Contextual AI

Retrieval-Augmented Generation (RAG) is an artificial intelligence framework^[1] that integrates information retrieval with text generation to improve the performance of large language models (LLMs)^[16] on complex, knowledge-intensive tasks. It was introduced in 2020 by researchers at Meta AI, including Douwe Kiela, Patrick Lewis and others, in their paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.^[6] RAG enables language models to access^[17] and incorporate external information, such as proprietary databases or real-time web content, at query time, instead of relying solely on pre-trained,^[18] internal, static knowledge. This architecture addresses common limitations of standard LLMs, including hallucination,^[19] outdated information, and lack of attribution to source materials.^[20] RAG systems retrieve^[6] relevant context through a variety of techniques - including vector search, keyword search, text-to-SQL - and feeds this context into the language model to generate responses. The approach improves factual accuracy,^[21] supports domain-specific customization, enables citation of sources, and allows for more updated information without retraining the model itself.

General Availability. In January 2025, Contextual AI announced the general availability of its enterprise platform for building specialized RAG agents.^[22] Early adopters included Qualcomm, which used the platform for their Customer Engineering team needs.

Grounded Language Model. In March 2025, the company introduced a Grounded Language Model (GLM)^[23] for factual accuracy in enterprise AI applications.

Reranker. In March 2025, Contextual AI released an instruction-following reranker^[24] that allows users to influence the ranking of retrieved documents through natural language instructions, such as prioritizing recent files, specific formats, or content from designated sources.

Contextual AI's platform has been adopted across a range of industries, including finance, technology, media and professional services. Clients include Fortune 500 companies such as Qualcomm^[25] and HSBC.^[26]