Thousands of years ago, people used to hunt. And there used to be hunting competitions. Who gets the biggest fish in the pond? Who kills a giant bear? Who shoots most birds? Hiring in the tech world is going through a somewhat similar phase. There’s competition between the companies to see who can hire the best developers.

With this hypergrowth in hiring, we forgot to scale the systems correctly. Companies rely on a similar hiring pipeline that has been there for years, which works, but at the cost of the several hundreds of work hours of engineers for each hire. The ratio of candidates to hire is around 200:1. And just for the interviews to hire is 48:1.

Overview

While interviewing people at Kawa, there are specific patterns we noticed. And after talking with many people from the industry, these patterns are ubiquitous.

Typically, there are five rounds of hiring pipeline. They always start with similar types. First is the resume review round, where around 25% of people drop-off. Then comes the phone round, where technical recruiters try to see if there’s a good fit and expectations from that person. 5% of the people drop off here. The most crucial round of hiring comes down to this third one, where candidates are set up on calls with engineers for their first technical round. This is like a Thanos snap, but only slower. 80% of people drop off in this round. And the next two rounds just nail down the rest of the stuff.

Problem

One of the problems, that lies dear to my heart, is the way the education system is structured. Most companies try to solve this problem with various methods. As often seen, the solution to this problem is considered to be in the edtech sector. Lambda School, Pesto, Newton School, and numerous other ISA based startups throughout the world have had a jab at creating solution for this problem. But thinking of the education system in isolated environment, they see this as the problem, and try to build solution for it.

We need to stop looking at the education system from isolated perspective. That's when we find that the entire system is structured for people to get jobs. That's where we fuck things up. Recruiters like to look at college degrees at the first glance. Even though that is changing in the current startup landscape, a person coming from a completely unconventional background, such as arts, or literature, or even just 12th std graduates into the IT field, is somewhat frowned upon. Combing through candidate's social and coding profiles can give you a good insight as to whether the candidate is competant or not.

And that's what we started to do. Looking at this problem from complete perspective, what we found after talking with multiple people and drawing from our own experiences, hiring is driven manually, instead of basing it on data.

Solution and Landscape

The heart of all the incoming candidates is the ATS. Most companies rely on the ATS systems that allow you to drag and drop the candidates through the pipeline and automate it on a minuscule level. They do not provide more information in detail from the user’s resumes and social profiles.

This is where Hyperlog comes into the picture.

As our first step, we are building our version of the ATS, which gives social insights and a proper breakdown of candidates amongst other candidates.

Our early prototype of this kind of ATS had received some level of validation from companies. It had some essential features that allowed to list out the candidates extremely fast. The algorithms that we built for analyzing GitHub profiles and social insights were perfect. But at the end of the day, it wasn’t as powerful as many other ATS solutions. So we decided to open-source that ATS and build it as an open-source project to increase the adoption.

Secondly, the first technical interview of a person is repetitive and could be easily simplified by AI. Every technical round has a flow—the introduction, problem statement, and clever solution. There are many solutions in the market for checking whether a person has good coding skills or not. Most of these systems just work as a simple coding playground. We are building a platform that analyzes developer’s technical skillsets and body language, and communication skills.

Our overall system will be comprised of tight knitting of these two products. This will help automate the process that can filter out 80% of unqualified candidates without wasting extra hours on hiring.

Thus, building a sustainable system that improves hiring and saves time providing the industry with furnished talent. Hyperlog strives to filter the quality of candidates and skills to the utmost edge.

Get to know more

You can check out a glimpse of our ATS at https://hyperlog.io.

Currently, we are a team of two. Feel free to get in touch with us.

  1. Aditya Giri - aditya@hyperlog.io - +91 96373 05012
  2. Kaustubh Maske Patil - kaustubh@hyperlog.io