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Lankavia
03EXPERIMENTAL2026

Agentic Knowledge System

A retrieval and tool-use system that answers technical questions from a private corpus with citations.

Abstract cover artwork: a retrieval graph of cited documents around a central query node.
RAGLLMsSearchAgentic AI

Overview

Status: experimental. This page documents the engineering approach rather than a commercial outcome.

Retrieval quality, not model size, decides whether an answer is useful. This system treats search, ranking and verification as separate engineering problems, and refuses to answer when the corpus does not support a claim.

Problem

General assistants produce fluent answers that are difficult to verify against private documentation. The constraint was groundedness with an explicit, observable failure mode.

Approach

Documents are chunked by structure rather than size, embedded into a hybrid index, and re-ranked before generation. An agent plans the query, calls tools for structured data, and every sentence is checked against retrieved passages before it is returned.

Result

Answers arrive with citations to the exact passages used, and unsupported questions are declined instead of completed.