CAP-01Active
Context-Aware Chunking
Splits documents at semantic boundaries, not arbitrary character counts. Tables, lists, and sections stay intact.
Intelligent document indexing that chunks, embeds, and organizes your documents for retrieval-augmented generation. Context-aware chunking preserves meaning across tables, sections, and page breaks.
Three essential steps to turn raw documents into precise retrieval for your agents.
Intelligent splitting that respects document structure. Tables, lists, and sections stay intact.
Multiple embedding models, vector + hybrid search. Choose your embedding provider without re-processing.
Precise retrieval with citations for AI agents. Documents become instantly available to assistents agents.
Raw documents context-aware chunking agent-ready retrieval
Built-in capabilities for production-grade document indexing.
Splits documents at semantic boundaries, not arbitrary character counts. Tables, lists, and sections stay intact.
Use OpenAI, Cohere, or custom embedding models. Switch models without re-processing source documents.
Index text, tables, charts, and images together for complete document understanding.
Set up once, documents auto-index as they arrive. Handles updates, deletions, and version changes.
Built-in evaluation tools to measure retrieval quality and tune chunking parameters before production.
Indexed documents become instantly available to any assistents agent through the Context Engine.
Retrieval latency, model flexibility, and pipeline freshness, read off the instruments.
Intelligent indexing is the foundation of assistents’ Context Engine. Once documents are indexed, any agent can retrieve relevant context in milliseconds, grounding responses in your data.
Set up intelligent indexing in minutes. Test retrieval quality with built-in evaluation tools. Connect to any assistents agent.