Precise, versioned definitions for business terms, metrics, and entities—from how "net revenue" is calculated in a specific regulatory context to the difference between a customer and a prospect across systems of record. Agents consume these definitions directly, eliminating ambiguous prompts and conflicting interpretations across teams.
Capture the relationships among data, KPIs, policies, processes, and data products so agents can navigate the business rather than query isolated information.
Use agent telemetry, steward feedback, and governance decisions to identify stale definitions and improve context over time while preserving human oversight.
Fragmented agents and applications can create isolated versions of business context. Teradata provides a governed foundation for creating, curating, and reusing enterprise knowledge across AI systems—helping agents operate from consistent definitions, relationships, lineage, and policies.
Industry knowledge models provide the domain-specific context that generic AI lacks. They encode the entities, relationships, KPIs, and business logic unique to each industry, giving agents a consistent foundation for reasoning and decision-making.
Built on real-world expertise, these models deliver consistent, validated, and continuously evolving business context. As industries, regulations, and terminology change, the models are updated to ensure agents operate with accurate, current, and relevant knowledge.
Knowledge context operates in three coordinated phases—ingestion, curation, and synthesis—supported by a continuous feedback loop that keeps the context layer current, consistent, and correct over time. The architecture is designed to coexist with existing enterprise systems, reading from and writing back to systems of record, catalogs, and data pipelines without requiring migration or displacement.
Structured and unstructured data are transformed into a common, AI-ready format enriched with semantics, metadata, and retrieval signals, creating the foundation for context curation.
Ingested signals are organized into a trusted knowledge layer by applying industry models, semantic relationships, lineage, and governance policies. The result is curated context that agents can use consistently and accurately.
Curated knowledge is assembled into task-specific context that agents can use to reason, decide, and act. Feedback from agent interactions continuously improves the context layer over time.
See how industry-specific context helps agents understand business concepts, relationships, and processes to drive more accurate decisions.
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.
Connect with the team to explore how enterprise knowledge context can improve agent performance, reduce operating costs, and scale across your organization.