Agents processing large documents have no built-in framework support for intelligent chunking, positional indexing, or overlap strategies to handle attention degradation at scale. Developers must manually implement these techniques, leading to wasted compute on retry cycles and fundamental indexing problems instead of higher-level prompt optimization. The absence of this as a platform primitive increases development time and error rates.

Agent developers manually implement chunking, overlap, and positional indexing for large documents, wasting days on plumbing instead of prompt logic — and often getting it wrong, causing attention degradation and failed retrievals.

AI agent developers and RAG pipeline builders who process documents exceeding context windows (10K+ tokens) as part of agentic workflows.

Every RAG tutorial reinvents chunking from scratch; LangChain's text splitters are primitive and context-unaware. A purpose-built library with semantic chunking, positional metadata, and overlap strategies saves real engineering days and improves output quality measurably.

Open-source Python/TypeScript library with paid cloud API — MVP supports semantic chunking (heading/paragraph-aware), configurable overlap, positional index metadata emission, and plugs into LangChain/LlamaIndex/CrewAI with one import.

Subset of the RAG/agent tooling market (~$2B by 2026); tens of thousands of developers building document-heavy AI apps, with ~$50-200/mo willingness for infrastructure tooling.