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Knowledge & search

Vector Knowledge Base AI Agent

An AI agent builds a vector base from your sources: normalizes, chunks, embeds and keeps the index fresh on change events. The foundation RAG runs on.

Vector Databases

“We want an AI that answers from our documents” almost always comes down not to the model but to the data. You can take the best LLM and still get confident inventions if retrieval feeds it garbage. The vector knowledge base agent closes that gap: it normalizes heterogeneous sources, cuts them into meaningful fragments, builds embeddings and keeps the index fresh — the foundation that RAG then runs on.

What it does

It pulls documents from your sources, cleans markup and duplicates, tags metadata and splits them into meaningful chunks by document structure. It computes embeddings and writes them to the vector index. On a document change event it re-indexes only that document rather than rebuilding the whole base — the index doesn’t go stale between manual runs. The output is clean, fresh, well-chunked retrieval, on top of which RAG systems and support agents work predictably.

Why it’s a separate agent

The quality of a RAG answer is decided less by the model than by the index beneath it: how sources are normalized, how chunks are cut, how fresh the index is. That’s an engineering task with clear levers — chunk size, metadata, re-indexing strategy — not “baked-in knowledge” inside the model. More on the engineering on the vector databases page; it’s assembled for your process on the same platform as the AI agents that run on top of this base.

How the chain works

  1. 01
    Source normalization · deterministic code

    Pulls documents from sources, cleans markup, drops junk and duplicates, tags metadata — garbage in means garbage in retrieval.

  2. 02
    Chunking · light model

    Splits documents into meaningful fragments by structure, not every N characters. Chunk size directly decides whether the right thing is found.

  3. 03
    Index build and update · embedder

    Computes embeddings and writes them to the vector index. On a document change event it re-indexes only that document — the index doesn't go stale.

Integrations

OpenAI YandexGPT Google Sheets

+ any external API

Cost calculator

200
2
Tokens, ₽/mo
—
Development, ₽
—
Support, ₽/mo
—

Estimate at a blended per-token rate (input+output). Exact cost depends on context length, number of calls and the share of manual review — we scope it to your process.

related cases

AI Systems

Enterprise Knowledge & AI Context Platform

faq

Straight answers

Is a vector base the same as RAG?
No, and it matters. A vector base is the store of embeddings and the search over them; RAG is the flow on top of it: retrieve fragments, pass them to a model, get an answer with a citation. This agent builds and maintains exactly the foundation — a clean, fresh, well-chunked index. Without it, a RAG agent will confidently answer from garbage.
Why not just load the documents into the model?
A model's context is limited and expensive, while a corporate base is thousands of documents that also keep changing. So knowledge lives in an external index and only the relevant part is pulled in per query. Retrieval quality is then decided not by the model but by how the sources are normalized and the chunks are cut.
How does the index stay current?
The agent runs on events: when a document changes in the source, only that document is re-indexed, not the whole base. So an updated policy reaches retrieval without a manual rebuild, and stale versions don't surface in answers. Schedule and triggers are tuned to how often your sources change.
Where do the sources come from?
From your systems on their contracts: file stores, knowledge bases, CRM, ticket history, exports. The agent normalizes heterogeneous formats to a single shape with metadata so retrieval works uniformly across the whole corpus instead of breaking on each new source.

Next step

Let's design an AI-native automation layer for your operations.

DBCV