All reference architectures

Enterprise Knowledge System

Unifying fragmented enterprise knowledge in one cited AI assistant

System connecting enterprise data sources to a secure RAG assistant
VALNOX / LABS / 01

Reference architecture overview

A RAG architecture connecting PDFs, wikis, tickets, and product documentation through permission-aware retrieval and citation.

This is not a client case study. It is a Valnox Labs reference architecture created to show production-oriented technical decisions transparently; it does not claim a delivered engagement or verified client outcome.

Data volume, latency targets, access controls, failure cost, and existing systems differ in every implementation. These layers are therefore not a recipe to copy unchanged; they identify the decisions that discovery must measure and validate. A production scope becomes concrete only with representative data, explicit success criteria, and an accountable operational owner.

01

Reference scenario

In this reference scenario, knowledge is fragmented across systems and users must search multiple sources for a current answer.

02

Architecture approach

The reference architecture combines source normalization, access controls, hybrid search, and reranking in one retrieval layer.

03

Example system design

Ingestion, metadata, permission filtering, retrieval, reranking, the LLM, citations, and feedback are designed as one observable flow.

Core system layers

  1. 01

    Source connectors

  2. 02

    Permissions and metadata

  3. 03

    Hybrid retrieval

  4. 04

    Cited generation

  5. 05

    Evaluation and feedback

Target system qualities

Cited-answer design

Permission-aware access

Retrieval quality measurable with an evaluation set

VALNOX / PROJECTS

Let’s adapt this architecture to your operation.

We will assess your data sources, user flow, and success criteria to define a practical first production scope.

Discuss a similar project
Direct email
info@valnox.ai
Location
Bilişim Vadisi, Gebze/Kocaeli, Türkiye
Delivery model
Founder-led, end-to-end