Semantic search engine for scientific articles, patents, clinical trials, and industry news that reads and retrieves facts from the text to accelerate research in biotech and pharma
Current Status
1. MVP of product + 3 APIs;
2. 7 Clients
3. $5100 MRR
4. Win in the most significant competition in the computational biology field conducted in the US and supported by FDA
Problem or Opportunity
Scientific research is a time-consuming and tedious process. Scientists spend 14 hours per week manually searching and analyzing academic and industry documents. Less than 2% have IT skills, so they have to hire professionals to process big data or do it manually. It makes research and development extremely expensive in the Life Sciences industry, with biotech and pharma companies spending 23% of their revenue on R&D. Launching one drug takes $2 billion and 12 years. Researchers can't generate high-quality scientific hypotheses from millions of documents without an accurate automation tool
Solution (product or service)
The first semantic academic search engine based on novel NLP technologies and trained on self-created data that finds target information with unprecedented accuracy and retrieves facts from it.
It searches scientific articles, patents, and clinical trials by understanding their language instead of identifying keywords. To apply this knowledge in drug discovery, users can also find relationships between biological entities in scientific literature, like gene-gene interaction or drug-disease association. Due to our unique algorithms, users can even predict unexplored facts and get proofs.
Business model
1. SaaS subscription;
2. APIs subscription to various parts of the product for solving narrow textual data processing tasks;
3. On-premises licensing
