RAG

Turn internal data into an evidence-based AI search system

RoaMeta organizes and preprocesses your manuals, consultation records, product materials, and operational know-how
to build a RAG system where AI finds the right documents and answers with cited sources.

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AI transformation starts with organizing your data

To use AI effectively, you need company knowledge that AI can reference.
RoaMeta organizes internal documents and operational know-how, builds a work database that AI can use,
and deploys an AI/RAG system that answers questions based on evidence.

A general AI chatbot alone cannot accurately answer company-specific work

General AI does not know your internal documents.

Wrong answers—hallucinations—can occur.

It is hard to reflect the latest policies or manuals.

It is difficult to verify the source of an answer.

Employees end up asking the same questions repeatedly.

Customer support, handoffs, and training materials vary by person.

RAG is a structure that finds accurate source documents before answering

User question
Relevant document search
Source extraction
AI answer generation
Source citation
Answer quality review
The core of RAG is not letting AI answer freely,
but finding evidence in company materials first and answering based on that evidence.

Core RAG build tasks

It does not end with uploading documents. We carry out the following work together for search accuracy and operability.

Document audit / Data audit

Review document formats, freshness, duplication, and scope of work.

Document preprocessing

Organize PDF, Word, Excel, and text files into forms AI can read easily.

Chunking strategy

Split documents into appropriate units to improve search accuracy.

Metadata design

Attach department, task, document type, date, version, and related information.

Embedding / Vector DB

Store documents in a vector DB such as ChromaDB for semantic search.

Search quality evaluation

Test search results and answer quality with sample questions.

Source document display

Let users see which documents an answer was based on.

Operations design

Plan for document add/update workflows and access control.

Work process

We proceed systematically from material review through demo and report delivery.

1

Sample material review

2

Scope selection

3

Document preprocessing

4

ChromaDB ingestion

5

Search testing

6

Report or demo delivery

Tech stack

Default setup

OpenAI API ChromaDB LangChain Python Django / PHP MySQL

Scalable options

Pinecone pgvector Weaviate Claude / Gemini FastAPI AWS / GCP

Vector DB, LLM, and server architecture may vary by project scale and security requirements. Start with a lightweight MVP and scale to Pinecone, pgvector, and similar systems for production.

See whether your company materials can be searched by AI

We can help you decide whether AX Explore, AX Build, or AI Feature Build is the right fit.

Contact us AI Automation