Enterprise Data RAG Tools

Retrieval-augmented generation is a generative AI framework designed to ground LLMs with a company's internal data, typically docs stored in knowledge bases. These docs can include procedures, policies, product documentation, how-to manuals, and more.

Each document's text is converted into numeric vectors that capture textual meanings and inter-relationships. The vectors are stored in a vector database, enabling the LLMs to quickly search for the most appropriate docs to a customer's prompt.

K2view RAG tools, a.k.a. GenAI Data Fusion, uniquely add structured data to the mix, by injecting all the relevant data associated with a single business entity (customer, device, or order) into the model, for more personalized and meaningful responses.

To leverage the vast amounts of insights and knowledge stored in your structured data, K2view RAG tools, a.k.a. GenAI Da Fusion, use a structured data retriever.

The structured data retriever ingests, unifies, cleanses and transforms multi-source enterprise data, and then injects it into the LLM via contextual prompts in real time. It also employs dynamic data masking and role-based access controls to ensure that users see only the data that they're meant to see.

Yes, RAG tools can work with both structured and unstructured data.

A RAG workflow can be created to invoke a structured and an unstructured data retriever, and then merge the responses from both, into a prompt that grounds the LLM.

Retrieval-augmented generation is ideal for customer-centric use cases – like answering questions through a RAG chatbot, or generating cross-/up-sell recommendations for call center agents – where customer context is critical for providing accurate and personalized responses.

Augmenting your LLM with your company's internal docs is great for use cases like competitive, financial, or market analyses, RFP response generation, and more.

The answer to the first question is, "Yes". The data for every one of your customers is stored in its own high-performance Micro-Database.

The K2view platform is cost-effective because the Micro-Databases are compressed by up to 90% and require minimal CPU for processing. Also, billions of Micro-Databases can be managed concurrently over commodity hardware.

Micro-Databases can be stored in the private/ public cloud database of your choice, including: Microsoft Azure Blob Storage, Amazon S3, and Google Cloud Storage.

On-prem data stores, such as Cassandra and PostgreSQL, are also supported.

The Micro-Database has a built-in, sophisticated data synchronization mechanism that lets you configure user-defined sync rules at the field level.

These sync rules enable you to fully control how and when the Micro-Database is updated from the underlying source systems.

For example, Micro-Database fields whose values rarely change (e.g., address) can by synced infrequently, while quick-changing transactional data may be synced via real-time streaming events (e.g., CDC updates). Yet others can be refreshed on demand, whenever the Micro-Database is queried.

Very little, regardless of the data integration methods used to sync the Micro-Database:

• Streaming, messaging, or CDC methods "push" data updates to the Micro-Database without impacting the source systems at all.  
• JDBC queries or APIs, that are invoked against the data sources, "pull" Micro-Database updates for a specific entity. They hardly impact the source systems due to the low compute required to perform a simple query for a single entity.