A Small Business Owner's Guide to Pricing and Piloting RAG-Powered AI

What AI Actually Costs

TL;DR:

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  What we're talking about: Implementing a private AI answers assistant that searches your own docs (SharePoint/Google Drive/email, etc.) and drafts answers with source links so staff can trust and act.

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  Where it pays first: Support/IT, Operations/HR, Sales/Success: Fast, consistent answers to repeat questions; updates flow from your latest documents using RAG methods (Retrieval-Augmented Generation. No model retraining needed).

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  What are the cost components:
  One-off: connect/clean data, first-pass embeddings, build index, simple chat UI + SSO/permissions.
  Monthly: AI usage (API calls), index hosting/storage, ongoing ingest of new/changed docs, light support/monitoring.
  Often missed: development and integration, identity/permissions mapping, OCR for old scans, guardrails & logging, training/change mgmt, backups/DR, legal/DPAs, cost caps.

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  How to pilot (2–4 weeks): Pick one library (200–1,000 files) and 10–20 real questions; give 5–10 staff access; set a hard budget cap with alerts; track cost per answer, time-to-answer, deflection rate, and accuracy with citation; decide to extend or stop.

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  Security & ROI: Data stays private and permissioned; ask where it's stored/processed and keep audit logs. Simple maths: 20 staff × 15 min/day ≈ $60k/year lost to searching—your assistant should beat that by cutting 20–60% of time-to-answer. Keep bills down by sending only relevant snippets, caching repeats, using smaller models first, and enforcing monthly spend caps with a kill-switch.

You know AI is important. You've heard the hype.

But you're often too busy to sit down and evaluate where the compelling business cases are for your business. It all feels a bit complicated, and expensive.

Fortunately the dust is starting to settle around AI. The upside is you're not going first anymore. There's been a solid couple of years of empirical data coming back about implementing AI in business.

However AI projects often run over budget because of hidden costs. Not to mention ongoing recurring costs that can and will get away from you if not anticipated.

The Pitch Versus Reality

Here's the tantalising promise:

AI that knows your business inside and out, delivering instant answers from your company's knowledge base.

The ROI has to add up though. Like any project.

Most organisations jump into AI (Retrieval-Augmented Generation - RAG) projects without understanding the true costs, and end up facing budget blowouts that could have been avoided.

If you're evaluating how to introduce AI for your business, you need a clear-eyed view of what this tech actually costs.

The first step? You should start your AI journey with piloting a RAG system implementation.

Retrieval-Augmented Generation (RAG)

Use AI to unlock your company's own data and experience with Retrieval-Augmented Generation (RAG) based AI systems..

It's definitely the place to start for any business to make AI actually useful at work, and to kick-off your AI journey.

What is RAG?

A RAG-powered AI solution is smart search and trustworthy answers from your business documents and data.

It finds the relevant passages across your business data - Eg. SharePoint/Google Drive/PDFs. Then an AI model composes an answer with citations to those passages, so staff can act with confidence .

A RAG-based AI solution (Eg Chatbot that knows your business data) is like a staff member who's been with you "forever" and remembers everything. They've memorised every document, spreadsheet, email, and procedure your business has ever created.

Except AI tools designed to securely retrieve information from your business data are available 24/7, never take a sick day, and can recall a specific detail from a 2018 report in half a second. All the while providing a citation, linking back to the source for you to verify.

That's RAG. It's an AI application retrieving "grounded" (links to the sources cited) answers based on your private business knowledge, not the internet (though - yes you can go hybrid and retrieve answers from the Internet too. That's "Search-Augmented Retrieval". We'll get to it.)

Why RAG AI Assistants Matter (Outcomes)

• •An internal business AI solution provides faster answers: 20–60% lower time-to-answer for staff and customers.
• •Consistency: Eg. Answers reflect the latest Standard Operating Procedures,, not staff memory.
• •Trust & compliance: Every answer shows where it came from; audit‑ready logs.
• •Low change cost: Update a doc → answers update immediately (no model retraining).

