In fall 2020, we noticed that our offering is way too complex to explain in a minute. Furthermore, the tech development, releases and maintenance had become challenging, thanks to too many variants and versions. All in all, we decided to rebuild our offering. To be honest, this journey took 12 months more than I expected. Simplifying complexity is not the easiest job and we have ended up in complexity because we’ve been solving systemic grand challenges comparable to climate change or over population. So, the question is what we are solving? We help organizations succeed in a rapidly changing future by helping them find answers in large amounts of data, answers that they can't otherwise see. Our algorithms help customers see the big picture in scattered data by revealing unknown connections and even explaining why they exist. So, in short, HeadAI builds a cognitive AI engine powering sustainable economic growth: Competitive Intelligence, SDG/ESG Scorecards, Signals & Foresight.
"HeadAI builds a cognitive AI engine powering sustainable economic growth."
All our approaches start from good quality data sets with what we can build analyses and simulations. Way too often we and our clients got stuck on a dilemma that we do not have enough good data to get started. And at the same time we knew that it is not true, because the world is full of high quality data publicly available for everyone. In fact, this observation became the first cornerstone of our product/solution offering: Open-Source Intelligence.
Open-Source Intelligence was traditionally a subset of military intelligence, but nowadays it’s in the crossroads of competitive intelligence, business intelligence and systemic foresight operations. Since most of our predictive analytics we have built has been based on open data sources like public research projects, scientific papers, policy papers, economic news, technology news, investment reports, public procurements, job ads, curriculums (national and institutional), it was a natural decision to name our Data Stream offering as “Open-Source Intelligence”. Open-Source Intelligence -data streams serve as a backbone for the rest of our product/solution offering: Scorecards, Compass and Fast Learning. The big thing is that now we can make all the data sets compatible with every method we use. Data types are time series and graphs (mathematical definition of graph). Naturally, our customers can build their own data sets into exactly the same models and apply their own data as a part of every solution we have. Customer data is either myData-type of personal data or just confidential data owned by the customer. Generic Scorecard tells you how similar two datasets are and why (explainability). Generic scorecards can be used to e.g.
match startup to investor’s portfolio,
find optimal candidate for a job,
predict if an item in a health record contains a functionality/disability code,
what is the gap between educational offering and labor market demand or
how well an environmental report (from e.g. construction project) fits into United Nations Sustainable Development Goals. In fact the last one, the SDG Scorecard, is one of our most valuable subsets of Scorecards and that’s why we named it as one of our core products.
“The SDG Scorecard is one of our most valuable subsets of Scorecards and that’s why we named it as one of our core products.”
SDG / ESG / Green Deal Scorecards enable continuous measures against well known and complex future goals. United Nations Sustainable Development Goals must be one of the best known set of goals in this category. Economical Sustainability Goals as well as EU’s Green Deal Programme are other important future goals in this domain. Any scorecard can be computed against pre-defined goals and Open-source intelligence -datasets. Also customer data is enabled, but starting continuous improvement does not require customer data. Our earlier hypothesis, that there is enough public data to get started, is valid. Green Deal Scorecards can be used to predict what new research activities a corporate R&D department should start in order to make sure they are the industry leaders in terms of the EU Green Deal.
While Scorecards are generic and organization/nation -level approaches to sustainable economic growth, our Compass -family offers tools to individual sustainable growth. Compass compares individual data (myData) against selected goals (open-source intelligence data sets), and when the gaps are found, it suggests activities to override the gap. The activities are also selected from open-source intelligence data sets. Compass can be used to e.g. find optimal courses for a last-year student to make sure she/he gets a job right away in a domain/industry she/he has studied.
Fast Learning is a subset of Compass. Instead of only suggesting new courses, it automatically assesses a person's understanding on selected topics (open-source intelligence data sets) and after that either suggests readings or just passes the user to the next topic to assess. As an outcome, a person gets a validated skills profile, transferrable to any external system, e.g. in xAPI format. The internal user profile in Compass and Fast Learning is in exactly the same data model as any data in Headai’s current approach. This enables e.g. comparing personal skills profile to UN Sustainable Development Goals to make sure the person is 2030 compliant. The new product family launch is close. Stay tuned. More information coming soon.