RAG & Knowledge Systems
We build intelligent retrieval-augmented generation systems — including vectorless, reasoning-based RAG via PageIndex — that let your teams search, query, and interact with your entire knowledge base using natural language.
How it works.
Your organization has years of accumulated knowledge locked in documents, wikis, databases, and email threads. RAG systems unlock all of it. We build retrieval-augmented generation pipelines that let anyone ask questions in natural language and get accurate, source-cited answers. We were among the first to implement PageIndex — a vectorless, reasoning-based RAG that replaces similarity search with human-like tree search. No vector DB, no chunking. Just reasoning over document structure. Systems powered by this approach have achieved 98.7% accuracy on financial document benchmarks, outperforming traditional vector RAG. We combine this with hybrid retrieval where appropriate, so you get the best of both worlds.
PageIndex: Vectorless, reasoning-based RAG
Traditional RAG relies on vector similarity — but similarity ≠ relevance. What retrieval needs is reasoning. PageIndex replaces vector databases and chunking with a hierarchical tree index and LLM-powered tree search. It simulates how human experts navigate complex documents through structure, not semantic distance.
No Vector DB
Reasoning over document structure instead of similarity search
No Chunking
Natural sections, not artificial chunks
98.7% Accuracy
FinanceBench benchmark — outperforms vector RAG
Traceable
Reasoning-based retrieval with page and section references
We were among the first to implement PageIndex for client deployments. Ideal for financial reports, regulatory filings, legal documents, technical manuals, and any long-form content where reasoning over structure beats semantic search.
How it works
- 1Build a hierarchical "table of contents" tree from your documents
- 2LLM reasons over the tree to navigate to relevant sections
- 3Retrieve exact pages and passages — human-like, traceable, accurate
What You Get
Ideal For
Step by step.
A proven methodology tailored to this service, designed to minimize risk and maximize impact.
Knowledge Audit
We map your existing knowledge sources, documents, databases, wikis, APIs, and identify what to ingest.
Retrieval Architecture
We design the optimal approach — PageIndex for reasoning-based retrieval over long documents, or hybrid with vectors when semantic search adds value. No cookie-cutter pipelines.
Search & Generation
We build the retrieval layer (tree search, hybrid, or both) and conversational interface with source attribution and accuracy safeguards.
Integration & Launch
We integrate with your existing tools (Slack, Teams, web portal) and launch with user training and feedback loops.
PageIndex is a reasoning-based RAG that builds a hierarchical tree index from documents and uses LLMs to reason over that structure — like how humans use a table of contents. No vector database, no chunking. Systems built on it achieved 98.7% on FinanceBench. We were among the first to implement it for client deployments.
Let's build together.
Tell us about your challenge. We'll respond within 24 hours with a clear path forward.
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