The Two-Product Trick That Beats a Better Product Every Time

01 /The Concept

MirWork built Quick Mock – a free tool that lets anyone practice job interview preparation with an AI interviewer.

To get started, users install Quick Mock as a Chrome browser extension.

Once installed, when a user opens any job listing on LinkedIn, a new "Quick Mock" button appears directly in the interface. One click starts a mock interview tailored specifically to that role.

The key mechanic: the AI reads all the requirements stated in the job posting and structures the interview to probe the candidate on each of those requirements specifically.

The interview runs as a voice conversation – the way real interviews actually work. The AI asks questions out loud and listens to spoken responses.

Afterward, the user gets a structured debrief: a score on a five-point scale, a written explanation of that score, an assessment of how well they met each requirement in the posting, a competency chart, and a full transcript of the interview for detailed review.

Information about MirWork appeared on Product Hunt recently. The startup is part of both Google and Microsoft startup programs, though it hasn't raised external investment yet.

02 /Why It Matters

Here's the non-obvious part: MirWork is not actually building a product for candidates. It's building for recruiters.

The same AI that runs mock interviews for job seekers can be deployed by recruiting teams to conduct first-round screening interviews at scale – fed the job description, it outputs a structured report on how well each candidate meets the stated requirements.

This matters because first-round screening is where recruiting time goes to die. According to MirWork's analysis, of a typical 100-hour hiring cycle: 15 hours go to building the candidate funnel, 50 hours to first-round interviews, 20 hours to second-round, 10 hours to final interviews, and 5 hours to offer preparation. Half the time is screening.

With MirWork, that 50-hour block collapses to 5 – enough to review AI-generated reports rather than sit through the interviews themselves.

There's another layer to the model: Quick Mock users are, by definition, people actively applying to specific roles. Nothing stops a candidate, after a strong mock interview, from clicking one more button to send their report to the company that posted the job. The company receives a self-initiated, pre-screened applicant with a detailed evaluation attached – meaning MirWork can effectively extend a company's inbound funnel from its own user base.

This could significantly increase the quality and volume of applications for participating companies: candidates who apply via Quick Mock have already passed an initial filter and arrive with supporting data recruiters can use to compare them immediately against others.

The result is an engine that powers two distinct products from a single AI – the consumer-facing Quick Mock and the enterprise-facing MirWork. Quick Mock builds brand awareness and generates a candidate pool; MirWork monetizes that pool through B2B sales to companies hiring from it.

A different startup pulled the same structural trick in a completely different domain. Ajust, [covered last month](/review/udivitelnyj-sposob-zavoevat-ljubov-klienta), built an AI tool that helps consumers draft and submit effective complaints against companies – targeting Australians unhappy with telecom providers, airlines, internet companies, and retailers.

For consumers, it's free. Ajust makes money selling the same AI to those same companies for embedding in their own websites and apps – making it easier for customers to file complaints.

Operationally, structured complaints are faster to resolve than vague, frustrated messages. But the deeper effect is counterintuitive: every successfully resolved complaint – even a small gesture of compensation and apology – measurably improves customer satisfaction scores.

Companies that handle complaints well are liked better than companies that simply avoid problems. Which means every business should probably learn to make complaints work for them.

Ajust identifies the companies most complained about through its consumer tool and then targets exactly those companies with its B2B pitch. The consumer product is both a marketing channel and a lead qualification engine.

MirWork can run a parallel play: the list of companies whose job postings attract Quick Mock users is, by definition, a list of companies actively hiring – and likely interested in a better screening tool. Especially if those companies are already receiving unsolicited applicant reports from Quick Mock users, and noticing the quality.

03 /Opportunities

There's nothing technically unique about AI interview tools or AI complaint drafting. There's nothing unique about most products from most startups. Which means the primary competitive lever isn't the product – it's the go-to-market.

That's what makes MirWork and Ajust interesting. Both figured out how to use a single piece of technology to run a B2C product that actively drives a B2B product. The consumer side builds brand, generates data, and creates the pipeline that the business side monetizes.

Because these two startups operate in completely different categories, the implication is that this structural trick can be applied almost anywhere – including whatever domain you're working in.

The pattern is transferable to almost any market: identify a problem that both consumers and businesses have, build the consumer-facing solution as a free or low-cost acquisition channel, and monetize the aggregated demand and data through an enterprise product. The connection between the two products is what makes the moat – competitors who build only the B2B side face a cold-start problem, while those who only build the B2C side leave the highest-value revenue on the table.

Good answers to those questions can fundamentally change a startup's go-to-market strategy – and potentially create a durable advantage over competitors building functionally identical products.