Internal RAG

That big-budget RAG project —
do you really need it?

Building “an AI that answers from our documents” usually means months and a large budget with an outside vendor. But what the company actually wanted was one thing: put documents in, get answers out.
Wisdombase kept that one thing and removed everything else.

How a RAG build usually goes

These are the stages on a vendor’s proposal. Each one is meetings, decisions and cost.

1
Collect & organize documents

Meetings on which department’s documents go in

Gather scattered HWP, PDF and scans, pick the latest versions, build an inventory.

1–2 wks
2
Preprocess & clean

Scanned PDFs yield no text, so redo them

OCR, strip tables and headers, fix broken encodings, QA the extracted text.

2–3 wks
3
Design a taxonomy

Define how to split policies, manuals and FAQs

Write the metadata schema, tagging rules and access-control design.

1–2 wks
4
Decide a chunking strategy

Nobody can answer “how many characters per chunk?”

Fixed length vs paragraph vs clause, overlap percentage, exceptions per document type.

2+ wks
5
Pick an embedding model

The famous model may be worse on your documents

Compare candidates, validate Korean performance, review pricing, dimensions and licenses.

2–3 wks
6
Build & run a vector DB

A whole new system: servers, indexes, backups

Choose a DB, provision infrastructure, design indexes, build a re-indexing pipeline, monitor.

3–4 wks
7
Tune search quality

“Why did it pull the wrong document?” on repeat

Hybrid search, reranking, diversification, building an eval set and iterating.

3–6 wks
8
Integrate & hand over

Once it’s built, who maintains it?

AI tool integration, permissions and audit design, re-indexing rules for document updates.

2–4 wks
Typical timeline3–6 months
What the quote saysA large budget
What you’re left withA system that needs constant care

And all of it has to run again every time documents grow. Every new department, new policy, new revision.

Wisdombase erased all eight stages

Not removed — automated. Your only job is uploading documents.

The old way

  • Document collection meetings
  • Outsourced preprocessing & OCR
  • Taxonomy design docs
  • Chunking strategy meetings
  • Embedding model experiments
  • Vector DB build & operations
  • Endless search tuning
  • Integration & handover

3–6 months · a large budget

→

Wisdombase

  1. UploadHWP, PDF, Word and scans as they are — thousands at once via ZIP
  2. WaitExtraction, structuring, chunking and embedding run automatically (strategy chosen per document)
  3. Paste the URLPaste the MCP URL into ChatGPT or Claude settings

5 minutes · zero code

The decisions experts spend weeks on — already made

Chunking strategyA lightweight model reads the document type and picks clause, Q&A or section splitting itself
Embedding model5 models benchmarked on real Korean company documents (the most expensive came last)
Search qualitySemantic + full-text hybrid (RRF) and de-duplication (MMR) built in
OperationsEdit a document and only that page is re-indexed — no rebuild projects
Technology in detail →

So who uses it

Organizations too small for a “RAG project” start today.

Start with one team

No IT approval, no budget request — a team lead uploads twenty documents and it’s running. If it works, widen it to the whole company.

A whole small business

No dedicated developer needed. Admin or HR uploads policies and manuals, and every employee gets answers from company rules in their own AI.

Security-conscious organizations

Lock it private and open it only with per-member access keys. Originals are deleted instantly, chunks and embeddings are stored encrypted, and there’s an audit log.

Individuals & solo founders

Upload your notes, files and contracts and it becomes your personal memory. Your AI starts answering from your own records.

It takes 5 minutes — start for free

No credit card · Try it with a few documents first