EWOR AI

Daniel is a passionate ed-tech entrepreneur who seeks to modularize and order the world’s educational content. He’s been recognized as Germany’s, Austria’s and Switzerland’s top talent under 25, is a TEDx speaker, Global Shaper and tech advisor to several corporations (eg Commerzbank).

Daniel has built Kairos Society Europe, a community of distinguished entrepreneurs who tackle some of the world’s most burning challenges. In the past three years, he scaled the community to 110 actively engaged volunteers, 35 directors, and 600 of community members. Kairos Europe has launched over 25 impact-projects through their Summit and non-profit accelerator ImpactGen.

Daniel founded EWOR to modularize, digitize, and democratize education. EWOR has launched successful partnerships with SGS and EBIKE Technologies and aims to become the world’s largest individualized education provider.

Daniel has studied at University of Cambridge, HKUST and University of St. Gallen, where he consistently scored in the highest percentile.

Knowledge work encompasses 1/8th of the population today. Current knowledge workers struggle to stay apace in today’s quickly changing environment, while individuals with limited access to higher education can hardly enter the knowledge working pool. Simultaneously, high-quality educational content is widely available on the web. But instead of being empowered, most individuals are overwhelmed by the breadth of available content. 

EWOR AI aims to create outstanding educational learning journeys by modularizing existing online educational content into content pieces, ordering those through a machine learning algorithm, and packaging them for individuals in a personalized manner.

EWOR AI can assemble personalized skill-transformation journeys for employees, and recent graduates; it can compile high-quality individual learning journeys for university students and learners without access to higher education EWOR AI elevates learners by offering them an education experience that fits their strengths, goals, and personality to make them fit for knowledge work.

Gartner stated in December 2019 that someone, somewhere, started a new job without realizing that they have become the billionth knowledge worker. The global need for knowledge work (which requires higher education) has doubled from 1980 to 2015. Knowledge work bears two challenges: Becoming a knowledge worker and staying one. 

The former problem exists in many developing countries, as university education is time-consuming and expensive. In South Asia, only 21% enroll in higher education, and in Africa, merely 9% of the population has access to higher education. Many factors contribute to this problem including poor primary and secondary education. One significant factor is the high barrier to entry of university degrees. While workers would be able to engage in education part-time, full-time commitments are often not an option. 

The second problem has similar roots. While fortunate employees upskill through corporate-sponsored MBA programs, PhDs or alike, most workers are left behind. The barrier to quitting a job and returning is too high. Yet, constant technological change requires constant upskilling. To counter this, corporations all over the world spend $370 BN on advanced training - an incredibly inefficient and intransparent market, which requires individualisation of learning content per corporation.

EWOR AI is a customizable, user-friendly AI-powered hyper-layer of all available online educational content, which is packaged in individual learning and skill-transformation journeys. We use three core technologies.

Content aggregator: We gather, modularize, and order publicly available education content. Initially, the aggregator focuses on select subjects (computer science, data science, and entrepreneurship). Later, any conceivable subject will be covered. The platform’s early version exclusively aggregates content from select educational content providers, including Google Books, Coursera, YouTube and Khan Academy. Most importantly, EWOR makes sense of the concepts and orders them appropriately.

Preference discovery engine: EWOR ensures that the recommended contents suit our learners’ preferences, abilities, and needs. We approximate individual preferences based on micro-surveys and in-app behavior. Moreover, our engine continuously suggests “lateral” ideas from topics not yet covered. Our application will become every learners’ personal “mentor”: guiding them through the relevant educational content towards their goals.

Track Designer: Our track designer will allow B2B partners (educational institutions and corporates) to host and manage their content. Affiliated learners have exclusive access to “local content” which will seamlessly combined with “global content” in a single, easy-to-use interface.

EWOR AI serves two groups: Life-long learners and individuals without access to higher education. Wary about having to tackle a beachhead market, we conducted a learning program with individuals in a high-paced environment eager about life-long learning: entrepreneurs. Our learners participated in an entrepreneurship education program without any teachers, curricula, or tests. The entire experience involved tailored individual learning journeys.

