How I built MyPitch AI interview platform as a client project | Worklayer

Sergii Alekseev·August 13, 2026

I am Sergii Alekseev, the founder and creator of Worklayer. Worklayer is my broader work: helping companies and founders use AI with real business context, not just as a blank chat window.

This article is about one of the clearest examples of that work: a client project called MyPitch - AI Interview Platform for Candidate Screening.

To be precise, MyPitch is a client product, not a Worklayer product and not a company I founded. My role was to help shape, build, and turn it into a focused B2B AI pre-screening interview platform.

That work included product strategy, workflow design, AI interview logic, candidate experience, recruiter reports, and the practical product decisions needed to move from an idea to a working B2B SaaS workflow.

The client product did not start as a B2B pre-screening product.

It started as a hiring marketplace.

The original idea was simple: candidates should be able to show more than a CV, and companies should be able to understand more than a LinkedIn profile before the first call. A candidate could spend 15 minutes talking about motivation, experience, expectations, and questions about the role. A company could review richer candidate context before deciding whom to invite forward.

On paper, it made sense.

In reality, the marketplace was too wide as a starting point.

This is the story of how I helped move MyPitch from a two-sided hiring marketplace into a focused B2B AI pre-screening product, why that pivot made the product stronger, and what this client project shows about the kind of AI-native product work I do through Worklayer.

The client brief: a hiring marketplace where candidates had a voice

The first version of the MyPitch product tried to solve a real problem in hiring.

Most candidates are reduced to a document. A CV can show titles, dates, skills, and a few bullet points. It rarely shows how a person thinks, why they want the role, what tradeoffs they care about, or which questions they ask when they actually understand the job.

For companies, this creates another problem. Recruiters often have too many applicants to review deeply. Hiring managers get resumes and notes, but not enough comparable evidence. Good candidates can disappear in the pile because nobody had time to speak with them early.

MyPitch marketplace was built around a better loop:

A company publishes a role.
A candidate applies with a profile and a 15-minute AI pitch.
The recruiter reviews video, transcript, summary, and ranked insights.
The company sees more candidate context before the first human interview.
The candidate gets a chance to tell the story behind the CV.

That direction still matters to me.

But a marketplace is not one product problem. It is several problems at once.

You need companies. You need candidates. You need liquidity for each role. You need trust from both sides. You need enough supply before demand gives up, and enough demand before supply loses interest. You need positioning for recruiters, founders, hiring managers, and candidates at the same time.

The product idea was good, but the entry point was too broad.

The real bottleneck was before the first recruiter call

The more I worked on the client product, the more obvious the strongest part became.

The value was not "another job board."

The value was the first screening layer.

In many hiring processes, the first recruiter screen is not meant to make the final decision. It usually answers practical questions:

Does the candidate understand the role?
Is the salary range aligned?
Is the person open to the work format?
Can they work from the office if needed?
Do they have the basic experience required?
Does their motivation fit the role?
Are there risks the hiring manager should clarify later?
What questions does the candidate ask about the company?

When there are 10 candidates, this is manageable.

When there are 100 candidates, it becomes a capacity problem.

The company has a few options, and most of them are bad. Recruiters can work more hours. The company can hire more recruiters. The team can screen fewer people. Or strong candidates can be missed because the process only has enough time for the first slice of the applicant pool.

That is where MyPitch - AI Interview Platform for Candidate Screening became much more focused:

Help hiring teams run AI pre-screening interviews so they can review more candidates without adding recruiter headcount.

This was a smaller idea than a marketplace.

It was also a much more useful product.

What MyPitch does today

MyPitch is an AI screening layer for hiring teams.

A recruiter creates a role, defines the interview structure, adds questions or assessment criteria, and invites candidates manually, through CSV upload, or through a common job link. Candidates complete browser-based AI video interviews on their own schedule.

After the interview, the recruiter receives a structured report:

Go, Doubt, or No-Go as an advisory recommendation;
strengths;
risks and follow-up points;
per-question analysis;
full transcript;
interview recording;
share links for hiring managers.

The important part is control.

MyPitch does not make the hiring decision. It does not replace the recruiter or hiring manager. The AI output is advisory. The human hiring team keeps decision authority.

That distinction matters because AI in hiring becomes dangerous when it is positioned as a black-box judge. I believe the better role for AI is to collect structured evidence, reduce repetitive screening work, and help humans make the next decision faster.

Use case 1: structured AI pre-screening for high-volume roles

The first MyPitch use case is structured pre-screening.

