How are we staying up to date with COVID-19: Reliable information in times of disruption

Right now, accurate, up-to-date, and reliable information is absolutely essential in various fields, from governmental policy-making to medical research.

But the truth is that this information is also difficult to obtain, verify, organize, and maintain.

This is the reason why we are creating a research platform for COVID-19. Our solution revolves around AI models that help humans collect, organize, and maintain knowledge bases.


Our users work in non-profit, hospitals, health care, life sciences, biotech, and manufacturing. They need to find and share scientific information, directly and indirectly, related to COVID-19. From virology to psychology, the speed of research is accelerating fast. This makes it difficult to catch up with what canonical knowledge is at the specific point in time.


Imagine that you need to find relevant information on the epidemic distribution of the virus (when, where, who), or answer on policies that can easy the population’s “psychological distress” during the quarantine.

How many sources would you need to go through to aggregate information, how many types of formats will you need to work with (papers, news, institutional websites, etc), and once that information is collected, who will be in charge of its maintenance?


Our solution is a COVID-19 insight platform that combines a search engine, a knowledge management tool, and smart alerts.

We make it easy to organize research into living documents that collectively become a knowledge base available for public use.


It collects information from a wide array of sources, including news, scientific journals, public health data, supplier databases. It processes free form text, as well as structured data for use in our smart editors.

And finally, we use AI to tag information that is incorrect inconsistent, or outdated to aid in projects such as effective public health campaigns and supplier database building.

• Analyze the latest COVID-19 papers, news, and datasets • Transform your research into living documents and databases • Share the results of your work • Set smart alerts for when new data is available


We aim at developing a commercially viable solution that will fit particularly knowledge intensive sectors such as medical research, pharma and CRO (Contract Research Organizations):

AI Discovery

Make insights more easily discoverable: uncover hidden knowledge from your unstructured data stored in your Data Management Systems. Leverage AI to analyze signals from the internet

Automatically process signals from the web to signals related to drug safety (Pharma)

Market Intelligence

Monitor your competition. Analyze R&D activity, product launches, filed patents, news mentions, sentiment, etc. Discover and follow startups and their advancements for business development and M&A.

Knowledge Management

Foster better collaboration and information reuse. Enable knowledge sharing through the implementation of F.A.I.R. data principles.


• Gathered feedback from researchers • Prioritized with researchers top questions of interest for analysis from literature • Developed a search engine module that extracts answers for specific questions such as "what do we know about the role of vitamins in fighting COVID-19", or "what governmental measures can be put in place for easing psychological distress in citizens under quarantine"


Servers: 10000 USD/year will cover computational costs Employees: ○ Two AI-focused developers ○ One front-end developer ○ Product manager/sales/marketing ○ Community manager and scientist

60 thousand Euros will allow us to move forward for four months to complete the steps below.


Milestone #1: May 2020. We aim at seeing our Research Engine cut by more than half the time medical professionals spend for the analysis of literature and media on COVID-19. We want to give back time to researchers and doctors so that if today they spend one-hour doing research they could achieve the same results with our platform in twenty minutes.

Milestone #2. June-July 2020: we aim at building a tool for flagging fake news and social media speculation on COVID-19.

Milestone #3. June through August 2020: bring onboard over 200 separate research teams to collaborate on COVID-19 data and insights. We want to save those teams collectively beyond 30,000 hours of research work.

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posted an update

Signed-off the day with a work-in-progress version of an AI model that extracts key insights/facts from papers and news. Users select and issue/topic they want to receive insights on, and the AI categorizes and extracts excerpts from COVID-19 open literature and news outlets.

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