We engaged these learners over several months in different activities, for instance, by creating map-like structures to visualize entrepreneurship education content. We rigorously tracked and watched people’s engagement with our map.

After the initial program’s success, we realized the next step is an entirely dynamic and personalized education aggregator and recommender system. Our new solution gives learners a tool to cope with the ever-changing world as content is scraped directly from the web and is rated for its usefulness. We built Wizard of Oz Prototypes to test users' reactions. Our learners rated the experience higher than the learning map, and asked for more after completion.

As our learning journeys are adaptive and flexible, they ensure that individuals can continue learning up-to-date, relevant content for ever, especially next to their occupation. EWOR aims to become a life-long education companion.

We believe life-long learning is one of the greatest elevating factors of the 21st century. It will reach the majority of the global population. It will make us more productive globally, create opportunities for those left behind in the traditional education system, and allow everyone to follow their passions.

EWOR AI’s educational content aggregator ensures that education is affordable and accessible. Our preference discovery engine ensures that individuals receive the content that fits them best. This is important because studies found that fit of personality to the subject studied determines final performance to a greater extent than ACT.

I had a hard time in high school because I frequently questioned fundamental processes and principles. It felt as if the teachers tried to fit me in a box. All of this changed when I went to university and studied what I loved. I soon realized how fitting people in boxes was important, as standardization of educational content was necessary to scale the system. But considering today’s vast technological development, I am convinced that we can do better.

I, hence, decided to build a school myself. I built a team of seven, including Alex Grots, the former EU Managing Director of IDEO (whose highest degree is from middle school!), and advisors such as Chris Coleridge, the founder of the MSt in Entrepreneurship at University of Cambridge. Our central hypothesis - all content already exists on the internet; we only have to order and visualize it in the right personalized manner - was confirmed during our prototype: The EWOR fellowship. To reach learners worldwide and abstract further, we welcomed Bernhard Gapp, former BCG consultant, and a team of coders, to shape the vision and product for EWOR AI, our global education content aggregator and order engine.

I always felt fooled by the education system. I went to a mediocre high school and struggled to find a connection between the educational content I consumed and its relevance for real-life applications. I naturally excelled in math but struggled with any subject that required me to memorize facts. During the first half of 10th grade, I had five failing grades out of 13 classes. Eventually, I turned things around during my senior year and later graduated as best of the year from Europe’s leading business university.

Next to studying business, I focused much time on math and computer science-related subjects. I never understood why I had to limit my studies to business topics only. Especially in combination with computer science and statistics, business knowledge became particularly valuable. Moreover, teacher-centered learning and mindless memorization of facts bugged me. It didn’t prepare me for the knowledge work I was deemed to do later on. Hence, I’ve set myself the goal to start my own school, one which leverages the technological advances of the 21st century. The initial model was terrible, but the most recent prototype worked quite well. We’ve learned what we can do exceptionally well: promote and foster life-long learning.

With a Master's degree at the University of Cambridge and a Bachelor's degree at the University of St. Gallen, I have experienced different formal education frames. I've coached business and politics students in Europe, and the Jiangsu Olympic Math team in China. Throughout my coaching, I've learned what motivates people, what the current education system lacks, and where individuals' frustrations lie. I have developed a good intuition backed by quantitative market research, on how life-long education could look like amidst the fast pace of today's world.

Over the last years, I've gained significant leadership experience: from small associations, I've led at university, to turning Kairos Society Europe into a community of 110 committed team members (starting with four active directors) and hundreds of active community members. We're a highly selective group quite different from our US brother and launched an annual Summit and a series of Think Tanks, which have produced over 25 unique projects that address the SDGs. For instance, we have developed a solution for decentralized routing in Paraguay, which is currently being implemented. I have experienced what it takes to lead large teams and organizations, hire and convince talent, develop MVPs, iterate quickly, and scale quickly.

