AI Training
Custom AI Training — RAG & Fine-Tuning for Your Business
A custom AI trained on your products, customers, procedures, and voice. Knows your operation as deeply as your best employee. Production-ready in 4–8 weeks. From $1,490.
Book a Free Discovery CallGeneric AI hits a ceiling. Trained AI doesn't.
ChatGPT and Claude are powerful general-purpose tools. The moment you need an AI that knows YOUR products, YOUR customers, YOUR procedures, YOUR voice — they hit a ceiling. They haven't been trained on your business and can't be without exposing your data publicly.
Our AI Training service builds custom AIs that genuinely know your business. Trained on your documents, your knowledge base, your past communications. Available wherever your team needs them — embedded in your website, your internal portal, your existing tools, or your AI Receptionist. Significantly cheaper than traditional custom AI development ($40,000–$300,000 from agencies in 2026), and built around the right architecture for your specific use case.
The Architecture Decision
RAG, Fine-Tuning, or both?
Most prospects don't know the difference between these approaches. Most agencies don't bother to educate. The honest answer matters because it determines whether your project costs $5,000 or $50,000.
RAG (Retrieval-Augmented Generation)
for knowledge and current information
The AI is connected to your knowledge base and looks up the right information at query time. Cheap to build, easy to update when your information changes, supports citation tracking. Best when your knowledge changes regularly and you need source-traceability.
Fine-Tuning
for format and voice
The AI is trained on your specific examples so it learns patterns — your brand voice, your output format, your domain language. More expensive, takes longer, harder to update. Best when output style matters more than knowledge content.
Hybrid (RAG + Fine-Tuning)
the enterprise approach
Both layers combined: fine-tune for behaviour, RAG for current knowledge. The most powerful and most expensive option. Justified when both precision and breadth genuinely matter.
We diagnose the right approach in the discovery call. We don't sell you fine-tuning when RAG fits better — and that honesty saves most clients $20,000+ on the typical project.
Decision Guide
Which approach fits your use case?
Use RAG when:
- →Your information changes weekly or monthly
- →You need to answer questions about products, documents, or FAQs
- →Citation and source-tracking matter (regulated industries especially)
- →Budget is under $10,000
- →You need speed-to-deployment
Use Fine-Tuning when:
- →Output FORMAT or STYLE matters more than knowledge
- →You have stable training data that won't change often
- →You need consistent brand voice or specific output structure
- →Inference speed is critical for high-volume use
Use Both when:
- →Enterprise-grade work where behaviour AND knowledge both matter
- →Stable behaviour rules plus dynamic knowledge needs
- →Budget supports the hybrid approach
Use Cases
Common AI Training projects
Every project is custom-scoped, but most fit one of these patterns:
Custom support chatbot. RAG over your docs, FAQs, and knowledge base. The most common engagement.
Internal company AI assistant. Your team queries the AI for company info, procedures, product specs, policies.
Specialist domain AI. Legal contract analyser, medical research summariser, accounting Q&A, technical support AI.
Customer-facing AI embedded in your product. A trained AI inside the app your customers already use.
Voice agent deep training. Specialist domain knowledge trained into your AI Receptionist for industry-specific expertise.
Brand voice fine-tune. AI that writes consistently in your tone for all content production.
Structured extraction agent. Pulling specific data from messy documents — invoices, contracts, forms — at scale.
Multi-agent system. Orchestrating multiple specialised AIs (researcher + writer + reviewer + checker) for complex workflows.
Pricing
Fixed-price packages
AI Readiness Audit
$1,490
Written assessment of your data, your use case, and the right architecture (RAG vs Fine-Tune vs prompts). Recommendation, cost estimate, 1-hour walkthrough. Often becomes the deposit on a build.
Knowledge Base Build
$2,990
Basic RAG: import your docs, build vector database, simple chat interface. Up to 100 pages of source material. 2-week delivery.
Custom Trained AI
$5,990
Full RAG implementation: multiple data sources, custom admin UI, retrieval optimisation, evaluation suite. Up to 500 pages of content. Integration with one existing system. 4-week delivery.
Fine-Tune Project
$7,990
LoRA fine-tune on your data plus RAG hybrid OR pure fine-tune for behaviour and style. Data preparation included. Evaluation suite. 6-week delivery.
Enterprise / Multi-Agent
From $14,990
Multi-agent systems, complex orchestration, governance documentation. 8-16 week delivery.
Voice Agent Deep Training
$2,990
Specialist domain training for an existing AI Receptionist. Industry-specific knowledge, edge case handling, custom flows.
Ongoing Plans
Tune-Up Retainer
$590/month
Monthly performance review, knowledge base updates, prompt refinement, hallucination monitoring.
Continuous Training
$1,490/month
Tune-Up plus 4 hours/month of feature development.
All prices include GST. API costs (Claude, OpenAI, Pinecone) are billed directly to you at cost. This service is the most R&D Tax Incentive-eligible work in our portfolio — AU companies under $20M turnover get a 43.5% refundable offset. We provide the documentation your accountant needs.
