AI needs business context, not fragmented data
What makes K2view data products AI-ready
Five capabilities span design-time and runtime, with AI automating much of the work to create and evolve data products.
- 01 Data discovery & classification
- 02 Business entity modeling
- 03 Entity-based data integration
- 04 Dynamic governance
- 05 Policy-driven synchronization
01Data discovery & classification
AI-assisted discovery profiles enterprise data, classifies sensitive fields, identifies relationships, and enriches metadata with AI-generated descriptions, all managed in the data catalog.
02Business entity modeling
AI accelerates modeling by using catalog metadata to identify business entities, suggest the root table for each entity, and bring relevant tables, fields, and cross-system relationships into the entity model.
03Entity-based data integration
The entity model defines how each table is populated for every entity instance.
Auto-generated ingestion flows query the required source data and can validate, enrich, mask, or transform it before it becomes part of the entity’s business context.
04Dynamic governance
Access to data and permitted actions are determined dynamically for each interaction, based on the user, task, permissions, and privacy requirements.
05Policy-driven synchronization
Entity data is synchronized bi-directionally with source systems according to freshness policies defined for each use case, keeping business context current for AI.
A reusable data product for each business entity
K2view data products are defined around business entity types such as customers, accounts, orders, and claims. Each packages the schema and reusable logic for ingesting, transforming, governing, synchronizing, and delivering multi-source enterprise data.
The same definition is applied at runtime to every instance of that entity, creating and maintaining its business context.
One context unit for every business entity
At runtime, each entity instance, such as an individual customer, gets its own isolated Micro-Database™, which manages its data and context. Dynamic in-memory caching enables millisecond access to entity context.
K2view synchronizes each Micro-Database with source systems according to use-case freshness policies, while supporting governed read and write-back. For unstructured data, the Micro-Database can also serve as a vector store for RAG.
The result is business-fresh context that AI can reason over and safely act on.
"K2view has a unique approach of integrating enterprise data with the LLM's advantage"
Business operational context, ready for AI
Complete by entity
Each entity’s context unifies relevant data, relationships, and business semantics across enterprise sources.
Trusted
Entity context is built from validated, cleansed, and resolved data, with quality policies enforced during ingestion.
Read/write access
Entity context is available in milliseconds via APIs and MCP, with RAG retrieval and governed write-back to source systems.
Governed
Entity context and actions are governed dynamically per interaction based on user, task, permissions, and policy.
Business-fresh
Entity context is synchronized with source systems according to each use case’s freshness requirements.











