The Contextual AI Platform is a managed context-engineering platform for building and hosting agents that answer questions by reasoning over an organization's own documents, such as technical documentation, specifications, and institutional knowledge. It is positioned as a context layer between generalist AI models and an organization's data, extracting relevant context from enterprise sources and passing it to a model.
The platform is used through a prebuilt agent, a natural-language prompt, a drag-and-drop interface, or developer APIs, and it includes built-in evaluation tooling for accuracy and groundedness. Described use cases include agentic search over datasheets and product data, device-log root-cause analysis, report generation for intellectual-property and compliance research, and structured extraction from large sets of unstructured documents.
Features
- Document-grounded agents: agents reason over enterprise documents and data rather than a model's general training data
- Multiple build paths: a prebuilt agent, natural-language prompt, drag-and-drop interface, or developer API
- Built-in evaluation: accuracy and groundedness checks are part of the platform
- Agentic search: automated responses to technical inquiries drawn from datasheets, call logs, and product data
- Deep research: compiles reports on topics such as intellectual-property conflicts and regulatory compliance from source documents
- Structured extraction: pulls key data out of large sets of unstructured documents
