Services · RAG Knowledge Systems
RAG Knowledge Systems: AI that answers from your own documents
ChatGPT-style answers grounded in your policies, manuals and product catalogues — with citations on every response. Fixed scope, live in 4–6 weeks.
Who this is for
Built for businesses like yours
Law firms & conveyancers
Precedents, retainer terms and matter checklists answered with pinpoint citations — no more digging through shared drives.
Clinics & healthcare practices
Front-desk and clinical staff get instant answers from your policies and procedure manuals, privacy-safe by design.
Trade & field-service businesses
Spec sheets, safety procedures and quoting guides available to every tech on the road, from their phone.
Franchises & multi-site businesses
One source of truth for every location: ops manuals, marketing guidelines and HR policies, always current.
What's included
Everything in the fixed fee
- → Document ingestion pipeline — PDFs, Word, web pages, spreadsheets
- → Vector database setup with automatic re-indexing when documents change
- → Answer layer with source citations on every response
- → Access controls — staff only see what they are allowed to see
- → Web chat interface, or Slack / Microsoft Teams integration
- → Handover documentation plus team training session
Explainer
What is RAG? (Explained for business owners)
Standard ChatGPT answers from what it learned on the public internet. Ask it about your refund policy or your product specs and it guesses — confidently, and often wrongly.
RAG — Retrieval-Augmented Generation — fixes that. When someone asks a question, the system first retrieves the relevant passages from your own documents, then generates an answer based only on what it found. Every answer comes with a citation, so your team can check the source in one click.
The result: an assistant that knows your business as well as your best employee — available to everyone, all the time.
Decision guide
RAG vs fine-tuning — which one do you actually need
RAG — what most SMBs need
- →Your facts change often: prices, policies, stock, staff.
- →You need answers that cite their source.
- →You want updates to take effect the day a document changes.
- →Lower cost, faster to ship, easy to audit.
Fine-tuning — the exception
- →You need a specific tone, style or output format baked in.
- →The knowledge is stable — it rarely changes.
- →You have thousands of example outputs to train on.
- →Higher cost, slower to update, harder to audit.
In practice, most businesses need RAG for what the AI knows — and, if anything, light prompt design for how it talks. We'll tell you honestly which camp you're in on the scoping call.
Architecture
Typical setup: documents → search → answer → citation
Step 01
Document ingestion
Your PDFs, manuals, catalogues and web pages are chunked, cleaned and loaded into the pipeline.
Step 02
Vector search
Each question is matched against your documents by meaning, not just keywords — "refund" finds "returns policy".
Step 03
Answer layer
The AI writes a plain-English answer using only the retrieved passages, with guardrails against guessing.
Step 04
Citations
Every answer links back to the source document and section, so trust is one click away.
Pricing
From $9,500, fixed. No hourly billing.
Enterprise knowledge platforms quote $50k+ before licensing, and a knowledge manager hire runs $80k+ per year. Our fixed fee covers the full build — ingestion, search, answer layer and training — with no per-seat pricing.
FAQ
Common questions
What does RAG stand for?
Retrieval-Augmented Generation. Instead of letting an AI guess from its general training, a RAG system first retrieves the relevant passages from your own documents, then writes an answer grounded in them — with citations you can verify.
Is my data safe? Does it train public AI models?
No. Your documents live in a private vector store we set up for you, and we use API tiers that contractually do not train on your data. Access controls decide who can ask what.
How is this different from uploading files to ChatGPT?
Three ways: every answer cites its source document; the index updates automatically when your documents change; and access controls mean staff only see what they are allowed to see. ChatGPT file uploads do none of that.
What kinds of documents can it handle?
PDFs, Word documents, spreadsheets, web pages, help-desk exports, product catalogues — if it can be exported as text, it can usually be indexed. We assess your document set during the free scoping call.
How much does a RAG system cost in Australia?
Enterprise knowledge platforms regularly quote $50,000+ for implementation. Bumblebee Studio's RAG builds are fixed-scope from $9,500, delivered in 4–6 weeks, with handover documentation included.
Ready when you are