Built by the pioneers of RAG, the Platform delivers production-grade accuracy to support specialized knowledge tasks and boost expert productivity
MOUNTAIN VIEW, Calif., Jan. 15, 2025 -- Contextual AI, the enterprise RAG company, today announced the general availability (GA) of the Contextual AI Platform, helping enterprises build specialized RAG agents to support expert knowledge work.
While there is broad consensus that general-purpose AI agents are poised to streamline many generic tasks, Contextual AI believes that specialized RAG agents will instead be required to transform high-value, domain-specific knowledge work. For subject-matter experts in any organization, this means having AI tools that match their level of expertise and can be trusted to address complex or technical problems with confidence. With today's GA announcement, Contextual AI has released a set of public benchmark results demonstrating state-of-the-art performance in advanced RAG workflows across diverse enterprise domains, outperforming frontier models like Anthropic's Claude 3.5 Sonnet and OpenAI's GPT-4o.
The Contextual AI Platform includes all the capabilities needed to build, evaluate, and deploy specialized RAG agents, helping enterprises accelerate their higher-value AI initiatives from pilot to production and deliver near-term ROI. Fortune 500 companies across financial services, technology, and professional services trust Contextual AI to improve employee productivity and navigate vast amounts of noisy enterprise data.
Qualcomm (QCOM), a global semiconductor leader, chose Contextual AI after finding other RAG solutions inadequate for its highly technical and mission-critical Customer Engineering needs. The Customer Engineering team has deployed Contextual AI in production to quickly and accurately synthesize information from tens of thousands of technical documents to streamline resolution of 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 this GA announcement, enterprises can now use the Contextual AI Platform to create specialized RAG agents that intelligently orchestrate retrieval and generation based on conversational context, delivering highly accurate responses for complex knowledge tasks across large corpora of structured and unstructured enterprise data.
"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."
The Contextual AI Platform is powered by RAG 2.0, which involves jointly optimizing the retriever and the generator in the RAG system to deliver exceptional accuracy and groundedness at scale. The Platform provides the following benefits to enterprises:
- Faster time-to-production through streamlined deployment and tuning
- Reduced maintenance overhead with a fully managed system that can sustain state-of-the-art performance over time
- Simplified AI tech stack, with development, evaluation, tuning, and deployment in a single system optimized for RAG
- Greater ROI from unlocking more complex use cases
In published benchmark results comparing Contextual AI to other leading AI companies, the Contextual AI Platform was found to outperform other solutions across every major component of the RAG pipeline (e.g. document understanding, reranking, groundedness) as well as end-to-end accuracy, setting a new state of the art on a variety of RAG benchmarks. These benchmarks follow other industry-leading research publications from Contextual AI, including LMUnit, a new paradigm for AI evaluation.
Read more about the news on the Contextual AI blog.
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