Interview With Hazim Mohamad Co-Founder and CEO of Coffespace

In this edition of the Founder interview, we introduce Hazim Mohamad, Co-Founder and CEO of Coffeespace, a platform that connects individuals looking for cofounders or partners for startup ventures. Coffeespace has been experiencing growth through strategic partnerships and platform availability. Hazim aims to facilitate meaningful connections among aspiring entrepreneurs, emphasizing the importance of finding compatible partners in the startup ecosystem. Read more about Coffeespace here.

Can you share with us the inspiration and story behind the creation of CoffeeSpace? 

It’s hard to bring ideas to life alone. I sat on an idea for two years, but it was only when Carin reached out (by luck) that we started exploring together and building. This is a common phenomenon – in America alone, 61% of people have a business idea, but 9/10 of them don’t follow through, hence most ideas never come to life (i.e. they don’t even try).

5 out of 6 top reasons people don’t follow through can be tied to not having a cofounder (e.g. not having all the skill sets, feeling overwhelmed building alone/of the uncertainties, not having the time, insufficient resources).That’s why we’re building CoffeeSpace, a TInder/Hinge-like platform that algorithmically matches people exploring ideas and looking for cofounders in the tech space and beyond. 

We want to unlock entrepreneurial opportunities globally by reducing the barrier for people to explore their ideas.

How does CoffeeSpace differentiate itself from other co-founder matching platforms like YC and Cofounder App?

We’re different in 3 main ways:

We have more granular data + filters like prior experience selling/co-founding/working in a startup, time commitment level, idea commitment, equity preferences etc.

Dual-sided compatibility i.e. by default we will first recommend candidates who meet one another’s requirements, increasing the odds for a successful match.

Our app very much operates like Hinge in terms of UI/UX which we believe is much more intuitive in addition to higher/faster response rates

In addition, while still early, we’re working on improving our algorithm in 3 phases:

• The ‘Boolean’ Level: Filters based on specific variables like location, skillset, equity split, timeline, commitment to ideas, etc.
• The ‘Social Graph’ Level: Using data such as education and experience as well as swipe data to come up with a social graph of people they have a preference for (especially weighting those they swipe right on).
• The ‘Semantic / Personality’ level: Using textual data such as similarity of ideas between two people or the complementarity of personality/motivation based on prompts using OpenAI’s embeddings API and other similar LLM applications.

Can you elaborate on the granular filters CoffeeSpace offers and how these filters improve the matching process?

During our pilot phase from Aug to December last year, we got thousands of data points on what makes people swipe right or left on recommendations, and most (90+%) were based on dealbreakers/preferences like: 1) location, 2) skillset, 3) equity split, 4) timeline, 5) commitment to ideas, etc

As shared in #2 above this is the first phase of matching and we’re working on building out the different elements for Phase 2 (i.e. Social Graph)

What key challenges did you face while developing CoffeeSpace, and how did you overcome them?

Challenge #1: Validate it’s an actual (and painful) problem and people were willing to pay

The most important part for us was initially to validate that people actually had the problem (of finding a cofounder), that it was a ‘hair-on-fire’ problem as defined by YC i.e. it’s something that is top of mind for them and one where they’ll use even basic/simple tools to help address (since an app built by an early-stage startup will initially be rather simple/basic).

Not only that, we wanted to validate it without spending too much time developing a full app since the true trial for how painful a problem is is that people would use (and pay) for a very simple version of the solution.

What we did was to initially interview about 50 people whom we knew had the problem within the first 2 weeks of ideation as they were posting/using some other platforms where people are finding co founders.

After 2 weeks of doing so, then within the interview we started asking if they would pay for a very simple version of the solution, i.e. we did everything manually:

We proxied the profile feature via a Google Form – we asked who they are and what they wanted in a cofounder.I proxied the recommendation model/ algo, i.e. I personally looked for people on platforms like LinkedIn, Reddit, Slack/WA Groups etc and sent both sides messages.

If they both wanted to chat, they’ll message me, “Yes, I’m interested!” and I’ll put it on their calendars (proxying the double-sided ‘swipe’ feature).We got the first 15 people paying us within the first month alone and only after that did we build a simple MVP .