This is useful when a company already has a recruiting process, but candidate volume is too high for manual phone screens.

For example, a company needs to hire many people for the same role. The team already knows the basic questions:

Are you comfortable with this salary range?
Can you work from the office?
Why are you interested in this role?
What relevant experience do you have?
What kind of team environment do you work well in?
What would you need to clarify before moving forward?

Normally, each candidate would require scheduling, a short call, notes, and a handoff to the hiring manager. None of these tasks is hard by itself. Together, they consume a lot of recruiter time.

In MyPitch, the company can turn this into a structured AI pre-screening interview.

Candidates complete the interview asynchronously. At the end, they can also ask questions about the company or the role. If the company has uploaded role and company materials, MyPitch can answer from that context and keep those candidate questions visible to the recruiter.

The output is not a raw AI conversation.

The output is a recruiter-ready report that can be reviewed quickly, checked against transcript and recording, and shared with the hiring manager.

This is valuable when the company wants to give every candidate a chance to speak, but cannot manually interview everyone.

Use case 2: live AI interviews for smaller teams

The second use case is live AI interviewing.

Some companies do not have a recruiting department. A startup founder may be hiring directly. A small company may need to hire for a role it has never hired before. A team may need to evaluate candidates for a function where nobody internally has a strong interview structure.

In that case, a static checklist is not enough.

The interview needs goals. It needs follow-up questions. It needs to adapt to the candidate's answers. It needs to understand what the company is trying to learn without pretending to be a final judge.

In this mode, MyPitch can conduct a live interview around a defined objective:

what the company needs to understand;
which signals matter;
where the AI should ask follow-ups;
which topics should be treated carefully;
what should be left for a human interviewer.

The result is similar: recording, transcript, strengths, risks, red flags, suggested follow-ups, and an advisory recommendation.

For small teams, this makes the first interview more structured without requiring a full recruiting function.

The Worklayer lesson: AI needs business context, not just prompts

This is where MyPitch connects directly to my work on Worklayer.

The biggest lesson from building MyPitch is that AI becomes useful when it has the right context and a defined workflow.

A blank prompt is not enough.

For a recruiting product, the AI needs role context, company context, question structure, candidate answers, transcript evidence, decision guardrails, and a report format that humans can actually use.

The same is true in business work more broadly.

That is why I am building Worklayer: an AI-native workspace where company context, documents, tasks, templates, connected tools, and AI workflows can live in one operating layer.

MyPitch is a case study in the same belief.

AI should not be treated as magic. It should be connected to a real job-to-be-done.

In MyPitch, the job is not "use AI for hiring." The job is:

Help a hiring team process more first-round candidates with structured evidence while keeping humans in control.

That level of specificity changed the product.

How I built the client product with AI-native execution

MyPitch was built with a modern AI-assisted development workflow that many people now call vibe coding.

I use the term carefully.

Vibe coding does not mean asking AI to randomly generate an app. At least, that is not how I use it.

For me, AI-native product development means moving quickly between product thinking and implementation:

Define the user problem.
Build the smallest useful workflow.
Test it against real business context.
Read the output like a user would.
Fix the product behavior.
Improve the prompts, report structure, UX, and edge cases.
Repeat until the workflow works in a real setting.

AI helps with speed, but speed alone is not the advantage.

The advantage is learning faster.

When I build this way for a client, I can move from "I think this is the bottleneck" to "a customer used this workflow for more than 100 interviews" much faster than a traditional small team could.

But it still requires judgment. If the product direction is vague, AI only helps you build the wrong thing faster. If the role context is weak, the interview gets weaker. If the report is too generic, recruiters will not trust it. If the candidate experience feels unclear, people will drop off.

I still had to own delivery quality: product logic, user flow, report usefulness, candidate experience, and whether the final workflow solved a real business problem.

A real customer proof point

One of the most important proof points for MyPitch came from a real hiring workflow.

In one customer account, a recruiting team of three people used the platform to run more than 100 pre-screening interviews in about a month and a half. Over that period, they closed 10 positions.

That is the kind of proof I care about.

Not a polished demo.

Not a landing page claim.

A real team using the product to handle real candidate volume.

The lesson was not that MyPitch solved everything. Real usage immediately exposes the next layer of work:

reports need to be more specific;
candidate setup needs to be clear;
role context needs to be stronger;
hiring managers need evidence, not just summaries;
AI recommendations need careful wording;
recruiters need to see where a follow-up conversation is required.

