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Features
Agentic search harness
A retrieval agent that iteratively queries your corpus, reformulates on partial results, and stops when it has enough to answer. No RAG pipeline to assemble.
RL-trained model
We use reinforcement learning to train small, specialized models tailored to your corpus. The result is agentic search that’s 10x faster, cheaper, and more accurate than frontier models.
Context management
The agent manages its own context window across multi-step retrieval — compacting, pruning, and keeping only signal as it scans candidate documents.
Accuracy and recall without embedding engineering
No embedding model to pick, no chunk size to tune, no reranker to train.
Scalable storage
Documents are stored in object storage, so ingestion, indexing, and search cost a fraction of traditional search engines.
Metadata filters
Schematized typed attributes with comparison, set-membership, and logical operators. Combine natural-language search with precise structured predicates on any filterable field.
Quickstart
Getting started
Install the SDK, upload documents, and run your first search in a few minutes.
CLI
Use Charcoal from your terminal — manage namespaces, upload documents, and search.
Learn more
Namespaces & Documents
How documents are organized and schematized.
Search
Streaming, multi-turn sessions, and the search lifecycle.
Filters
The full filter syntax for narrowing results.
API Reference
Full endpoint documentation.