Challenge #2: Solving the ‘Cold Start Problem’

For an app like ours, we need to get the initial critical mass to serve folks.I.e. If we only had say 200 users it would be very hard for anyone to find a match.Our theory was that if we could launch and get 1,000 people within a few targeted cities on the app within a month of launch, most people would be able to get at least a few dozen possible candidates.

Hence what we did was to have a pilot phase for a closed beta group of testers to see who was interested (whom we served via the simple MVP) before actually launching our public mobile app.By the time we built and launched the app in March this year, we already had a lot of people who’ve indicated interest, hence that helped a lot in getting to the 1,000 user mark within the first month of launch.

How does CoffeeSpace handle the privacy and security of its users, especially considering sensitive information like startup ideas and equity preferences?

Users have full control over what they share, and only those who have been approved to join our platform can see their profiles (when both preferences match each other’s) For ideas, users would generally share the high-level description that would be sufficient to get potential matches interested to chat but not the proprietary details.

As for equity preferences, we have the option of ‘fully negotiable’ if they don’t want to share the specifics

Will you share a success story from CoffeeSpace that you find particularly inspiring or noteworthy?

The first match who told us they are now working together was Sara Creech and Ted Lin – they paired up to build Akoya, an AI-powered travel platform.

Sara was previously at Nike doing product and strategy, while Ted is a software engineer at a Series A travel startup.Sara had tried for many months through multiple channels like personal network, LinkedIn, and other platforms.

While they were both committed to their ideas, it turned out that their ideas were similar hence that made the match even more ideal (we were still manually serving people at this time).This match is also why we’re so excited to incorporate LLM/NLP into the recommendation model because with the latest tech we now have a way to use text and extract semantics at scale – beyond ideas, we’re looking to match folks based on personality (using content written in prompts), working style, life philosophy etc

Can you explain to us the pricing plan for CoffeeSpace?

We use a freemium model similar to dating apps, i.e. everyone can use the app for free and can subscribe to our premium tier to unlock more features.

At the moment we have launched the Business Class tier ($49.99/month), which currently includes: 1) More daily recommendations, 2) Set premium filters, 3) Send priority invites, 4) See all invites, etc.

Users can also just subscribe for 1 week to try out the premium tier or for longer periods (3/6 months) for better value.Launching Economy ($24.99/month) and First Class ($249.99/month) tiers in the coming months.Launching Send a Coffee (Superlike-equivalent) and Boost features in the coming months

How does CoffeeSpace support users not building tech startups but interested in other business ventures?

We already have users on the platform who are building their own funds, launching CPG businesses, and pioneering more innovations outside of tech We’ll be;

1. further granularizing the skill set filter for a broader user pool, and 

2. adding ‘type of business’ and ‘company stage’ as additional filters to ensure we’re able to match people with similar preferences.

Can you share some details about the funding journey for CoffeeSpace?

We have raised a total of $350k to date – we’re backed by exited founders, YC-backed founders, and angel investors from Google, Meta, Quantum Black, and more. Most of them have either gone through the problem of finding a cofounder themselves or know of others who are, hence they strongly relate to our vision.

Lastly, what advice or key tip would you give to entrepreneurs and startup founders in the market?

Some of the main things I’ve learned are crucial as an entrepreneur:

1. Resourcefulness: after going through the journey of building CoffeeSpace since June last year (and a couple of different ideas before that), I’ve learned that you can do a lot with little resources, and especially in the earliest stages of a start-up that’s very crucial.

2. Being able to learn fast and being agile: the main advantage a start-up has is speed, since in a startup you can just try to experiment on something quickly and validate/decide to proceed or otherwise while in bigger companies it might take weeks to months to do so.

3. Being customer-centric: the most important people you need to serve and make happy are your users. It’s famously said one 2 things matter for a startup to succeed: 

• Talk to users
• Build the product

As long as you keep on talking to users and make iterations to the product to make it better based on their feedback, you’re optimizing the odds of building something that’ll be loved and that your users will rave about