
A never-ending learner that understands technologies.
Today’s AI doesn’t really understand the meaning behind the words people use. It is even worse for technology domains where problems are specialized or need expert knowledge in order to solve. This is what we want to address.
Expert Graph develops a “never-ending learner” that has a fluent understanding of natural language on technologies. The learner was trained with hundreds of millions of tech documents and has been learning to understand new technologies 24/7 since then. The learner is able to write fluent literature review around any tech field or social / economic topic. It enables us to provide exclusive services to discover new experts, competitors (orgs), products, patents, grants, research papers related to a specific technology.
Extract Tech Semantics
Our exclusive semantic analysis technique: word2tech goes beyond keyword matching by mapping natural language into 200K pre-defined semantic categories that machines understand.
Literature / Tech Review
Our exclusive technology / literature review technique can automatically generate a literature review around any technology or social / economic topic, including new technologies released hours ago.
Find Tech Experts
Users can find 33M experts across the world using skills, read a machine-generated notable work review for each, and search for related work within each one’s publications.
Discover Tech Companies
Users can analyze 3M organizations, including all major US companies, startups, research institutions and universities, and then find experts, R&D activities, competitors, and products within each.
Expert Graph Services
Access for everyone (with some results masked). Paid users ($79.00/M for each personal account) see all results.
For Bankers/Researchers
For Tech Hiring
For Editors/Funding Agencies
Commonly Used Functions
Example: find top experts “in academia” working on “relation extraction“
Example: find top experts “in industry” working on “relation extraction“
Example: find top “junior level” experts working on “relation extraction“
Example: find top “junior level” & “mid-level” experts working on “relation extraction”
Example: find top experts working on both “relation extraction” AND “question answering”
Try it yourself
Example: find experts working on “machine learning” at “Amazon“
Example: find experts working on “machine learning” at “University of Massachusetts Amherst“
Try it yourself (find target org first, and then click on i-Search to find experts)
Example: find R&D work related to “machine learning” by “Amazon“
Example: find R&D work related to “machine learning” by “University of Massachusetts Amherst“
Try it yourself (find target org first, and then click i-Search to find related R&D)
Example: find all products made by “Pfizer” released in “the latest 3 years“
Example: find who competes with “Pfizer” on technology in general
Example: find who competes with “Pfizer” on “Heterocyclic Compounds“
Example: find who competes with “Huawei Technologies” on technology
Example: find executives working for “Pfizer“
Try it yourself (for millions of organizations)
Example: browse (by research area) all tech experts working for “Amazon“
Example: browse (by research area) all tech experts working for “University of Massachusetts Amherst“
Example: browse all tech experts working for “University of Massachusetts Amherst” on “Information Intelligent Systems“
Try it yourself (find target org first, and then browse “Experts”)
Example: browse (by research area) all R&D work & expiring high-impact patents by “Amazon“
Example: browse (by research area) all R&D work by “Pfizer” in the “last 3 years“.
Example: browse all R&D work related to “Audio in a user interface” by “Amazon“
Example: browse all R&D work related to “Audio in a user interface” by “Amazon” released “last year”
Example: browse (by research area) all R&D work by “University of Massachusetts Amherst“
Try it yourself (find target org first, and then click “R&D”)