Reskillme: Better work through better learning

We use technology to better understand you and match you to jobs based on what you know and learn

Problems

Jobs are being automated away and people need to learn skills to adapt to new jobs. The future will require us to be more adaptable and to be learning constantly to stay employed and find better jobs. This is true for blue and white collar workers. There is currently no great way to truly capture who a person is and match them to great job fits. Additionally:

1. Job seekers with non-traditional backgrounds, lack of specific experience and missing hard skills have a hard time finding work even if they would be great candidates

2. Old-fashioned data matching systems look at a finite set of keywords for hard skills data which may not be good indicators of strong on-the-job performance.

3. On-the-job workers may not perform well due to missing soft and hard skills, which may be a result of a mediocre candidate selection process and challenges identifying training needs. These workers are generally less productive and often less happy.

4. Managers have a difficult time understanding why workers underperform and why they leave the company.

Solution

Reskillme has 3 components that solve all the problems above.

1. We capture richer data about a job seeker / existing worker and about someone who would be successful at a specific job, using conversational AI.

2. Our AI Engine processes the profiles of people and jobs to identify best fits as well as gaps in hard skills, soft skills, interests, etc.

3. We recommend learning options to fill skills gaps before hiring, during an apprenticeship or on-the-job.

Our solution greatly improves candidate selection at the top of the funnel, so better suited candidates are available for consideration. Because much more information is processed prior to interviews, candidates can go directly to an interview with the hiring manager, which can cut the time to find and hire a person by 50% or more.

Job seekers have the opportunity to learn skills targeted to the jobs they seek and to rule out jobs that don’t match their interests or work-style.

Since soft skills are often more important to success at a job than hard skills, a manager can hire a strong candidate as an apprentice or on a trial basis while they learn relevant hard skills.

When applied internally to an organization, Reskillme allows organizations to identify existing skills, determine specific training needs and to set future learning plans, making employees more productive, happier and increasing retention.

Impact

Reskillme is combining learning and work to create an  adaptable workforce that can help people move out of poverty and low paying jobs, even without college degrees. We can help older workers stay in the workforce longer by showing that they are valuable contributors, and this eases the burden on government programs.  

By bringing personalized learning to the individual and using AI to decipher skills gaps and learning needs, we remove obstacles and make learning easier. If learning is embedded in the work process, jobs can change as much as we want them to and people can and will adapt.

Our solution is innovative in 3 ways. 

1) We focus on solving the “finding a great job” problem for the worker or job-seeker (instead of for the company even though they get great benefit) which leads us to 

2) We define a person with many more dimensions than a resume only (currently 8) and then 

3) We use AI/ML/NLP to capture information and match people to jobs.

Our complete solution uses conversational AI to capture information about a person and a job through multiple conversations using Alexa, Google Home or a custom phone app. We use NLP and machine learning to improve the conversation capture and the mapping of data to people and job profiles. The matching engine uses algorithms to evaluate matches of people to jobs and to identify skills gaps and recommend learning options.

Over the next 12 months, we plan to work with a handful of companies to capture the 8 dimensions of their employee skills using forms and/or chatbots. We’ll iterate through the job matching process to find the optimal algorithms and data models. From the employee and organizational skills profiles and gaps, we’ll begin tailoring training and learning recommendations. Our goals are:

• Develop and test capture and modeling of skills for people and jobs

• Develop and test matching algorithm for people and jobs with skills gap identification

• Learn how to apply the process from internal company staff to general job-seekers

Once we have proved the MVP to work within companies, we want to enhance the functionality and extend it to the generally population. Enhanced functionality includes:

• Robust conversational AI engine to capture targeted conversations to build profiles

• More accurate matching of people to jobs

• People recommendations for companies where it goes beyond data

• Richer learning options to meet different learning styles

As a free platform for everyone, people can better present themselves to companies and incorporate continuous targeted learning into everyday life. The impact will be happier people working better jobs, more productivity in companies and a more educated society.

Our initial work is with companies who want to get a better handle on understanding employee skills, interests and preferences to find better fits for internal jobs and projects and to identify and deploy learning and development programs. We will retain these customers using direct sales. When we expand to the general public, we will aggregate existing job information from the web and use advertising, social media and voicebot channels to attract general users to the web, voice and smartphone apps.

We do not have current production customers at this time. We are talking with 3 companies about their workforce and how we can evaluate their skills and match them to positions and learning opportunities.

After the first year, we plan to scale up our support for internal corporate matching to 10 companies and up to 25,000 employees. In addition, we expect to gain 100,000 individual, non-corporate users in the second year and 1,000,000 in 3 years. We believe we can achieve these numbers by controlling initial growth as we prove out the platform and optimize the algorithms. Once this is done, we can scale the free consumer side through increased sales and marketing, while the dedicated corporate team scales more gradually.

We have a successful track record of starting tech companies, hiring and inspiring teams and deploying tech solutions. All 4 founders have been A-list coders in the past who have designed and developed complex out-of-box solutions to benefit corporate and educational customers. Chris has a PhD in AI. Eric and Chris have deep understanding of education, learning pedagogies and elearning and have led development of multiple learning applications. Richard and Chris are object-oriented coders who approach problems differently. Mark is a results-driven tech leader who has volunteered for City Year and is a trustee of the Awesome Foundation. 

Our revenue model includes multiple channels.

• Companies pay to be on the platform to find better matched candidates and to save money through automated prescreening interviews
• Companies pay for placements
• Companies advertise on platform
• Learning partners pay for hot referrals

Since we are creating the largest pool of workers in America with multiple layers of skill categories, companies can find better matches from workers looking for jobs or on-the-job.

We are solving for a giant vision that will have major impact in the Future of Work. We believe MIT Solve can help us connect to the right people to support and develop our vision. At small scale, our solution is only beneficial to individual clients, but at full scale, it can completely change the world and provide more flexible and financially rewarding work for people doing what they are best at and most interested in. 

Ideally, we are marrying up a Think-Tank concept and a Startup to achieve this impact.

It requires funding to dig deeply into a tech solution and incorporate massive amounts of data.

We are hoping MIT Solve will see our vision and help us go big.

Challenges:

- Really strong AI tech people are hard to come by and expensive

- We cannot sustain and scale the development easily without funding

- Our team is older and more experienced which is good for designing and delivering a solution but there is age-discrimination in the startup world (as in the job world)