TerminusDB Logo all white svg
RAG Venn Diagram
VectorLink - The TerminusDB Semantic Indexer - Logo White

Retrieval-Augmented Generation

Enhanced accuracy and reliability of generative AI using your data.

Need help getting started? Talk to us.

What is Retrieval-Augmented Generation?

Retrieval-augmented generation combines information retrieval of your business data with AI text generation. It uses these external data sources to enhance the accuracy and relevance of conversations with LLMs and helps find content.

Why RAG?

RAG enhances AI responses by integrating external information with the LLM. Organisations that extend models with their data can use generative AI to deep-dive context-specific information for more accurate and relevant responses.

Rag at Scale

Our RAG solutions can scale to billion+ records and we can help you to achieve high-quality results which work for your data. Our team are experienced in tuning retrieval-augmented generation to deal with domain-specific queries and problems. In addition, we can leave behind simple solutions for your teams to use.

Implementation complexity solved

Implementation complexity abstracted.

Training & Support for Self-Sufficiency

We help to train your developers on how to use the tools to integrate them into your search and interactive systems.

Good Developer Experience

Low developer learning curve with users left with intuitive interfaces.


Scale as much as you need with the ability to handle over a billion records.

Who’s RAG for?

Generative AI is changing the way customers search and interact online. Most businesses using AI assistants to respond to users need RAG to supplement an AI model’s parameterized knowledge with their data. For example –


A shop assistant who understands the customer’s tastes, trends and shopping habits.

Help Desk

If you have lots of information that needs to be surfaced to customers, RAG can help make it easier for you to find the right content


A financial analyst with an AI model supplemented with market data.


A nurse or doctor with an AI assistant that has access to a medical data index.

Query for the Non-Technical

RAG can be used to obtain database queries directly from natural language questions, opening siloed data to your less technical domain experts.


Make your catalogue searchable more deeply and interactively, where keywords are replaced with conversations that can help with the discovery of important content.