With a Micro-Master's degree in artificial intelligence from Columbia University, a series of technical and statistical courses I took at HKUST, and a series of technical consulting projects I delivered, I have the mathematical and technical understanding to build a functional prototype for the proposed solution.

As we launched our education prototype earlier this year, we had closed a large German corporation as our pilot partner. We had conducted a large application cycle to select a handful of outstanding individuals to test our solution. Two weeks before the kick-off, the corporation stepped out of the discussed process and demanded additional conditions that we could not deliver.

After a short moment of devastation, I called the team – 5 people at that point – and communicated the situation transparently. We developed a series of plans, ranging from Plan A to Plan D, the former was to close a new pilot customer, and the latter - to invent the business cases ourselves and to render the program completely virtual. One was clear to the entire team: No matter what, the pilot will be delivered in some form. The following week was full of phone calls, emails, and planning until late at night. Eventually, we found a pilot partner, E-BIKE International Technologies GmbH, with whom we delivered Plan A. On June 1st, Plan A was brought to completion. This left the entire team encouraged about our unique educational approach, and E-BIKE motivated to conduct another education program with us.

To me, a good leader is not the most aggressive shark in the tank but who collaborates and enables others. Long ago, at London International Model United Nations (mock debate), I led the largest block. I focused on speaking less and empowering everyone in the committee to contribute, especially those that felt insecure. As a consequence, I perceived our group as productive, organized, and quick.

The culture of the other block was brutal, including shouting at each other and backstabbing. They deleted our Google documents and uploaded pre-written draft resolutions to the chairmen's Drive. During their introduction, I decided to step back and voted for two quieter but extremely savvy individuals to be the primary speakers. The shark-like leader represented the other block. Having read our drafts, he introduced our propositions as his. After the committee, to my surprise (I did not speak at the formal debate), I won the committee's award. Afterward, five out of 30 people approached me individually and thanked me for making their experience meaningful. With tears in her eyes, one of our speakers shared that she wanted to quit the United Nations mock debates, but after her shining moment, she decided to continue.

EWOR is different from Coursera or Youtube. Once one has done one course in Python on Coursera, one is offered hundreds of other similar courses, not knowing which contents one has already studied and which concepts one is still missing. EWOR addresses this issue by creating an ontology of canonical content pieces, which cannot overlap.

In contrast, KhanAcademy has a non-overlapping content system, but only for a limited array of learning areas. Even though it is technically possible to globally aggregate all the free educational content across the web and order it appropriately, no such solution exists to-date. EWOR’s approach works as a hyper-layer for educational content that amplifies the reach of existing providers (instead of competing with them). We believe that EWOR will create an entirely new market: Global education content aggregators. With such technology, many things become possible: 

First, individualized learning journeys for every learner: adjusted according to the required depth of content (e.g., KhanAcademy vs. EdX Micro-Masters), learning type (e.g., auditory vs. visual), and a variety of other factors. 

Second, higher return: through tracking one’s complete learning journey, EWOR learners are expected to come back for EWOR content as its preference discovery engine recommends content that fits the individual based on individual preferences and ratings of other users. 

Third, ‘content designers’ can use EWOR to design entire curricula, even entire university degrees, by defining the content concepts which are to be studied. EWOR will automatically find the best educational resources for the defined content.

EWOR AI aims to connect learners with the right educational content at the right time. Through its unique content aggregation and preference discovery engine, EWOR AI presents the individual with a viable personalized learning journey. 

EWOR AI uses the same core technology to address different target groups of learners: 

1. Students who have limited access to education. 
2. Workers who aim to develop relevant skills and stay ahead in a fast-changing world; 
3. Managers and their corporations that seek to equip their teams with the newest relevant skills, and keep reinventing themselves;

The attached Theory of Change visual showcases how EWOR AI would impact the different levels of change across the three target groups. 

We believe that EWOR's long-term outcome results in life-long learning knowledge workers who maximize their potential and job security. We furthermore plan to have democratized education for the ones who did not have access to it. Finally, we will have brought companies to the forefront of innovation successfully. 

