Create precise, reusable definitions for business terms, metrics, entities, policies, and relationships so AI works from consistent enterprise meaning instead of reconstructing context for every use case.
Link business meaning across metadata, lineage, policies, processes, data products, and enterprise systems so AI can navigate relationships rather than query isolated information.
Learn from enterprise metadata, AI responses, corrections, outcomes, and steward feedback to improve reusable context over time while preserving governance and human oversight.
Enterprise knowledge is fragmented across platforms, catalogs, policies, applications, workflows, and people. Teradata provides a governed foundation for creating, curating, and reusing trusted business knowledge across AI systems—helping applications and agents operate from consistent definitions, relationships, lineage, policies, and industry context.
Tera Context Engine is an open, neutral context and orchestration layer for enterprise AI. It operates above and across databases, data platforms, pipeline engines, catalogs, knowledge repositories, models, applications, and AI agents—transforming distributed enterprise information into governed, reusable knowledge. Built once and governed consistently, that context can be reused across applications, agents, models, platforms, teams, and use cases.
Industry Knowledge Models provide the domain-correct business context generic AI lacks. They capture industry terminology, entities, relationships, policies, processes, metrics, and operating conditions to give AI a stronger foundation for accurate, explainable outcomes.
Combined with semantic relationships and deterministic grounding, these models help reduce ambiguity and retrieval noise while improving trust and time-to-value. Context can continue to improve through enterprise metadata, AI responses, corrections, and outcomes.
Tera Context Engine connects and governs context across existing enterprise ecosystems. It ingests signals from Teradata and third-party data, AI, BI, catalog, and transformation environments, enriches them with industry knowledge, semantic relationships, lineage, and governance, and makes trusted context available at runtime. Its open, bidirectional architecture can also write enriched context back across the ecosystem without requiring Teradata as the underlying data platform.
Ingest enterprise metadata, usage patterns, lineage, policies, AI responses, corrections, and outcomes across heterogeneous environments.
Apply Industry Knowledge Models, semantic relationships, deterministic grounding, lineage, quality, access controls, and policy to create trusted business meaning.
Make governed context available across applications, agents, models, and data products, then continuously improve it from new interactions and outcomes.
See how governed, industry-grounded business context helps AI understand terminology, relationships, policies, processes, and operating conditions to produce more accurate, explainable, and actionable outcomes.
Ground AI in trusted financial definitions, relationships, and regulatory frameworks to improve decision-making across banking, risk, and compliance functions.
Enable agents with industry-specific insurance knowledge spanning policies, claims, underwriting, and actuarial concepts for more accurate decisions and automation.
Provide AI with healthcare-specific context, standards, and regulatory knowledge to support clinical, operational, and financial workflows.
Give agents a unified understanding of customers, networks, products, and revenue models to improve service, retention, and operational efficiency.
Equip AI with retail-specific knowledge across demand, products, channels, and customer behavior to improve planning, personalization, and profitability.
Connect production, asset, quality, and supply chain knowledge to help AI optimize operations, reduce disruptions, and improve performance.
See how governed, industry-grounded business context can reduce the context tax, improve AI economics, strengthen explainability, and help move AI from experimentation toward production outcomes.