Quantum Talent

At Quantum Talent, we envision a world where unskilled individuals can access good jobs as easily as finding a cab with Uber, and firms can hire the talent they need in the same manner.

We want to use our technology to empower the millions of workers who subsist at the fringes of the labor market in spite of their potential, and direct them onto a path of self-improvement and growth. We want to help companies tap into the most underutilized asset in Latin America: its people.

With our AI engine, we want to place 150 million people in Latin America in jobs where they can be successful, and transform the productivity of firms.

The future looks grim for the 150 million workers in Latin America who are currently unemployed or employed informally. These are poorly educated young people, who are not acquiring the skills required by the digital economy.

This employment crisis has profound consequences. Youths are turning towards precarious jobs with low income levels, short job tenures, and high vulnerability. Labor informality reaches over 80% of the young in some countries. Evidence shows that once these individuals take an informal job, they are likely to stagnate.

These young workers start with a skills gap that can be breached if they have the right job. Research shows that on-the-job learning is the most important source of skills acquisition for people without higher education.

But hiring practices hinder that. Firms use outdated methods plagued by bias and discrimination, leading to insufficient opportunities for the unskilled and underprivileged.

The candidates also lack the knowledge to navigate the job market successfully. 
Because the recruiting process is broken, individuals face long and frustrating job searches, finally settling for informal employment.

We serve people like Orfe, a 22-year-old Peruvian woman who was the first person in her family to finish high school. Orfe could only find employment as a maid. Yet like most young people, she can learn to perform in a superior job. Thanks to our technology, Orfe was hired by a large company, then trained and promoted. Her life has changed.

Quantum Talent targets the 150 million workers in Latin America who are either unemployed or employed informally, like Orfe.

Most precarious workers lack the toolset required by the modern economy because they are unable to access jobs where they can develop it. It's a vicious circle of exclusion.

Such  opportunities remain elusive because when employers interview someone like Orfe, they see a story of deprivation instead of potential. In a region like Latin America--the most unequal in the world--race, gender, and poverty are drivers of widespread discrimination and exclusion. Also, Orfe doesn't know how to look for a job and in which ones she could be relatively good at.

To include people like Orfe in the digital economy, we need to change the way in which they can find and access better jobs.

Our solution works in three steps. First, we measure candidates' hard and soft skills with a state-of-the-art online psychometric assessment based on the work of Nobel laureate James Heckman. Then, we process that data in real-time with a proprietary machine learning engine based on key insights from the field of behavioral economics. Finally, we are able to predict the fit between candidates and jobs with high precision. We share our predictions with firms and candidates, generating good matches instantly. 

The result is a two-sided matching platform, with firms on one side and candidates on the other.

Our business model is simple: we charge firms for each candidate they screen with our software. Our value proposition for firms is powerful: our SaaS with AI predicts the fit between candidates and jobs in minutes, making recruiting 5x faster, 10x cheaper, and 50% more effective.

Our value proposition for candidates is compelling: take a few minutes to complete our online assessment and you will be immediately matched with a suitable job. No more frustrating and drawn-out application processes to finally settle on a poor job.

We tap into the economies of scale of SaaS, machine learning, and network effects to make our business scalable. The more companies that use our technology to hire, the more attractive it is for candidates to take our assessment; and the more candidates that take our assessment, the more attractive it is for companies to use our technology to hire.

Our main competitors are traditional staffing companies like Adecco or Manpower. Potential competitors include other companies in the HR tech space--such as online job boards and recruiting bots. 

Our competitive advantage rests on three factors. The first is our proprietary machine learning engine, which is based on the scientific insights of two Nobel laureates, James Heckman and Richard Thaler. We combine advanced social science research with cutting-edge AI to produce high predictive accuracy that is difficult to match. 

The second is the huge amount of feedback data we are collecting. It allows us to keep improving our algorithms and makes it even more difficult for other potential entrants to replicate our precision. 

The third is the design of our business model as a matching platform with strong network effects. Using our platform is very compelling for both firms and candidates, compared to those of potential entrants with less users.