With the newly attained knowledge and relevant skills, EWOR's life-long learners can tap into their full potential, contribute effectively within their jobs, and elevate their communities.

While our solution is based on a unified platform, we plan to offer different versions for the distinct customer groups: A free B2C version for learners everywhere, and an affordable B2B version. While the latter will generate cash flow, most scaling will be achieved through our B2C solution:

For our B2C solution, four learners have currently gone through our complete three-month innovation education program, which used the aforementioned learning map and rigorously tracked user behavior. Moreover, forty people have tested the map for our statistical analysis, and we are currently onboarding 100 new learners through partnerships we have closed. Within the next year, we plan to scale to 10,000 learners. The number 10,000 is derived from an analysis of our distribution partners, who reach an audience of 2.5 million people across Europe. Based on previous work with them, we calculated with a conversion rate of 0.004, which results in the numbers above.

Overall only 216M students are enrolled in higher education, which is expected to grow globally to 600M students to 2040. We believe that many of those students will receive their higher education online. EWOR will provide the digital infrastructure to make this transition happen and plans to onboard over 15 million students within the next five years. Hopefully, by catering to both students who prefer flexible, personalized digital education and ones who don't have access to higher education in the first place, we can scale to hundreds of millions in the subsequent decades.

During the next year, we aim to have finalized and validated the EWOR AI platform and to achieve initial adoption. This should lay a good foundation for approaching impact-focused venture capitalists and government grants to fuel our growth. During the next five years, we foresee two unique use cases of the platform.

Our first product will be a free tool (B2C), which helps learners from all over the world compile individual learning journeys. This product stream is built to elevate those without access to higher education and those who struggle with digital literacy. Our goal is to eventually spread the product to hundreds of millions of people in India, Africa, South East Asia, and South America.

Our second product focuses on the global advanced training market, which is ~370 BN $ in volume. Corporations have an increasing need to find relevant content to upskill their employees to stay competitive. Our goal is to close 100 large corporations within the next five years to reach at least one million active users. Thanks to the cash inflow from this stream, we can offer the B2C product for free. 

Our main focus during the next five years is to have achieved significant adoption for both products, consistently improve the platform experience for our users, and have a global reach.This will enable us to build the world’s most adopted education hyper-layer and serve billions of learners.

Barrier 1: Talent

In the immediate future, our main aim is to validate our AI-enabled learning journeys and prototype and validate our learning management system. While we have developed strong hypotheses, it is crucial to answering questions like “What is the optimal user interface?”, “Who are our loyal customers?” and “Are customers willing to pay for this solution?”. We aim to recruit a team of four engineers, one designer, one marketeer, and one commercial analyst by the end of the year to build, iterate, and learn until our solution is fully scalable.

Barrier 2: Access to paying customers

While we drive global adoption and social impact through our free web application for individuals, one of our primary customers on the journey towards financial sustainability are corporations. Especially at the beginning, we know corporations are the most financially promising segment. To succeed, we need to gain access to tier-1 customers. We must convince them to replace current (manual and digital) training solutions with our application. 

Barrier 3: Broad adoption of digital learning solutions 

To maximize our impact, we hope to broaden our adoption reach to individuals that don’t have access to quality education in the first place to democratize education for groups left-behind. A potential barrier is reaching those individuals and creating a solution that would seamlessly fit their unique cultural setting.

Barrier 1: Talent

We are well-positioned to recruit the right talent to build a truly world-class solution. The primary talent pools that we are currently tapping into are (a) the University of Cambridge, the University of St. Gallen, and ETH Zurich, as well as (b) the Kairos Society Europe network (a talent network with 700 members). To fund our employees, we seek to take on outside investments. We have already received early interest from various Angel investors - and hope to gain further interest from the MIT Solve& Elevate networks.