But this is exactly why customer usage matters. It turns the roadmap from a list of ideas into a list of specific workflow improvements.

What I learned about AI recruiting products

I do not believe the best AI recruiting products will win by saying "AI will hire people for you."

That is the wrong promise.

The better promise is more practical:

AI can help hiring teams collect structured early evidence, reduce repetitive screening work, give more candidates a chance to speak, and make human review faster.

For recruiters, this means less repetitive scheduling and note-taking.

For hiring managers, it means better candidate context before spending interview time.

For candidates, it means a chance to explain motivation, expectations, and fit beyond a static CV.

For companies, it means more screening capacity without immediately expanding the recruiting team.

That is the category I believe MyPitch should occupy: not a full ATS, not a black-box hiring engine, but an AI pre-screening interview layer.

Why I recommended the move from marketplace to B2B SaaS

The pivot from marketplace to B2B SaaS was not a rejection of the client's original idea.

It was a sharper version of it.

The original MyPitch belief was:

Candidates should be able to show more than a CV, and companies should be able to review richer candidate context.

That belief is still there.

The difference is the entry point.

The marketplace tried to create a new market loop from both sides at once. The B2B product enters an existing workflow where the pain is already visible:

companies already have roles;
companies already have applicants;
recruiters already have screening overload;
hiring managers already need better evidence;
candidate volume already creates a bottleneck.

That made the product easier to explain, easier to test, and easier to measure.

It also made the business more honest.

What this says about working with me through Worklayer

MyPitch is not the only product I have built this way for a client.

But it is one of the clearest examples of how I think about AI-native execution at Worklayer Agency.

The pattern is consistent:

Start with a real business workflow.
Identify the bottleneck.
Use AI where it creates leverage.
Keep humans in control where judgment matters.
Build enough product to test the workflow.
Use real context and real usage to improve it.

This is what I mean when I say Worklayer is about AI-native work.

Not hype.

Not "AI transformation" as a slogan.

Practical systems that help a business move faster, reduce repetitive work, and learn from real usage.

MyPitch is one client case. The broader Worklayer mission is to help more companies operate this way.

What comes next for MyPitch

The next stage is not to make the AI sound more impressive.

The next stage is to make the screening workflow more useful for real hiring teams.

That means:

better interview setup for each role;
stronger role and company context;
clearer candidate disclosure;
more useful candidate Q&A;
better hiring manager share links;
more evidence-backed reports;
careful tracking of completed interviews, report views, and hiring outcomes.

If you are evaluating AI recruiting software, I would look less at whether the demo feels futuristic and more at whether the workflow respects the actual hiring process.

Can the recruiter configure the interview?

Can the candidate understand what is happening?

Can the hiring manager verify the evidence?

Does the product preserve human decision control?

Does it help the team review more candidates without making the process feel careless?

Those are the questions that matter.

Final thought

I worked on MyPitch because the client product had a strong underlying belief: candidates should have a better way to be understood.

I helped turn it into MyPitch - AI Interview Platform for Candidate Screening because companies had a more urgent, measurable problem: they needed to run more first-round screening without losing quality or hiring more recruiters immediately.

That is the kind of product lesson I care about as the founder and creator of Worklayer.

A good AI product is not the one with the broadest promise.

It is the one that finds the real workflow, understands the real bottleneck, and helps people do the work better.

FAQ

What is MyPitch?

MyPitch - AI Interview Platform for Candidate Screening is an AI interview platform for candidate pre-screening. It helps hiring teams run browser-based AI video interviews, generate structured candidate reports, review transcripts and recordings, and share evidence with hiring managers.

Did Sergii Alekseev found MyPitch?

No. MyPitch is a client product. I worked on it as the founder and creator of Worklayer, helping shape and build the product into a focused AI pre-screening interview platform.

Is MyPitch an ATS?

No. MyPitch is not positioned as a full applicant tracking system. It is a screening layer that works before deeper recruiter or hiring manager interviews.

Does MyPitch make hiring decisions automatically?

No. MyPitch provides advisory Go, Doubt, or No-Go recommendations with evidence. The human hiring team keeps final decision authority.

How does this connect to Worklayer?

Worklayer is my broader company and product direction around AI-native work. MyPitch is a concrete client case study of the same approach: use AI with structured business context, connect it to a real workflow, and keep human judgment where it matters.

Who is this article for?

This article is for founders, recruiters, hiring managers, operators, and business leaders who are exploring AI pre-screening interviews, AI recruiting software, or practical AI-native product development.