Where It Helps First - Good places to begin integrating AI into your business

• •Support & IT: Fewer escalations, tighter SLAs, and a helpdesk that grows without adding more staff.
• •Operations and HR: Policies, forms, who‑does‑what, "what's the latest rule?"
• •Sales/Success: Pricing, terms, case studies during calls.

Security & Privacy

This is the single biggest difference between using a private RAG system and a publicly available AI solution like ChatGPT.

The AI solution built for your business is architected so that information remains completely private and secure.

Your assistant runs on your data, your controls. Content is not used to train public models. Access follows your permissions (e.g., Microsoft Entra / Google Workspace). Keep audit logs and align with the Privacy Act 1988 (Cth) and the OAIC Notifiable Data Breaches scheme; for most businesses this means role-based access, encryption at rest/in transit, and basic logging with a retention policy.

Putting RAG to Work: Example Scenarios

The Construction Firm:

A project manager on-site needs to check a variation to a building contract from six months ago. Instead of calling the office to get someone to dig through emails, they ask their phone, "Show me the approved variation for the 123 Smith Street job." The AI Assistant based on RAG instantly provides the right PDF, saving 30 minutes.

An Accounting Practice:

A client calls with a question about advice provided in the previous financial year. Instead of manually going through folders and files, a junior accountant asks the RAG-based AI Assistant, "Summarise our advice to ACME Pty Ltd regarding Fringe Benefits Tax in March 2024." The AI app verifies their credentials and permissions to the information - and responds with a summary with links to the source emails.

Manufacturing:

When a machine on the factory floor has an issue, the maintenance team can securely ask their internally implemented AI app, "What's the standard procedure for a pressure fault on the XYZ Model 5?" instead of hunting for a printed manual. The RAG AI system pulls up the exact section from the correct technical guide.

The RAG-Based AI Cost Breakdown

AI is a tool, and like any tool, it needs to pay for itself.

The costs to put in and run an AI RAG-based app in your business are really driven by two main things:

1. •The amount of data you have, and
2. •How much you use it.

The 3 Phases of AI implementation costs

Next we'll look at AI implementation costs over three phases:

1. •Upfront setup costs.
2. •Ongoing recurring costs
3. •Hidden costs. AI project costs that are often missed

Phase 1: The Upfront Setup Costs

• •Ingest your sources (business data): First, the system needs to ingest and clean (normalise/deduplicate/structure) your data to make it ready for storing in a (vector) database. It's a variable effort/cost. If your info is in SQL databases this is less development effort and cheaper to "extract" and tidy up than if it's locked in scanned PDFs with tables and multiple columns, as more work is needed to clean it up and structure it. The amount of data preparation is dependent on the type of source data.
• •Chunking and Embedding: Your documents are broken into multiple smaller logical "chunks", enriched with metadata present in every chunk. Each chunk is then converted (embedded) into a number called a vector. This initial embedding of all your existing data is a significant one-time cost, typically priced per million "tokens" (pieces of words) processed.
• •Indexing and Storing in a Vector Database: All those vectors are stored in a vector database - so called as they are designed to search vectors instantly.
• •The Initial Build: This is the cost to design and develop the retrieval system and integrate it with the software you already use (Eg. Email, documentation tools, CRMs, Support, Accounts, files, productivity and other systems/apps used in your business). Privacy and Security controls are a critical part of this phase.

Phase 2: The Ongoing Running Costs (The Monthly Bills)

• •AI API costs for LLM Inference: This is typically the single largest ongoing cost. Every time a user asks a question, you pay the AI model provider for API calls to the AI model used in your RAG implementation (like OpenAI or Google). The cost is based on the information you send over the API to the model (input tokens) and the answer it generates (output tokens).
• •Vector Database: Established as a one-time setup cost in Phase 1 above. The Vector Database is now mentioned here as a monthly cost. Either as a cloud service subscription based on storage volume, or self-hosted on-premises.
• •Keeping the Knowledge Fresh (Data Updates): Your business is always creating new information. Every time you add new source documents, they need to be processed via your "RAG Document Ingestion and Indexing Pipeline". This is the same process you saw in Phase 1, but is now also a recurring operational cost to regularly ingest (extract, clean-up, chunk, embed, index, and store for retrieval).

