Mine Unstructured Data Using Your Domain-Specific Language

DataScava mines unstructured data to make it more accessible, understandable and actionable using domain-specific topics tuned to your business and in your control.

Our technology keeps the Human in Command and works on its own or as an alternative or adjunct to NLP and NLU to curate, filter, match and route relevant textual content for use in AI, ML, RPA, prediction engines, business analytics, production systems, and other data-driven solutions.

Proprietary Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS)  provide highly precise results you can see, control and measure. DataScava doesn’t try to disambiguate natural language or infer what you’re looking for. It finds what you are looking for.

DataScava is for Business Users, Data Professionals and Software Engineers. You don’t have to be a Data Scientist to use it. Ease of use and transparency make it accessible to technical and non-technical people, enabling collaboration and a rapid path to efficiency.

With DataScava You Can . . .

  • Create highly precise unstructured textual data searches to automate time-consuming curation and tagging of large data sets;
  • Search, filter, match and route text such as documents and emails to downstream systems based on their content and intent;
  • Mine research databases, news and other subscription-based feeds, match people to jobs . . . and more;
  • Measure output of AI/ML/RPA systems to reduce the risk of their suggested actions;
  • Encapsulate your organization’s language, jargon and acronyms in software for company-wide use;
  • Make business decisions that correspond to what your data is telling you.

How is DataScava Different?

  • It is a self-service, data agnostic open tool that nontechnical and technical people can master in a short time;
  • It works top down through the entire corpus, precisely indexing, quantifying and filtering raw text at the file level;
  • It is not an AI/Machine Learning tool focused on a classic NLP use case of drawing insights from documents;
  • It uses DSLP and WTS to index, score and measure the found keywords/phrases associated with your domain-specific topics;
  • It uses indexed topic scores to identify and highlight highly precise on-topic documents and eliminate irrelevant ones;
  • It provides a competitive advantage to companies seeking to maximize the transformative power of data.

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