Introduction

Okay, just waiting for that to disappear.

And yeah, we'll go into the presentation.

Okay, here we go.

Founder and Origin Story

Okay, so just want to introduce myself.

My name is John.

I'm the founder of Skills Match, which is an AI powered job matching platform.

So I launched this just last month, actually.

And it really came from the idea that I had many years ago, which was everything shouldn't be when it comes to looking for work just based on CVs.

I wanted to

change that by being able to put your skills down and the hiring manager post their job and an algorithm will match the candidate with the role and this was a few years ago i had the idea and didn't have the resources or anything to

to build it and and then with these new ai coding tools i developed this mvp myself using bolt.new and i created it and saw it come to come to life so i've launched it now it's it's still in beta mode and so i'm just going to just take a few a few of the slides and i'll give you a demo

The Hiring Problem Today

But what it ultimately does is reduces the speed to finding the right talent.

So we know that it takes, you know, months to hire quality talent.

And on average, it says it's about.

about three months to fill a position in a professional role on average.

And there is a lot of noise from

you know, applicants, some unqualified.

You know, I looked at a role out of curiosity the other day.

It was an AI engineer role.

And when you go on LinkedIn and you see that it says 100 people have clicked, and then when you actually go into it, you know, that one role had over 1,200 applicants.

So there is a lot of noise, even for just single, single roles.

And that is difficult for the actual hiring manager.

So, you know, with tools like Skills Match, that should help them reduce all of that noise.

So they have some clearer idea of relevant talent.

And some of the best talents, they may already be employed, they're not actively looking on job boards, they're passive, they're not active.

So this tool is there to find that talent.

Noise in Applications and Passive Talent

So what it does, it matches based on skills.

So you know, the talent puts in their relevant skills, they put in their experience as well.

And then the

the hiring manager puts in the role and they put in the skills for that role, which is required.

And then the AI will provide a score for that role for the candidates, but also the hiring manager will see that score as well.

And then from that point, the human in the loop will be the talent partner will

will could provide an introduction.

So this is the flow of how it works the hybrid flow.

How Skills Match Works

Skills-Based Matching and Scoring

Hybrid Flow: Human-in-the-Loop

So they the hiring manager will define the role.

And then the once they've posted it, or they submitted a role on the platform, then the AI will generate the match.

And

Then after that, the manager would review, they would have access to look at all of the candidates in their dashboard based on the scoring, based on the matches, and they will see what the match is.

And then the talent partner could facilitate an introduction.

And from that, they will take it from there.

And this,

significantly reduces the time.

As I said, on average, it's about three months to fill a particular role, a specialist role that is, where this would reduce it into, until a short list up to 72 hours, they can get a short list of talent.

Live Demo Walkthrough

So we're gonna go for a demo now.

We're just gonna just see how it works.

And yeah, we're just gonna look, I've put a,

a live job from Anthropic.

And yeah, this is just part of, for the demo purposes, but it's a live role.

And just to see how it works.

We're gonna look on both sides.

We're gonna look from the candidate's view, but we're also going to look at the hiring manager's view as well.

So we're gonna log into both.

Here we go.

Candidate Dashboard and Match Insights

okay so so we go to the dashboard um and just a few jobs at the moment but here we can see that

For this role here, Software Engineer ML Performance and Scaling, we can see that John Ok has a 90% match for that role.

And then it's got an AI analysis

blurb at the bottom there, which says that the candidate has strong skills in Python and GPU programming, et cetera, et cetera.

But however, they lack experience in a particular area, which is large scale systems, distributed systems, et cetera.

So it gives you some information on where you may be lacking, but 90% match.

what that also does for the candidate is it saves them time from sifting through

different jobs because they can see that the ones with the higher match, better score is the ones that they'll probably target, you know, rather than going through a typical job board and just sifting through and then going into it straight away, they can see the the score and think, right, OK, let me view details, for example, to go in here and then get further, further information and then they can apply for it.

So that's that's how

That's the view of the candidate, as it were.

So this is the dashboard view.

So it says three job matches.

The average match score is 83%.

Then it's got the skills, one job they've applied for.

So it's got all the relevant information.

Building a Strong Candidate Profile

And then now what we're going to look at is the profile of the candidate and what they enter in.

So here they'll just enter in their skills, technologies, and they put it all in, put in experience, location, and then they just put the professional summary.

And if they acquire more skills over time, then what they can do is add the skill there and then

click save profile, generate matches, and then it would refresh and then go back to the dashboard.

Then this could change based on the updates of that.