Phase 3: The Often Missed AI Project Costs

(These might be the ones owners later say, "No one told me about that.")

• •Identity & permissions mapping: Which staff can see what.
• •Licences & API limits: Microsoft/Google plan prerequisites; Graph/Drive API quotas; third-party connectors.
• •OCR/clean-up for legacy scans: if you have lots of scanned invoices/manuals.
• •Guardrails & quality checks: light fact-checking, banned topics, brand tone; adds a small per-query cost.
• •Logging/analytics: usage dashboards to spot savings and issues.
• •Change management & training: show staff how to ask better questions; 1–2 short sessions + a cheat-sheet.
• •Data governance chores: doc owners, review cadence, "who updates what when".
• •Ongoing Maintenance: An AI system deployed in your organisation needs people to build and maintain it. This includes model updates, performance optimization, security patches, and system monitoring.
• •Backups & disaster recovery: snapshots of the index and source store.
• •Security reviews: basic vulnerability scan / annual pen-test (scaled to SMB).
• •Legal admin: supplier DPAs, terms review (especially if data leaves Australia).
• •Environment split: dev/test/prod
• •Cost controls: budget caps, alerts, and a "kill-switch".

Deliberately left out (on purpose): fine-tuning foundation models, custom re-ranker implementation, multi-region latency tuning, advanced retrieval algorithms. They might be integral to your project phase 1, and most likely will be part of your conversation later, but for day-one they add cost and complexity.

ROI. What is the cost of not solving these problems with AI?

What is the cost of not solving problems now solvable by AI tools today?

An example:

Say you have 20 staff, and they each spend 15 minutes every day searching for information in messy shared drives and old emails.

• •15 mins/day x 20 staff = 300 minutes (5 hours) of lost productivity per day.
• •At an average loaded cost of $50/hour, that's $250 a day, $1,250 a week, or over $60,000 a year that you are spending right now just on inefficient searching.

Suddenly, an upfront investment in an AI solution to solve that problem becomes a compelling argument. And it goes beyond time savings:

Knowledge Retention: When staff leave, their knowledge walks out the door. A RAG-based solution is your company's permanent memory. It captures that knowledge so it's never lost.

How to Get Started

The smart way to start building with AI is to prove the value on a smaller project.

Start Small with a Pilot Project

Don't try to connect your entire business on day one. Pick one high-pain, high-value area.

Some example use-cases:

• •Start with just your HR policies to create a 24/7 chatbot for staff.
• •Begin with only your last 5 years of sales proposals to help your team build new quotes faster.
• •Focus on technical manuals and safety procedures to provide instant answers.

A pilot project lets you prove the value of AI with a much smaller initial investment and lower risk.

How to run a low risk 2–4 week pilot

1. •

   Pick one library: e.g., HR policies or product manuals (200–1,000 files).

2. •

   Pick 10–20 real questions from recent tickets/emails.

3. •

   Give access to 5–10 staff and measure time-to-answer + accuracy.

4. •

   Set a hard budget cap and review "cost per answer" weekly.

5. •

   Decide: extend, pause, or roll into a second area of your organisation.

Your Checklist: 6 Questions to Ask Any AI Partner

Here's what to ask:

1. •How will you ensure the security and privacy of my data?
2. •Can you explain your full pricing model? What are the setup fees, and what are the ongoing usage costs?
3. •Can you show me a demo using a few of my own sample documents so I can see it work in my context?
4. •What kind of support and maintenance is included after we go live?
5. •Who on your team will be my primary contact, and what is their experience with businesses like mine?
6. •Where your data is stored and processed (Australia or overseas)?

Beat the AI Project Statistics

AI is no longer just for the big end of town. Start by focusing on a single real business problem. With an understanding of the costs you can beat the dismal stats on AI projects!