Why Contextual AI
Expert AI trusted to deliver your most knowledge-intensive work, in weeks not months
How AI adoption has stalled
Most industries are struggling to adopt AI
Generic AI works when the task is generic. The moment a use case requires deep domain knowledge—engineering specs, medical records, financial regulations, manufacturing processes—off-the-shelf models fall short.
How LLMs are missing critical business context
Why off-the-shelf-AI can’t deliver complex use cases
While models are getting increasingly sophisticated, context engineering systems are required to give them the context they need to deliver reliable, quality outputs.
The DIY challenge
The cost, complexity and time it takes to DIY
Building your own context engineering stack means overcoming challenges that consistently prevent AI projects from reaching production
Our offering
How Contextual AI reduces time to production from months to weeks
Proven pre-built apps
A library of pre-built agent templates with high out-of-the-box accuracy on your most complex expert tasks. Focus on customization rather than developing code
Enterprise-ready platform
A full library of retrieval, reranking, orchestration, evaluation, and data ingestion tools—with the security, governance, and observability required to operate at enterprise scale.
Research-backed AI experts
A team of data annotators, AI engineers, and active researchers who apply the latest techniques to tune, optimize, and continuously improve your agents.
Customer testimonials
Contextual AI has been an important part of our AI transformation efforts. The technology has been rolled out to multiple teams across Advantest and select end customers, saving meaningful time across tasks ranging from test code generation to customer engineering workflows.
Keith Schaub, VP of Technology and Strategy, Advantest
30
10K
Expert AI, without the DIY
Learn how our platform and context layer can help you get a context-aware agent in production, in weeks not months