webAI
We find the right leads
webAI browses hundreds of thousands of company websites, evaluates them using an array of quantitative measures and identifies the perfect leads for you.
The platform
WebAI is an artificial intelligence platform developed by istari.ai, which scans and evaluates company websites. Companies use their websites to inform about their products and services, their technologies and implemented standards, as well as their corporate strategy, partners and customers. webAI identifies and analyzes this content using cutting edge methods from text mining and machine learning. On this basis, we generate an array of web-based indicators that allow us to identify companies that match your specifications.
Individual results
Your specifications can be either a textual description of the target firm or the website of an existing company. Additional geographical, technological, sectoral, and networking filters allow you to refine your webAI query. One of your existing leads can hence serve as a guideline to identify similar companies, that match your specifications in additional dimensions (e.g. strong focus on digitalization).
Our skills
Web analytics
AI-based web analytics for information extraction and preparation of data from the Internet.
Web scraping
Identification, extraction and preparation of web data.
Text analysis
Analysis of structured and unstructured big text data for classification and information extraction.
Data mining
Pattern and trend analysis, classification and prediction of big data.
Consulting
Consulting in the area of machine learning, data and web mining, artificial intelligence and web analytics.
Developing
Development and programming of individual solutions in the area of machine learning, data and web mining, and artificial intelligence.
Our team
We at istari.ai are a team of young scientist who joined together to bundle their skills and passion for artificial intelligence, machine learning, and web mining. Our aspiration is to offer well-founded web analytics using cutting edge scientific methods.
David Lenz
Management Technology
David Lenz is a researcher and PhD student at the chair of statistics and econometrics at the University of Giessen. His academic interest concentrates on the application of novel methods from machine learning and AI research economics. Hereby, he puts the emphasis on studies in innovation economics using text data.
Jan Kinne
Management Strategy
Jan Kinne is a researcher at ZEW – Centre for European Economic Research in Mannheim and a PhD student at the University of Salzburg. In his research, he has a focus on generating web-based innovation indicators using text mining and machine learning.