Build Club Spotlight: 🧩Multitudes with Lauren Peate

Welcome to the Build Club Spotlight Series, where we highlight some of the most promising AI startups!

This week, we’re spotlighting Multitudes with founder Lauren Peate.

After sharpening her skills in statistics at Stanford and diving into the startup scenes globally, Lauren saw firsthand the inequities in tech. Driven by a desire to make a real impact, she created Multitudes—a company dedicated to helping teams perform better by addressing both work challenges and wellbeing.

Join us as we explore Lauren’s story and insights that led her to build a tool that’s reshaping how teams collaborate.

I would recommend checking out the demo below!

Let’s dive in!

What is your background and how did you get involved with start-ups?

I’m an “accidentally-in-tech” person, which is ironic given how technical the work is that I do now. I grew up in Southern Arizona in the US; I’ve always been a nerd who liked reading and math, but it wasn’t until I was almost 20 that I wrote my first line of code.

I did my undergraduate at Stanford and went deep into statistics, specifically econometrics – at the time I thought I wanted to do a PhD in economics. I was surrounded by startups at Stanford but many of the founders I met at the time seemed focused on hype rather than impact.  

Only when I moved to Jordan (I’d studied Arabic at Stanford) and started working with founders there did I get excited about start-ups. The founders I met in Jordan had all the grit and determination that you see in founders around the world, but they were also building with a lot of heart – they were very focused on solving real problems that impacted their communities.

Fast-forward a few years – I moved to New Zealand (I married a Kiwi!) and had been working with startups around the world, in the Middle East, the US, Southeast Asia, and of course New Zealand. 

What was the reason your started Multitudes?

I was frustrated by the inequity I saw in the tech sector – the number of times an amazing person from a marginalized group was passed over for an opportunity or promotion. That frustration eventually led to me starting my own consultancy for diversity, equity, and inclusion, focusing on how product and engineering teams could improve. We worked with amazing global companies like Automattic, Xero, Vend (pre-acquisition).

While going deep into data science, I started to experiment with better ways to measure our impact on teams.  In one of those experiments, a manager I was working with thought a team member was underperforming. When I looked at the data, it turned out this person was getting less feedback and support than anyone else on the team!

When I asked the manager what might be going on there, he went quiet and then shared that this person was the only woman on the team – so maybe unconscious bias had gotten in the way of people giving her good feedback. The team wasn’t setting her up for success.  This is a common problem, where women get less feedback than men even if they ask for it. 

The story ends well though – once he saw the data, the manager was very motivated to improve things and the team ultimately got to a more balanced place with feedback. That was the moment where I knew I had to build this out.

The core insight behind Multitudes is that teams already have rich behavioural data about how people are collaborating and how the work is going, so we use that to surface those key insights about people (burnout risk, who’s not getting support) and also the work (where work is getting blocked, what’s interrupting people). It’s already worthwhile to look at this data to support team wellbeing and collaboration, but it also turns out that improving those things makes the work go better too – teams using Multitudes get 25% faster on average.

Looking back on my journey to get here, my key takeaway is that you can always pick up new skills. I came into tech feeling like I was behind because I didn’t start coding at 6 years old. But so many people in this industry have changed careers or are self-taught – and, frankly, bring even more value  to their work because of their diverse experiences.

How would you describe your product offerings to a new customer and to an AI engineer?

New customer: Multitudes shows you what’s holding your team back from performing as well as it could, and then guides your team to take action. For senior leaders, Multitudes provides insights into research-backed metrics from DORA, SPACE, and more so you can share your team’s progress and find levers to improve on gaps.

AI Engineer:  Multitudes enables AI teams to improve the time from model development to deployment. AI projects often involve complex pipelines, with many iterations, longer lead times, and delays in deployment due to the complexities of ML model deployment. Our AI agent tracks metrics like lead time and deploy time, and it proactively identifies bottlenecks in the MLOps process. 

How has the journey been so far - how has Multitudes evolved?

We’re in an exciting phase now where we’re doubling down on our core differentiators – how to keep these metrics human-centric (even when performance conversations come up), and how to help teams take action.

In terms of the tech, we’re also at a tipping point where we have enough integrations and users that we can bring in more sophisticated ML methods. 

We haven’t changed in terms of our bigger goal – making tech more equitable and inclusive – but the product has definitely matured to where we’re bringing in larger customers and able to bring more of the bigger vision to life. 

What advice would you give to aspiring AI builders?

Start with the human impact. Just because you can build it doesn’t mean you should – there’s all sorts of cool tech that doesn’t link to user needs or, even worse, could cause harm.

Without checks and balances, LLMs can exacerbate the worst of humans – our racism, sexism, homophobia, and more. And it’s hard as startups because most of us aren’t the ones training the models. But there are still lots of things we can do to make sure we don’t cause harm – for example, more specific prompts (saying what you don’t want, like racism, as well as what you do want) and asking the model to critique itself. 

We hosted an amazing talk by Pedro Silva, an ML engineer who manages the Inclusive AI team at Pinterest, and he shared more practical tips for evaluating your models to be more responsible; you can see more here.

We’ve also put our own guardrails in place at Multitudes as a check to make sure we’re not causing harm. You can read more about those here: Measure what matters and don’t be creepy

How can our audience help you build and grow Multitudes?

We’re always looking for people to give us product feedback! Especially from product and engineering folks out there, we’d love to hear how you collaborate and support wellbeing on your teams. We have a one-month free trial if you want to give it a play with your team.

Liked this post and want to be featured next? Reach out to Daniel Malkinson or [email protected] 

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