Now you might say,

right well anybody can just put in you know any skills you know that is correct you know you know people are not always um honest when it comes to skills and and experience however you know as i mentioned this is a hybrid um platform as well so this

It's like a talent partner having this tool to aid them, aid them with finding the right talent.

So they will be vetted.

They will be vetted to see if they stack up to what they claim as well.

But this is there to kind of aid the whole process and speed up the recruiting process.

So

we yeah we have this match here if we go for the role that i mentioned earlier which i said is 90 so now what we're going to do is go into log out and then go into the the view of the manager and see what they see all right let's go

Hiring Manager Dashboard and Actions

Okay, so yeah, this is the manager's view.

So these are some of the jobs that they've submitted, posted.

And yeah, let's look at this role.

This role is what we saw from John's profile, who was the candidate.

So let's look at this role.

So same role, the hiring manager can go in into his dashboard and he can see, right, okay, he's got a match for the different roles that he's posted and go into it.

you can see right okay this person has got a 90 um score you know so that's very um very high and could be a um a good candidate for the for the role and then also that same information the analysis that john had also the hiring manager can see as well here as well so they can see you know which specific areas they're lacking in and

After that, what the next stage is, is they can request an intro.

Okay, so they can now think, okay, so they can see the candidates they want to follow up with, and then they can request that intro.

And yeah, they will just, this will just reach out to the talent partner

who will make that connection happen.

And that just is there to make the process seamless, efficient, and ultimately match the right candidate to the right role.

Why Human-in-the-Loop Matters

It's not a...

I guess, yes, there's a human in the loop.

It's not just, you know, technology's doing the whole thing.

We need the human in the loop to do that, but it's just making the process a lot quicker and a lot painstaking as well, really.

Core Functionality and Beta Status

Yeah, so that is the main

sort of core functionality of it.

I launched it last month, so it's in beta.

The actual goal now is to, yeah, is to build it up, to build up a talent pool, and then, yeah, just connecting with hiring managers as well, and then just to build this up over time.

So yeah, that is it.

Messaging and Coordination

There's also a messaging feature there as well, but it's gated at the moment.

Once the hiring manager and the,

candidates communicate then that will be open and they can communicate in terms of you know whatever sort of plans they want to schedule and that can be done but yeah that is skills match i'm going to open up for questions thank you very very much john um

Q&A Session

We've got first question from Alexander, which is, where are you sourcing the candidate data from and users have to do it?

Right, okay.

Where am I sourcing the candidate data?

So do you mean in this example or in general going forward?

I think it's in general, yep.

Sourcing Candidate Data

Yeah, so in general, it's, the candidates is, it's ones that,

you know, myself and, you know, my partner, we will be having connections with them, you know, personal connections, just like talent partners do.

And then,

inviting them to join a platform, and then they put in their data, their information.

We don't actually even see it, actually.

We run the platform.

We don't actually see their data.

So in terms of all the data they put in, that's what they see.

But in terms of getting candidates onto the platform, that's through connections and inviting people onto the platform.

Very good.

Tools and Stack Behind the MVP

I have one question, which is, did you use any tools to help build the platform?

Yes, yes.

I built this tool using bolt.new.

So it's like, for those who haven't heard of it, it's like lovable.

It's like lovable.dev.

I don't have a coding background myself, but as I mentioned previously, I had this idea a few years ago, actually, and I didn't have the resources to make it.

But with...

know these tools now readily accessible you know i um i went deep into it you know and the the database side of it as well super base um and integrated it with um with open ai um

one of the models there and yeah, yeah, I, I created this, you know, a little bit of pain sometimes, but, um, yeah, this is the, my MVP.

Um, perfect.

Yeah.

Yeah.

And, and, and authentic vibe, vibe coded, um, sort of solution or, um, yeah.

Yeah.

Yeah.

So it goes to show.

Yeah.

Yeah.

Yes.

Thank you.

Handling Wording Nuances in Job Posts

We have another question from Alborz about sometimes different postings for similar roles will have different wording for similar tasks, for example, annual versus yearly.

Will this factor into your resume slash profile?

Yeah, no, good, good question.

Yes.

So what I will say is from my understanding of your question, it does factor in in terms of nuances.

So, for example, if if something is they might say cloud computing experience, but someone just puts in Google Cloud Platform, then

I've prompted the AI to pick up nuances, but I guess that's all part of testing and that can be refined as well.

But I have prompted it to pick up nuances in terms of wording.

So yeah, but I guess that's something that is constantly monitored and can be refined.

Amazing.

Absolutely amazing.

I don't think there's any more questions.

Conclusion and Thanks

So I think that's going to bring today's talks to a close.

Thank you so, so much, Sean.

Oh, you're welcome.