PoachMe.AI: A Job Match That Works

PoachMe.AI is a two-sided platform that effortlessly connects employers with latent job seekers while promoting a desirable flow of talent between firms.


For jobseekers:            Don’t chase your dream job – let it find you.

For employers:            Trade your star “pitchers” for their star “hitters”.

Generic:                       Do not take a chance – take a match that works. PoachMe.AI

Value Proposition

PoachMe.AI is a two-sided platform that effortlessly connects employers with latent job seekers while promoting a desirable flow of talent between firms. Powered by advanced people analytics, the platform uses past performance review data (in an obfuscated and anonymized fashion) to take the guesswork out of the job match-making to make matches that work.

The Problem

Jane Doe has been working for the same company for 5 years. Jane feels that she has been doing a good job for her current employer but no real opportunities for growth have been coming her way lately. Her employer values her but she feels that she has been pigeonholed into her current job and her manager is uninspiring. She is hoping that the right opportunity would come her way one day, but between home and work, she has very little time to invest in networking, reviewing job postings, and applying for open positions. Even if an attractive job posting comes her way, then if she applies, aces all of her interviews, and gets an offer, she is still not sure that the new job and the new manager would prove out to be a better match than the current ones and, before she knows it, she may need to start looking again.

ACME corporation, which employs Jane, has a different dilemma. ACME has many talented employees like Jane who have over the years developed ACME-specific skill set. But ACME also realizes that the skill set it has been so good at developing is somewhat lopsided and is not diverse enough. ACME has been great at developing “pitchers” (like Jane), but it needs “hitters” too and would not mind trading some of its excellent, but plentiful, “pitchers” for some skilled, but scarce, “hitters” from the outside. To make the matters worse, many perceived pitchers that ACME has hired turned out to be “shortstops”.

Of course, Jane’s and ACME’s story is a lot more complicated to solve than the same type of dilemma in professional baseball. Jane is not an all-star baseball player with a multimillion-dollar salary and an agent, her job and skill sets are a lot less standardized than those of a baseball star. The labor market for Jane’s talents is extremely inefficient. Because of this inefficiency, many Janes end up being laid off after stagnating in their career for years and many ACME corporations go out of business because of the obsolescence, lopsidedness, and lack of diversity in their work force.

Sports, however, provide us with an apt metaphor that will serve us as a guiding star in helping both Jane and ACME. We believe that a properly designed marketplace built on top of an advanced analytics platform powered by the proprietary employee performance data contains the key to Jane’s and ACME’s success.

The Solution

Enter PoachMe.AI marketplace and analytics platform.

We can think of this marketplace as a league of teams (or employers) who are willing to trade their players (or employees). These employers will be contributing their employee’s historical performance data (raw performance data) to the analytics platform.

An analytics platform will distill raw performance data into a set of analytics using advanced NLP and AI algorithms. Derived analytics will completely obfuscate any personally identifiable information contained in the raw data.

Anonymized derived analytics will be published on the platform subject to employee consent. These analytics will be browsable and searchable by both participating employers and employees. Employers will be able to push job postings to the desired anonymous candidates and it will be up to the employee to respond. Employers and 3rd parties will also be able to push training, seminar, and certification offers to the employees.

The revenue model will consist of employers paying a nominal fee when pushing job posting to their desired candidate (which will also prevent spam and abuse). The service subscription fee will also be charged from the participating employers.

Advantages for Employers:

  • No more dealing with self-serving, exaggerating resumes. No more reading on the tea leaves of your interview impressions and your colleague’s interview notes. Get the true historical performance record of a candidate thru the prism of derived performance analytics summarizing their skills, abilities, and achievements.
  • Get access to a massive latent pool of candidates and find your true gem candidates reliably and quickly.
  • Provide your employees with timely, necessary, and appropriate training that would enhance their skills and would push their careers in a desirable and fulfilling direction.

Advantages for Employees:

  • No more being frustrated with stagnation at your current employer or with your current manager. No more spending endless hours reading thru endless job postings searching for a needle in a haystack. No more tweaking your resume trying to impress the imaginary hiring manager. Let your perfect job find you and let your actual performance record speak for itself!
  • Get timely and necessary training offers to enhance your skill sets, differentiate yourself from the crowd, and to push your career to a new level.



  • It might be hard to entice an initial critical mass of employers to join the platform.
  • Employer’s participation in the marketplace may appear as encouragement of employee turnover, which might contradict stated goals of employee retention. Special reservations may arise around highly valued and “key man risk” employees.
  • Some employers will prefer infrequent yet severe pains of large involuntary layoffs to a more sustained yet less severe pain of increased voluntary turnover.
  • Platform development relies on the ability to access a large training set of historical employee performance data, which is private information that employers will be reluctant to share with a third party.
  • Performance data will likely be influenced by biases of the original data creators: managers, peer reviewers, and employees. Although it is likely to provide a more balanced perspective vis-à-vis a traditional resume, the data will not be bias-free.
  • Deriving desired analytics will a be highly experimental task with high uncertainty of success. Such an analytics set would have to reliably replace a resume while ranking a person on a myriad of yet unknown dimensions. Even if such an analytics set can be derived, it is not clear whether employee anonymity can be reliably preserved.
  • Given the above-described technical complexity, we may have difficulty identifying and attracting individuals uniquely skilled for the implementation of the project.
  • Even if we solve all the above challenges, employees with the target employers may refuse to provide consent to publish their derived analytics, thus undermining all the previous investment.


Bonus feature: watch this Malcolm Gladwell’s talk on our propensity to misread strangers and (starting at minute 24:00) how this misreading mistake renders the job interview process virtually useless.


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