Narrative based Ai (Ai-narration) uses natural language processing (nlp) and machine learning (ml) to augment storytelling about real world event context.
Macro linguistics™ feeds smarter context learning to create actionable insights informing critical organization and financial market decisions.
Prechelon uses internal and external data wranglers to acquire unstructured data and store it in our event base. We use an agile architecture to scale alongside the anticipated growth in unstructured signal data.
2.5 exabytes of data are produced daily and the pace is accelerating. We use proprietary natural language processing (NLP) to extract, disambiguate and classify entities (i.e. people, places, dates, things, other).
Prechelon uses a proprietary event representation to move through abstraction in ways that improve event relations, dependency inferencing and learning capability.
A virtuous event data cycle extends machine learning algorithms to better isolate divergence and penetrate faux ambiguity so we identify and infer the most relevant entities and intentions related to world event.
Machines can understand and infer connections in more dimensions at a greater horizon than humans. Our system uses valuable internally generated context data to improve future event anticipation.
Myths, archetypes and stories are how we organize the world. From the newsroom to the trading floor, everyone wants to know where the narrative is heading and how it ends. We use natural language processing (NLP) to turn computed event context and prediction into human readable format.