Quantum Talent is, at its core, an AI company with a social focus. Our solution consists of a SaaS powered by a proprietary machine learning engine.

Our machine learning algorithms consume two types of data: the skills measurements of candidates--input data--and their tenure once hired--feedback data. Input data is used by our algorithms to generate immediate predictions that firms and candidates can use to make better decisions. ¬†Companies send us feedback data regularly so our algorithms continuously learn from past mistakes and keep improving their precision. We use supervised classification algorithms with different types of machine learning models: from neural networks to boosting methods. 

Both candidates and HR teams access our platform through a SaaS web app that is device-agnostic and highly scalable. Our web app has been optimized to load quickly in the simplest smartphones and minimize data consumption, improving accessibility. Candidates can access jobs anytime, anywhere. We have optimized our infrastructure using state-of-the-art auto-scaling cloud architecture. We have the capacity to efficiently cater to millions of users in Latin America.

All our technology--including our machine learning engine--was entirely developed by our team, and all the components of our software are fully operational in five countries in Latin America. 

We are working to use unstructured data--such as audio, text and video--to create new ways to predict job-person fit and automate the recruiting process even more.

Our technology has matched over 40,000 people with jobs in the modern economy where they can learn and grow. This figure is quickly expanding; our platform grows at an average of 15% per month. We are reaching individuals in Peru, Mexico, Chile, Argentina and Colombia.

Quantum Talent’s technology removes bias in the hiring process and improves equality of opportunity. When compared to traditional hiring methods, our algorithm recommends 20% more women.

We deliver powerful impacts on our beneficiaries’ employment outcomes. The success rate of the candidates matched with our program is on average 50% higher than that of candidates matched with traditional methods. We measure success by work tenure, since tenure is highly correlated with performance, skill acquisition and job satisfaction.

Our matching process shortens the job search for candidates by 80%. It also leads to strong increases in wages. In Peru, for instance, the average salary offered by firms using our platform is 30% higher than the average for work in the informal economy. Our technology is allowing a segment of the workforce which struggles with high turnover, low income levels, and prolonged hiring processes to access stable and higher-paying jobs in a fraction of the time

Our technology has matched over 40,000 people with modern-economy jobs where they can learn and grow. This figure is quickly expanding; our platform grows at an average of 15% per month. We are reaching individuals in Peru, Mexico, Chile, Argentina and Colombia.

Quantum Talent's technology removes bias in the hiring process and improves equality of opportunity. When compared to traditional hiring methods, our algorithm recommends 20% more women. 

We deliver powerful impacts on our beneficiaries' employment outcomes. The success rate of the candidates matched with our program is on average 50% higher than that of candidates matched with traditional methods. We measure success by work tenure, since tenure is highly correlated with performance, skill acquisition and job satisfaction.

Our matching process shortens the job search for candidates by 80%. It also leads to strong increases in wages. In Peru, for instance, the average salary offered by firms using our platform is 30% higher than the average for work in the informal economy. Our technology is allowing a segment of the workforce which struggles with high turnover, low income levels, and prolonged hiring processes to access stable and higher-paying jobs in a fraction of the time.

We aim to grow by 16% on a month-on-month basis. Revenue growth is the result of the value we add to both companies and candidates, and allows us to generate more cash to scale our impact. 

We aim to reach 1 million candidates this year and 10 million candidates in the next 5 years.

Our main internal risk is the development of our go-to market model. We have demonstrated excellent product-market fit. Yet in order to scale and achieve the degree of impact we aspire, we need reach a very large number of candidates and acquire an immense number of clients while maintaining a low cost of customer acquisition.

Our main external risk is potential aversion towards innovation in the Latin American HR space. Hiring processes in Latin America remain outdated, and technological solutions are seldom adapted in lieu of more labor-intensive, pen-and-paper hiring strategies. As a result, HR staff in Latin American companies may see our solution as a threat to their jobs and block its adoption.

