Built by the pioneers of RAG, the Platform delivers production-grade accuracy to support specialized knowledge tasks and boost expert productivity
Contextual AI, a leading enterprise RAG company, has announced the general availability (GA) of its innovative Contextual AI Platform. This cutting-edge platform empowers businesses to develop specialized Retrieval-Augmented Generation (RAG) agents designed to enhance expert knowledge work across various industries.
While general-purpose AI agents are transforming many routine tasks, Contextual AI advocates that specialized RAG agents are essential to revolutionize domain-specific, high-value knowledge work. For subject-matter experts (SMEs) in any organization, this means providing AI tools tailored to their expertise, enabling them to tackle intricate technical challenges with precision. As part of today’s GA announcement, Contextual AI has revealed public benchmark results that demonstrate the platform’s industry-leading performance in advanced RAG workflows across diverse enterprise sectors. The platform outshines advanced models such as Anthropic’s Claude 3.5 Sonnet and OpenAI’s GPT-4o.
The Contextual AI Platform offers a comprehensive suite of capabilities to build, evaluate, and deploy specialized RAG agents, allowing businesses to fast-track AI initiatives from pilot to production and realize substantial ROI. Major Fortune 500 companies across financial services, technology, and professional services rely on Contextual AI to enhance employee productivity and efficiently navigate complex enterprise data.
Qualcomm Chooses Contextual AI for Technical Excellence
Qualcomm (QCOM), a global leader in semiconductors, selected Contextual AI after finding other RAG solutions inadequate for its highly technical and mission-critical Customer Engineering needs. The company’s Customer Engineering team has successfully deployed the platform to synthesize information from tens of thousands of technical documents, enabling quicker and more accurate resolutions for complex support cases.
“Contextual AI gives me confidence that we can leverage generative AI to support our team, help our customers design and develop products efficiently, and set new standards for performance and quality,” said Yogi Chiniga, Vice President of Customer Engineering at Qualcomm.
With today’s GA announcement, businesses can now utilize the Contextual AI Platform to build specialized RAG agents that intelligently combine retrieval and generation techniques based on conversational context, delivering precise responses for complex knowledge tasks across expansive sets of both structured and unstructured data.
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“A Critical Turning Point for Enterprise AI”
“Enterprise AI has reached a critical turning point,” said Douwe Kiela, CEO of Contextual AI. “AI agents will soon be available to every employee at every company. However, the specialized work of subject-matter experts remains largely underserved. Specialized RAG agents built on the Contextual AI Platform bridge this gap, enabling SMEs to boost their productivity with AI that truly understands their domain.”
RAG 2.0: Next-Generation AI Technology for Enterprise Success
The Contextual AI Platform is powered by RAG 2.0, a next-gen framework that optimizes both the retriever and generator components of the RAG system. This optimization ensures exceptional accuracy, reliability, and scalability. Key benefits of the Contextual AI Platform for enterprises include:
- Accelerated time-to-production through simplified deployment and tuning processes.
- Reduced maintenance overhead with a fully managed system that guarantees sustained, state-of-the-art performance over time.
- Simplified AI tech stack by consolidating development, evaluation, tuning, and deployment into a single system optimized for RAG workflows.
- Increased ROI by unlocking the potential of more complex, high-value use cases.
Contextual AI Outperforms Industry Benchmarks
Independent benchmark results have shown that the Contextual AI Platform outperforms other leading AI solutions across every major component of the RAG pipeline, including document understanding, reranking, and groundedness. The platform also achieved superior end-to-end accuracy, setting new standards in the industry for RAG benchmarks. These results build upon Contextual AI’s previous ground-breaking research, including LMUnit, a novel paradigm for evaluating AI performance.