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RAG & Knowledge

RAG systems

LLM answers grounded in your documents — citations, freshness, hallucination control.

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RAG (Retrieval-Augmented Generation) is the enterprise standard when answers must rely on corporate documents. The LLM receives relevant chunks from the knowledge base and generates answers with citations — not from model memory.

Bober AI Systems builds RAG for support, HR, sales enablement and engineering. Kaspersky case: product documentation assistant with grounding and access control — production without RAG is not viable.

Technical contour: ingestion (parse, chunk, metadata), embedding model, vector DB (Qdrant, pgvector, OpenSearch), retrieval + rerank, prompt with context, guardrails. Index updates via webhook or schedule.

Quality measured with eval set: question → expected source → citation and factual check. Without eval, RAG degrades silently after launch.

Private LLM required when documents contain NDA and PII. Deployed in Yandex Cloud, Selectel or on-prem.

Timeline 4–6 weeks to production MVP. Scale to new departments via ACL and additional sources.

Bare LLM problems

  • —Model does not know your products, policies or prices
  • —Confident but wrong answers — legal and reputation risk
  • —Documents scattered across Confluence, SharePoint, PDF and CRM
  • —No process to refresh knowledge after policy changes

Deliverables

  • —Ingestion pipeline: PDF, DOCX, HTML, CRM → chunks → embeddings
  • —Vector store and retrieval with reranking
  • —Assistant API and UI with mandatory citations
  • —Re-index process and quality monitoring (eval set)

RAG project phases

01

Data audit

Sources, formats, ACL, update frequency. Map what to index.

02

Ingestion pipeline

Parsers, chunk strategy, metadata (department, product, date).

03

Retrieval tuning

Hybrid search, reranker, top-k, metadata filters.

04

Eval & production

Golden questions, regression tests, drift monitoring.

RAG architecture

  • —Sources: Confluence, S3, CRM attachments, email archives
  • —ETL: parse → chunk → embed → upsert vector DB
  • —Query: embed question → retrieve → rerank → build prompt
  • —LLM: private API, citation-required template
  • —Ops: re-index jobs, ACL sync, quality dashboard

RAG impact

−35%

employee time searching information

90%+

answers with citation when tuned

4–6 wks

MVP in production

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RAG FAQ

RAG vs fine-tuning?
RAG for fresh documents without retraining. Fine-tuning for style/niche domains — higher maintenance.
Which vector DB?
Qdrant, pgvector, OpenSearch — by scale and infra.
Multilingual?
Yes. Russian + English typical for RU enterprise.
Document updates?
Webhook on publish, nightly sync, chunk versioning.
ACL?
Metadata filters by role/department at retrieval.
Hallucinations?
Citation-only mode, «I don't know» on low score, human escalation.
Budget?
From €4,000 MVP. Private LLM separate or bundled.

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LLM answers grounded in your documents — citations, freshness, hallucination control.