To mitigate our internal risk, we will experiment with different customer acquisition models and a utilize very disciplined execution. To manage our external risk, we present our technology as a complement to, rather than a replacement of, managerial discretion. We believe our technology has proven enormous potential to enhance the capabilities of HR staff. Our leitmotif is that while an algorithm can beat a human, nothing beats a human with an algorithm.

We have a small team of 20 full time employees, comprised of software engineers, data scientists, product managers, designers and customer success representatives.

Our organization is based in Lima, Peru. We also operate in Mexico, Chile, Colombia and Argentina. 

We are divided into two main teams. The first caters to companies' needs, ensuring they get the talent they require in the easiest, fastest and most efficient way possible. The second caters to candidates' needs, ensuring they can access jobs in which they can thrive with insuperable ease.

What uniquely positions us to achieve our mission is a virtuous combination of three elements that reinforce each other: strategy, talent, and culture. To execute our strategy seamlessly and make the right changes in the right time we need the best talent. What allows us to attract superbly talented people is a combination of our purpose and our culture. To fully exploit the abilities of our team, our work environment is characterized by autonomy, alignment and collaboration, the three pillars of our culture. That culture is strengthened by the commitment and motivation of our team.

That is how we attract some of the best technical talent in the region. For example, one of the highest-ranking Latin Americans in Kaggle (the data challenge platform) is part of our data science team, and one of our software engineers was a global finalist in the ACM-ICPC programming competition.

Quantum Talent has been recognized by Nvidia as one of the startups that are revolutionizing the field of artificial intelligence, by BlueBox and Everis as one of the most innovative tech startups in Latin America, by Endeavor as part of its Scale-Up program, and by Blackbox for its highly selective Connect program in San Francisco. We also won Peru's national startup prize twice. We leveraged such recognitions with press coverage, achieving greater awareness on the impact and benefit of our technology, and gaining traction to expand our platform to more candidates and clients.

At Quantum Talent, we harness a simple yet powerful business model. We charge firms a monthly fee based on how many candidates they assess with our technology. This means we immediately generate cash flow with every new client. Our unit economics are strong: we have gross margins of 80%, and our customer lifetime value is 10x our customer acquisition cost. 

The scalability of our business model also benefits from the economies of scale in machine learning and network effects. As more companies include our technology as part of their hiring prices, using our platform becomes more attractive to the candidate. Similarly, as we recruit more candidates, our value-add to companies increases.

The use of machine learning also means that our algorithms predict more precisely as companies supply greater amounts of data. This provides firms with incentives to continue using our product, and has led to high retention rates.

At Quantum Talent, we harness a simple yet powerful business model. We charge firms a monthly fee based on how many candidates they assess with our technology. This means we immediately generate cash flow with every new client. Our unit economics are strong: we have gross margins of 80%, and our customer lifetime value is 10x our customer acquisition cost.

The scalability of our business model also benefits from the economies of scale in machine learning and network effects. As more companies include our technology as part of their hiring prices, using our platform becomes more attractive to the candidate. Similarly, as we recruit more candidates, our value-add to companies increases.

Our main investments in the next two years are technology and growth. We need to develop the capabilities to make predictions based on new types of data (audio, video, text) to make it even easier for candidates to access better jobs. This requires an investment in personnel (two machine learning engineers specialized in audio and video). 

To achieve our goal of growing at 18% per month and keep client acquisition costs low requires complex planning. We need to experiment with different customer acquisition tactics to find the most efficient. If selected as a Solver, we would use part of the prize funding to conduct this experimentation.

University based research centers focused in human capital economics, psychometrics, machine learning, and behavioral economics. These organizations can help us connect to cutting edge research relevant for our solution and strengthen our technology. Also, we can share data with researchers at these institutions to collaborate and find better ways to predict job-person fit. 

We would utilize the prize money to improve our AI engine in two ways. The first is to find better ways to extract more information from small data sets. We have been researching novel ways of achieving high predictive accuracy and reducing overfitting with small data sets, but we need to keep investing in this area. The second is to explore new types of input data that can be used to predict job-person fit: text, audio, and video.