Barrier 2: Access to paying customers

EWOR’s initial product - corporate innovation challenges - has proven to be a promising route to win initial customers for our technology platform. Several of the firms we previously worked with indicated a keen interest in piloting the new digital platform. This will not only provide valuable feedback but also serve as initial references for further customers. Once we have a proof of concept, we will leverage the team’s networks (e.g., previous employers, universities) to expand our customer base.

Barrier 3: Broad adoption of digital learning solutions

To reach a higher number of learners in-need, we partake in global opportunities to create visibility. Moreover, while we have defined clear hypotheses around our platform, only real-world implementation will show how users interact with it. To optimize the learning process, we follow a “lean startup” methodology.  We hope to uncover cultural idiosyncrasies in our target markets and iterate/pivot our application until we find a clear product-market-fit.

For EWOR, three groups of partners are in particular focus.

First, university and community partnerships, including foundations, have helped and will help us to distribute our product to individual learners. We have partnered with 30 university clubs across Europe so far, who spread the word about our product. 

Second, we target corporations that buy the EWOR LMS to empower their workers to gain relevant digital skills and stay competitive. We plan to receive both high-quality user adoption and financial sustainability, thanks to corporations. So far, we have partnered with SGS, a 100.000 employee company from Western Switzerland, and E-BIKE Technologies GmbH, a German-based SME to pilot our product. We have two further customers on board for our Summer prototype. 

Third, we want to partner with research institutions to power the technical development of the product. We plan to establish an especially close relationship with the department of engineering at the University of Cambridge. We have already found valuable advisors and supporters at the machine learning office there.

While our application will be based on a unified platform, we plan to address distinct customer groups.

EWOR AI (B2C): The basic version will be available to everyone for free (workers or students). Learners can download the app through the iOS App Store, the Google Play Store, or access it through a web browser. This version will offer all core features: a "learning journey" that connects learners with the right content at the right time. It will also make recommendations that help learners identify lateral fields of potential interest. The B2C version scrapes and compiles personalized learning journeys to help workers and individuals without access to education build their professional competencies and resources. The corporate and the customer versions are released in parallel, with corporates covering the platform's development costs.

EWOR AI Corporate (B2B): We offer a paid version for corporations. This version lets corporations upload their own content (exclusively available to the institution's workers) and recommend further materials online. It offers curated tracks for company's skill needs (e.g., intrapreneurship or data science) and corporation-specific security features. We track each employee's skill profile to help corporations find fitting candidates for difficult jobs easily. From our initial pilot this was identified as one of the greatest benefits.

The large amount of training data gathered by our B2C model will help us improve the quality of the B2B product. Our B2C model will be distributed primarily through PR and marketing efforts, while our B2B product will need a physical salesforce.

We aim to develop our solution in two phases: (1) Development phase and (2) Distribution phase.

1) Development phase:

The path to an initial proof-of-concept requires extensive research and development efforts. Given our technology-focused approach, we will need a team of world-class engineers and designers to make our solution user-friendly and appealing. For that purpose, we plan to attract external funding from venture capital investors. Thus, we are looking for an investor that can provide not only funding but also expertise and a corporate network that will allow us to gain access to clients.

2) Distribution phase:

As soon as we have validated the product-market-fit, our organization will focus on the application's dissemination. In this phase, we will gradually sell our solution to paying customers (corporations). The proceeds will be used to expand the engineering team, sales force, and a broader support system. We will also offer a basic version for free to everyone, next to corporate usage, which would drive brand recognition and impact.

We envision that, as soon as the service gains sufficient traction, we will be able to cover further development and expansion costs through profits from our existing solution.

Revenue sources:

SGS: ca.10.000$ for conduction of the pilot product (delivered)

E BIKE Advanced Technologies GmbH: 0$ for 2nd pilot (delivered)

E BIKE Advanced Technologies GmbH: ?$ (repurchase after successful pilot; starts Aug 2020) 

Undisclosed corporation 1: 250.000$ for pilot product with learning map (LOI to be signed in August; starts 2021)

Undisclosed corporation 2: 250.000$ for pilot product with learning map (LOI to be signed in August; starts 2021)

We have currently applied for Founders’ Fund in Berlin, which has preliminary agreed to fund four of the core staff for two years to develop and sell the initial product. We will also apply for Y Combinator in July 2020 for additional seed funding. We have calculated that with the Founders’ Fund and Y Combinator, we would be able to collect a total amount of 400.000$, which will be enough to bring us to the distribution phase. We hope that additional funding sources will fuel our growth, such as Solve or Solve Partner prizes, funding from the Bill & Melinda Gates Foundation, and further sources. 

