Not quite “better RAG for code”. The core idea is agentic discovery plus semantic search. Instead of static chunks pushed into context, the agent can dynamically traverse docs, follow links, grep for exact identifiers, and request only the relevant pieces on demand.
No manual chunking. We index with multiple strategies (hierarchical docs structure, symbol boundaries, semantic splitting) so the agent can jump into the right part without guessing chunk edges.
Context is selective. The agent retrieves minimal snippets and can fetch more iteratively as it reasons, rather than preloading large chunks. We benchmark this using exact match evaluations on real agent tasks: correctness, reduced hallucination, and fewer round trips.
To be reductionist, it seems the claimed product value is "better RAG for code."
The difficulties with RAG are at least:
1. Chunking: how large and how is the beginning/end of a chunk determined
2. Given the above quote, how much or many RAG results are put into the context? It seems that the API caller makes this decision, but how?
I'm curious about your approach and how you evaluated it.