How

DataScava uses your domain-specific language to index, search, score, match and curate data points for your use in data-driven solutions such as AI/Machine Learning, prediction engines, business analytics and production systems, delivering verifiable results fast.

Our proprietary Non-NLP Data Parser and patented Weighted Topic Scoring provide visualization, scoring and highlighting of data points that help you draw insights and make more accurate business decisions.

 

Control the Input, Mine the Output

DataScava . . .  

INDEXES your selected textual content using your customized Domain-Specific Topics and associated Keywords, not NLP and Semantic libraries

SCORES your textual content and generates Metadata including Topic Scores, Percentile Rankings, Data Tags and more.

MATCHES your weighted topics of interest using your Company-Specific Search Templates and Weighted Topic Scoring.

CURATES a subset of files for use in AI/Machine Learning and other data-driven systems and filters new data around the clock.

 

How DataScava’s Intelligent Approach Adds Value to Unstructured Data

This is an excerpt from a recent post written by our CTO John Harney.

Real-time mining of unstructured textual content isn’t simple. Available solutions don’t work well unless they’re fine-tuned to meet your specific needs and address the unique quirks in your company’s information.

To truly add value, your applications, whether AI-driven or not, require a vocabulary that captures the definitions, context and nuance of your business.

DataScava’s unstructured data miner provides fast and effective solutions for leveraging the explosive growth of unstructured data.

The sheer volume of information—even some small businesses must manage thousands of pages each day—challenges companies to find the most efficient and accurate way to read data, index it, extract what they need and quickly route it to its proper destination.

Conventional wisdom says that taking full advantage of information requires a “data-driven system” based on “artificial intelligence, “machine learning” or some other application. But without modification, these technologies often process information quickly, but incorrectly.

Needless to say, machines that use inaccurate data for machine learning, AI insights, business decisions, or that pass data to the wrong destination, don’t do anyone much good.

That’s where DataScava comes in.”

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