In late June 2020, I was casually browsing through LinkedIn. The feed was filled with people losing their jobs, companies doing layoffs and here I was sitting, wondering what does my future hold, when I graduate in 2021. From my previous experiences of looking for internships, I knew that the competition is fierce, especially for entry-level roles. Just a few weeks prior to this, I was given the responsibility to hire interns for a startup I was working with, so I had the first-hand experience of being a job seeker as well as a recruiter, while still being a student.

And suddenly, I had that lightbulb moment:

Lightbulb
Photo by Omer Sonido / Unsplash
What if there was a software that would allow me to highlight my skills, according to the job I was applying for?

At this point, I had read a lot of articles and discussions on the importance of Tailored Resumes and I knew that they made a huge difference, but the problem is that it takes a lot of time to tailor your resume and job searching is, unfortunately, a numbers game right now. If you applied to a 100 jobs online, you'd hear back from less than 20, you'll interview with less than 10 and will ultimately get just a couple of offers. I saw numbers like these in many surveys and experiments but understood their meaning when I experienced this for myself.

After sitting still for ~5 minutes, thinking about the idea and the problem I called my friend Rajveer, having worked on many projects together I knew that if I'll do this, I'll do it with him. We discussed the problem, the idea and what would an ideal solution be. Both of us have a basic understanding of Machine Learning and AI, so the first step for us was to verify whether we can make the AI models that can power this software.

Next couple of weeks were spent on gathering, cleaning and labelling the data to train the models, we then finally started the training and just after a few iterations, we were happy with the results and knew that we can actually make this.

But just because we can make something doesn't mean that we should, right? Well, that is what the internet suggested. So, next up, was idea validation. Before we started to code the solution, we wanted to verify that the problem exists for job seekers, so we created a landing page, with a Typeform to collect emails. Thankfully, we didn't have to go looking for job seekers to test the idea as all of our batchmates were starting their job search around this time. We created a short message and forwarded it on a couple of Whatsapp groups and people started to sign up.

What gave us a real sense of confidence was the fact that people started sharing our landing page with their friends, even before they ever got to try the platform, this did a couple of things:

  1. We were now sure that people understand what it is we are actually trying to make and how it can help them. It might not sound like a big deal but since both of us are engineers, copywriting is not really our strong suit.
  2. This also meant that people face this problem enough number of times that when they saw a potential solution, they were excited to share the news with their friends. Which indicated the word of mouth would help us grow once we launch and this was essential, since this a product where LTV (Customer Lifetime Value) is low.
Man raising his arms to signal victory.
Photo by Japheth Mast / Unsplash

We also took this opportunity to talk to a few potential users as well as a few HR professionals to understand the market further and finally started building the solution.

In the first week of September, we launched our MVP and gave access to around 25 people from our waitlist. To understand what worked well and what needs to be changed. This proved really beneficial as we discovered our onboarding needs to be overhauled. As you first need to invest 10-15 minutes to create and complete your profile before you can start deriving value from the platform, we realized that the onboarding has to be as simple and quick as possible.

While we were developing the product on one side, we also joined YC startup school, which is now open to all. Apart from the world class content, the weekly group sessions also helped us tremendously to create some great connections and get valuable feedback on the product.

I had been a huge fan of the way Y-Combinator works and wanted to apply but wasn't sure whether I should. Around this time YC startup school started their first build sprint, a program where 20 startups would be given a $10k grant. One of the steps needed to be considered for it: apply to YC, so we did.

Although we knew that we had most of the things YC expects from a startup to move to the interview stage, we were also aware that less than 0.5% of the companies that apply actually move to the next stage. So, we wanted to ensure that our application is actually easy to digest. To do that, rather than reaching out to other founders and ask their opinions on the same, we chose a different path: Applying to other accelerator programs.

The text steps highlighted
Photo by Clayton Robbins / Unsplash

I spent about an hour to prepare our initial application, applied to a program, got rejected, asked for feedback and spent 10-15 minutes to make changes as per the feedback. We repeated the process a few times and eventually got the application right. This helped in a few ways: for once we actually got the feedback directly from investors rather than fellow founders and secondly it also meant we didn't have to spend a lot of time obsessing over our application, rather making incremental changes over time, so that most of our time was actually spent on building our product.

Once we received the email from YC confirming we made it to the interviews, we spent the next 3-4 days going over commonly asked questions and preparing concise answers to those. We also practised by using a few tools and doing mock interviews with founders who recently went through YC. Although we didn't know any of these founders personally, once I reached out over LinkedIn and asked for help, most of them were happy to assist and their feedback really made us improve a few critical aspects of our application.

Then came the interview day or rather the interview night for us as we are in the IST timezone. Our panel had three interviewers: Brad Flora, Gustaf Alstromer and Victoria Krauchunas. Brad asked all the questions while the others were making notes, it went on for almost exactly 10 minutes in typical YC fashion. By the end, we were happy that we were able to communicate what exactly is the problem we're trying to solve and how are we planning to solve it. We also showed traction and speed with our beta launch and growing waitlist, but we knew the concern would be around how big can this be. We received the email next morning stating we didn't make it into this batch, the concerns shared were around growth and low LTV. Although it was a bit disappointing but wasn't really surprising. We went back to continue working on the product after that.

At present, we are a few weeks away from launching our next version, initially as a beta and then open to all. We are hoping to address the concerns that YC shared with us and are optimistic about the future of Bespoke Resume. The application and the interviews were a great experience for us and even though we didn't make it all the way, the fact that we were able to make it so far while being students and without spending a single penny (no T&C)  has really gotten us excited for the future. Going forward, we'll continue working on the project and hopefully help people with their job search along the way. Until then...