Enhancing Youth Employability Through Conversational AI

Q: Ginia was born to serve a segment with distinct recruitment needs. How did the company start and what market needs led to its creation?

AV: Ginia started at the Harvard Innovation Lab exploring the idea that the lack of sufficient data on what happens inside schools makes it difficult to solve larger systemic issues. We decided to explore WhatsApp as an agile channel to collect data and ran pilots with Yucatan’s National College of Professional Technical Education (CONALEP). Through this, we realized two things: first, traditional data collection on infrastructure did not solve core problems; second, students were constantly using the channel to ask how to find a job or transition to the workforce. WhatsApp proved to be an incredibly high-engagement channel, with response rates between 50% and 70%, especially among younger generations and lower socioeconomic segments because it does not take phone storage and works without heavy data plans.

AV: After winning a US$5,000 grant from Harvard, we traveled to Yucatan to speak with employers and realized there is a massive communication barrier between new generations and HR departments. It was not a lack of tools, but a failure to create real alignment. To build true technology around this, we paired business strategy with deep technical leadership. We eventually rebranded from Elvia to Ginia, graduated from Harvard, and raised US$1.7 million in a pre-seed round led by Wollef, alongside NFX, Latitude, and Lotux. While other platforms focus on highly operative blue-collar roles, Ginia specifically targets technical high schools and universities to build an educational focus powered by conversational tools.

Q: How does AI enable the creation of a successful match and how does your business model work?

MM: Within Ginia, AI is everything. It handles the entire interaction with the students through specialized chatbots. Instead of making them fill out endless forms, the AI engages them in a natural conversation adjusted to their language and pace to understand their personal data, salary expectations, experience, and long-term career interests. This information is then automatically structured into a standardized curriculum. On the employer and institutional side, we ingest job descriptions through a similar conversational interface. A machine learning algorithm backed by a Large Language Model (LLM) processes both sides to generate precise matching scores. We deliver highly curated shortlists of only five optimal candidates to the employer, avoiding overwhelming lists of 20 or 30 applicants. Structurally, our tech team is composed entirely of product engineers who own the business metrics, revenues, and OKRs while directly managing the AI agents that generate code.

AV: Our B2B business model is divided into two distinct components. For educational institutions, we act as a SaaS platform that charges a license per campus or student group. We do not replace their career service coordinators; we give them data-driven superpowers to track and measure the real employability rate of their graduates at graduation, three months, six months, and one year. For employers, we charge a success fee per hire. Because our process is heavily automated and optimized by technology, this fee is significantly lower than what a traditional headhunter or agency would charge, and companies only pay when a successful match is finalized.

Q: What do your data insights reveal about why the transition from university to employment is broken, and how does Ginia tackle issues like rotation?

AV: The absolute biggest pain point for companies in Mexico is turnover, which ranges from 30% to an astounding 400% annually in the worst cases, generating massive corporate costs. This stems from misalignments in salaries, daily commute distances, clear career paths, and poor onboarding. Furthermore, roughly 57% of recent graduates end up in jobs completely unrelated to their fields of study or career goals, turning entry-level positions into temporary safety nets while they look for something else. Ginia tackles this by initiating detailed profiling conversations before the student even graduates, identifying underlying technical and soft skills that transcend a generic degree title.

MM: To fully automate this matching process and reduce time-to-hire, we are launching a feature called Auto-Applier. Instead of forcing a student to apply manually to positions one by one, our technology maps their profile, web-scrapes all compatible openings, factors in location parameters, adapts their resume dynamically using specific keywords from the job description, and automatically submits applications to up to 100 companies with their prior authorization. This completely removes the administrative burden for candidates while delivering pre-filtered, highly compatible talent to HR teams.

Q: What would you change about the employability ecosystem in Mexico to accelerate access to opportunities?

MM: We would completely reform the educational system to teach AI and technology literacy directly from grade school. Years ago, the obvious answer would have been teaching children how to program, but the rise of generative AI has changed that paradigm. Students need to learn how to actively manage and collaborate with technology so they can seamlessly delegate repetitive task-work to AI systems, freeing human capacity to focus on high-value senior skills: strategy, critical thinking, direction, and analysis.

AV: We must urgently build a real corporate culture around professional internships. In markets like the United States, companies across all sectors implement structured internship projects starting from a student's freshman year, allowing them to gain real-world experience. In Mexico, internships are deeply undervalued; students are often relegated to administrative errands like making coffee or sorting mail, which builds zero practical experience. There is a three-to-one imbalance of students looking for professional practice versus what the market offers. We need an ecosystem where internships are project-based, allowing students to immediately apply classroom theories, like accounting structures, to local business operations.