs and we had some early research products, but only a handful of companies – Relevance included – were really connecting the dots on how to create the ‘AI infrastructure’ to deploy and use these systems. 

What makes Relevance so compelling is combining work in vector databases (representing all types of data so that machines can interpret it), ML techniques, and LLMs – all in an interface that allows any expert to create a custom AI workflow that we’re only now calling agents.

Unlike a typical chat interface for speaking with an assistant, Relevance AI is focused on task-based outcomes with an experience for delegating work rather than individual conversations.

There’s been a lot of concerns in industry, media and government around AI safety and potential job loss. How do you as an investor think about this in regard to investing in companies like Relevance AI?

James: It’s my view that the AI ‘taking over the world’ scenarios are seriously overblown. To be clear, there is a lot more research and development to be done before we have anything close to AGI. Unfortunately, AI doomerism sells. A lot of people are using this rhetoric to support their agendas.

While there is some fear about AI taking over the world, Relevance’s mission is to help businesses build AI workforces to supercharge productivity and human prosperity – it’s about enabling people to do more and totally new things. 

While this might sound lofty, we love their big vision and mission. In the end it’s about spending less time on the repetitive mundane tasks to be done and more on the activities that spark joy. 

Tell us about the first check back in 2020?

James: I like to say we invested in Relevance AI before AI was cool. 

Back then, the company was not called Relevance AI and AI agents as a category didn’t exist. They were trying to use the same techniques LLMs use but deployed internally for enterprises to take advantage of ML advancements like ecommerce search and image processing. 

We loved that they had an early product with customers that loved it, and could step-change or build a 10x better product by using AI to find the relevant information automatically. And this was all before conversational interfaces like ChatGPT became popular. 

From day 1, we had a firm belief in the team’s ability to build a transformative platform and are proud of how far they have come. The pic below was after signing their term sheet for their seed round led by Insight Partners (NYC) – the earliest that firm has ever invested in this part of the world. We think that says a lot about this team and their insight [pun intended] into this area.

What observations do you have on the Relevance AI team that other founders learn from?

James: There's a few things but the main parts are product vision and executional speed.

For founders I think the lessons (so far) are:

• Deliver with executional speed – the team has a high bar when it comes to deploying new features quickly. They iterate at a dizzying pace. Rather than pontificate and think, they ship and test with real customers and don’t get caught up with tech debt or features that don’t work. It helps that the team loves software engineering and consumes it for breakfast.
• Focus on core team strengths when thinking about Go to Market (GTM) – there's been a lot of directions the product has taken over the 3 years but the team has always focussed on what areas they have core strengths in and what areas they have true insight into. This is still playing out so time will tell if their GTM is correct! But the principle remains as its core to your competitive advantage.
• Have a big vision, based on insight - the team has always had a big vision for this space, but it comes from a place of technology and customer behaviour insight, not just lofty words. Sure, things change over time but having that bigger vision first and zooming in and focussing on what's next is an important skill every founder needs to learn.

The Relevance AI team is hiring. If you would like to be a part of their rocket ship growth, check out opportunities in Sydney and San Francisco 

Emerging founder looking to raise the first round -  We want to hear from you! Apply today.