Our algorithms extend the conventional NLP to enable discovery of industry specific phrases, topics, entities and exposes latent relationships.
Providing rich context for AI: information assets are converted into an ontology and processed thru a reasoner to derive latent knowledge.
A combination of semantic and linguistic algorithms builds an unsupervised training model to create domain specific entity extraction models.
A hierarchical knowledge model is created using deep learning to solve classification & aggregation use-cases.
Process automation requires intricate knowledge of a business, its policies and processes in combination with expert-level identification of relevant data. PA enables enterprises to automate knowledge-data interactions in loan underwriting and claims processing at massive scale in a fraction of the time.
For any given project, there exists a massive volume of text-based content that must be read. DA enables the assimilation of contextually relevant information from multiple sources, reducing document-browsing time and allowing more time for strategic planning in regulation analysis, contract covenant management and financial research analysis.
The richest source of knowledge in an enterprise is hidden in unstructured data present in reports, manuals, presentations, policy and product notes. Experts need to extract industry-specific concepts, contexts and the relationships between them. Parabole KD allows data-discovery to power knowledge applications for strategic initiatives.
Using advanced cognition, we connect your unstructured data with traditional structured
data to solve complex challenges in risk, regulation and compliance
Impact analysis, GAP analysis, Audit review, Evidence management, Dashboards
Term discovery, Context discovery, Relationship discovery, Lineage discovery, Classification
Hidden relationships, information miss-match, lower false positives
Track sentiments, Contexts, Classification, Root cause analysis