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The AMG Kernel trains AI with continuous market asset data signals to infer truthful predictive templates of sentiment at the forefront of change.

Regulated Financial data source 40+ million SEC EDGAR filings for output in any general format.

The Filtered sentiment source data are transformed for analytics (AWS, Tableau,....) to learn How relevant and specialized capital values are changing in unique sets or groupings.

The Kernel visualizes relationships between shared understandings and common questions about how the structured sentiment is changing; and formalizes truthful answers.

The EDGAR filings are filtered and embedded in fixed periods of annual, quarterly, monthly, weekly, daily;

and intraday fixed and flexible intervals of action:

The Financial market data reside in object storage (AWS cloud data buckets, eg.):

Fiduciaries visualize overlays of Financial sentiment vectors on interactive dashboards to embed the causal manifestations of asset changes - and train truthful inferences to relevant non-Financial value vectors (media, tech, social, rideshare, determinative....), where changing sentiment is also important..

The multidimentional 'etch-a-sketch' prototype accesses EDGAR data filings of Hundreds of Form Types for Thousands of Corporations reporting Continuous Regulated Market Core Data -- Data to train AI with truthful inferences that continuously originate in EDGAR filings.

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