To fuel our distribution phase, we plan to raise a structured Series A of ~$1.2 million during the second or third year of operation, which we want to invest in product development and sales equally.

Core team: 2.760$/m for each of the initial four team members (=net wage + 25% mark up for taxes and insurance) (= 134.000$/year; 55.000$ from Aug-Dec 2020).

Server, compute, and database costs: None, fully covered by 50.000$-worth of free Digital Ocean credits, which last for 1,5 additional years. 

Rent: None, fully covered by Judge Business School Accelerator.  

Traveling expenses: 300$ per person per month (=14.400$/year; 6.000$ from Aug-Dec 2020).

Pot. Sales Commission: 10% on a closed B2B deal with corporate partners, which amounts to 25.000$ per contract. 

Optional - further staff: four engineers (4.500$/month), one designer (3.400 $/month), one marketeer (3.400$/month), and one commercial analyst (4.000$/month) (= 346.000$/year; 144.000$ from Aug-Dec)

Conservative burn rate (w/o sales commission) from Aug-Dec 2020: 61.000$ 

Optimistic burn rate (w/o sales commission) from Aug-Dec 2020: 205.000$

I was especially drawn to Elevate because of its focus on big problems, which, when solved, elevate humanity as a whole. Our plan at EWOR is risky and bold, but I believe that MIT Elevate is a platform where such bold solutions are heard. 

Awakening the hero in all of us: Elevate's motto resonates a lot with EWOR's vision. Through democratizing life-long education, EWOR AI aspires to awaken and enable the hero in all of us - independent of geographical location or thematic interest - to learn effectively, grow up to their own potential, and contribute meaningfully to their communities.

While our presence is strong in the DACH region (Germany, Switzerland, and Austria), we want to expand globally. Through MIT Elevate, we could reach a global audience of users and supporters and spread awareness of the unique personalized opportunities we provide for learners - both students and workers. The tailored marketing campaign that comes with being selected as a finalist might elevate us as a solution and, as a consequence, humanity as a whole. 

Moreover, I am well aware that my social environment has a strong influence on who I am and who I become. At Elevate, I hope to meet like-minded people and social innovators from all over the globe who will challenge my ideas and spark new ways of thinking.

Lastly, we are convinced that Solve and Elevate, thanks to the network and brand recognition, can help us attract further funding from the US and worldwide.

Our team has significant experience in business modeling and raising money. We believe that many of us have built outstanding networks, which will further help us with legal and business matters. This is why we have not selected these areas.

We believe that MIT and the Elevate network are ideal for providing technological advice and mentorship. Many world-changing innovations have been developed at MIT, such as voice recognition by Ray Kurzweil or the world wide web by Tim Burners-Lee. There is no better place to dissect and improve our technology rigorously. We are also convinced that the Solve network may help us distribute our LMS to institutions by both adding credibility to the EWOR product and actively reaching out to close partners.

Lastly, MIT Solve's and Elevate’s strong brands and access to media, and thought leaders could help us market our product, especially our free B2C version.

MIT faculty could help us with our product's technical development. More specifically how to render our algorithms be more efficient; how to A/B test specific technological solutions and to implement new academic insights.

Furthermore, we would like to partner with corporations who seek to empower their workers to gain relevant digital and tech skills and stay competitive within a fast-changing world.

Finally, once the product is finalized, we'd like to work alongside organizations (e.g., NGOs) who have a reach to learners who do not have access to (high-quality) formal education and would benefit from our product independently of their location.