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Contextual AI

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Contextual AI
Company typePrivately held company
IndustryInformation technology
Founded2023
FoundersDouwe Kiela, Amanpreet Singh
HeadquartersMountain View, California, U.S.
Number of employees
95
Websitecontextual.ai

Contextual AI is an enterprise software company[1] based in Mountain View, California. It develops a platform for building[2] specialized Retrieval-Augmented Generation (RAG) agents for enterprise use.[3] The company was founded in 2023 by Douwe Kiela and Amanpreet Singh, both former AI researchers at Facebook AI Research (FAIR)[4] and Hugging Face.[5] Douwe Kiela previously led the Meta research team that introduced the Retrieval-Augmented Generation (RAG) approach in 2020.[6][7][8]

Contextual AI focuses on enterprise generative AI applications using RAG 2.0 technology,[9] with deployments primarily in the technology, banking, finance and media sectors.[10]

History

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In June 2023, Contextual AI announced[4] it had raised $20 million in a seed funding round led by Bain Capital Ventures (BCV), with participation from Lightspeed Venture Partners, Greycroft, SV Angel, and several angel investors.[2]

In August 2024, the company raised $80 million in a Series A funding round[11] led by Greycroft,[12] with participation from previous investors[13] including Bain Capital Ventures, Lightspeed, and Conviction Partners.[14] The round also included new backers such as Bezos Expeditions, NVentures (Nvidia), HSBC Ventures, and Snowflake Ventures.[15]

Features

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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 This architecture addresses common limitations of standard LLMs, including hallucination,[19] outdated information, and lack of attribution to source materials.[20] RAG systems retrieve[6] relevant context through a variety of techniques - including vector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.

Applications

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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]

References

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  1. ^ a b Singhal, Rahul (November 30, 2023). "The Power Of RAG: How Retrieval-Augmented Generation Enhances Generative AI". Forbes.
  2. ^ a b Wiggers, Kyle (June 7, 2023). "Contextual AI launches from stealth to build enterprise-focused language models". TechCrunch.
  3. ^ Wolfberg, Elias (August 29, 2024). "From RAG to Richness: Startup Uplevels Retrieval-Augmented Generation for Enterprises". NVIDIA.
  4. ^ a b Franzen, Carl (June 7, 2023). "Contextual AI emerges from stealth with $20M to pursue 'artificial specialized intelligence'". VentureBeat.
  5. ^ "Build specialized RAG agents with Contextual AI". Cerebral Valley. January 28, 2025.
  6. ^ a b c Douwe, Kiela; Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, Fabio; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich; Lewis, Mike; Yih, Wen-tau; Rocktäschel, Tim; Riedel, Sebastian (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks". arXiv:2005.11401 [cs.CL].
  7. ^ Kiela, Douwe; Riedel, Sebastian; Lewis, Patrick; Piktus, Aleksandra (September 28, 2020). "Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models". Meta.
  8. ^ MacManus, Richard (May 28, 2025). "No, MCP Hasn't Killed RAG — in Fact, They're Complementary". The New Stack.
  9. ^ Bridgwater, Adrian (January 15, 2025). "Contextual AI Specialized Agents, Even More Raggy than RAG". Techstrong.ai.
  10. ^ Patangia, Pahal (October 28, 2024). "Fintech Leaders Tap Generative AI for Safer, Faster, More Accurate Financial Services". NVIDIA.
  11. ^ Gain, Vish (August 2, 2024). "San Francisco's Contextual AI raises $80m to scale platform". Silicon Republic.
  12. ^ Vu, Marcie (August 1, 2024). "Expanding Our Investment in Contextual AI". Greycroft.
  13. ^ Cai, Kenrick (August 1, 2024). "Contextual AI raises $80 mln for model-enhancing technique". Reuters.
  14. ^ Fink, Charlie (August 9, 2024). "Contextual AI Raises $80 Million, Judge Calls Google A Monopolist, Bytedance Intros Jimeng Text-To-Video AI". Forbes.
  15. ^ Deutscher, Maria (August 2, 2024). "Contextual AI nabs $80M for its 'RAG 2.0' platform". SiliconANGLE.
  16. ^ Leng, Quinn; Portes, Jacob; Havens, Sam; Zaharia, Matei; Carbin, Michael (November 5, 2024). "Long Context RAG Performance of Large Language Models". arXiv:2411.03538.
  17. ^ "Retrieval Augmented Generation (RAG)". Prompt Engineering Guide. April 24, 2025.
  18. ^ Gao, Yunfan; Xiong, Yun; Gao, Xinyu; Jia, Kangxiang; Pan, Jinliu; Bi, Yuxi; Dai, Yi; Sun, Jiawei; Wang, Meng; Wang, Haofen (December 18, 2023). "Retrieval-Augmented Generation for Large Language Models: A Survey". arXiv:2312.10997.
  19. ^ Shuster, Kurt; Poff, Spencer; Chen, Moya; Kiela, Douwe; Weston, Jason (April 15, 2021). "Retrieval Augmentation Reduces Hallucination in Conversation". arXiv:2104.07567.
  20. ^ Wang, Han; Prasad, Archiki; Stengel-Eskin, Elias; Bansal, Mohit (April 17, 2025). "Retrieval-Augmented Generation with Conflicting Evidence". arXiv:2504.13079.
  21. ^ Leto, Alexandria; Aguerrebere, Cecilia; Bhati, Ishwar; Willke, Ted; Tepper, Mariano; Vo, Vy Ai (November 11, 2024). "Toward Optimal Search and Retrieval for RAG". arXiv:2411.07396.
  22. ^ Wheatley, Mike (January 15, 2025). "Contextual AI launches RAG 2.0 platform to aid in the development of domain-specific AI agents". SiliconANGLE.
  23. ^ Nuñez, Michael (March 4, 2025). "Contextual AI's new AI model crushes GPT-4o in accuracy — here's why it matters". VentureBeat.
  24. ^ "Contextual AI Launches World's First Instruction-Following Reranker". Associated Press. March 11, 2025.
  25. ^ "Contextual AI's Customers". CB Insights. 2025.
  26. ^ Sullivan, Mark (March 18, 2025). "The most innovative companies in applied AI for 2025". Fast Company.
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