Process
Our 6-phase delivery method
Phase 1 — Scoping & use case definition (Week 1)
We define exactly what the AI will do — and what it won't. Decide RAG vs Fine-Tune vs Hybrid in writing. Sign-off before any build.
Phase 2 — Data audit & preparation (Weeks 2-4)
Get access to your source material. Clean, structure, chunk. This is 50-70% of project effort and the most underestimated step — but it's where quality is determined.
Phase 3 — Build & pipeline (Weeks 4-6)
RAG path: build the retrieval pipeline, embedding generation, query handling, response synthesis. Fine-tuning path: run training cycles, iterate until quality matches the bar.
Phase 4 — Evaluation & testing (Weeks 6-7)
Build the eval suite. Test against known questions with known correct answers. Test adversarial inputs. Measure retrieval relevance, answer quality, and hallucination rate.
Phase 5 — Integration & deployment (Weeks 7-8)
Wire the trained AI into your actual tools — Receptionist, app, website chat, internal portal. Build the admin UI so your team can update the knowledge base over time.
Phase 6 — Handover & ongoing training (Week 8+)
Plain-English documentation. Team training. Move onto the Tune-Up retainer for ongoing maintenance.
Why Choose Us
Why Australian businesses choose Ozis Digital for AI Training
Honest architecture decisions. We sell you RAG when RAG fits — even though fine-tuning costs more. Most agencies do the opposite.
Evaluation suite included. Every build includes a test set so quality is measurable, not just claimed.
Hallucination control built in. Every system is designed to say "I don't know" instead of guessing — with human escalation paths.
Australian data residency available. For health, finance, government, or regulated work, we use AU-hosted infrastructure end-to-end.
No vendor lock-in. RAG pipelines are exportable. Fine-tuned models are documented. Knowledge bases are yours.
R&D Tax Incentive support. We provide development documentation for the 43.5% AU offset claim.
Enterprise data privacy. We use enterprise API tiers from Claude and OpenAI that explicitly exclude your data from model training — documented in every SOW.
AI Training amplifies every other Ozis Digital service. Pair it with AI Receptionist to deepen your voice agent's domain expertise. Combine with Smart Websites & Apps to embed AI directly in your tools. Connect via Automation & Workflow to put AI decisions inside your business processes.
FAQ
Frequently asked questions
You can — for general questions. The moment you need it to know YOUR products, YOUR customers, YOUR procedures, ChatGPT alone falls down. It hasn't been trained on your business and can't be without exposing your data. What we build is an AI that knows your business as deeply as your best employee — only possible when we train it on your specific data.
Simple analogy. RAG is an AI with access to your filing cabinet — it looks up the answer each time. Fine-tuning is sending the AI to your training school — it learns patterns through repetition. For most businesses, RAG is the right answer: cheaper, faster, easier to update, and the AI can cite its sources. Fine-tuning is for specific behaviour problems — wanting the AI to sound exactly like your brand, or output in a very specific format every time.
No AI is 100% hallucination-free, but we minimise it three ways. RAG architecture forces the AI to ground answers in your real documents — when it can't find the info, we configure it to say "I don't know" instead of guessing. We build an evaluation suite that tests against known good answers. Every response can show its source, so you see exactly where the answer came from. Most production systems we build deliver under 5% hallucination rate on in-scope queries.
No. We use enterprise-grade API tiers that explicitly exclude your data from training. Anthropic, OpenAI, and Google all guarantee this on their business tiers. We document this in the SOW. If data residency is critical, we use Australian-hosted infrastructure end-to-end.
Basic RAG with your existing documents: 2-3 weeks to first working version. Production-quality with evaluation suite and admin UI: 4 weeks. Fine-tuning project: 6-8 weeks because data preparation is the bottleneck. Hybrid enterprise: 3-6 months. Anyone promising "AI trained on your data in a week" is shipping a toy.
Yes — and the question is what to do when it is. We design every system with a "don't know" state and human escalation. We build the evaluation suite so you can SEE the error rate, not guess at it. Most clients accept the trade-off: 5% error rate with full traceability is far better than 0% errors on no queries answered.
That's exactly what RAG is built for. You (or your admin) update documents in the knowledge base — the AI immediately has access. No retraining needed. If the answer is fine-tuning, updates require retraining cycles which cost $500-$5,000 each. This is one of the biggest reasons RAG wins for most use cases.
You own all your data, the training inputs, the knowledge base, and the prompts. For RAG, you own the entire pipeline — we hand over the GitHub repo. For fine-tuned models, you own the trained model weights on platforms like Together AI; for OpenAI fine-tunes, OpenAI hosts but you control. No lock-in.
AU companies under $20M turnover get a 43.5% refundable tax offset on eligible R&D, including AI/ML development. AI Training projects are usually fully eligible. We provide the technical documentation your accountant needs. Effectively makes the project cost feel close to half.
Because the work is technically deeper. Data preparation alone is 50-70% of project effort. Evaluation suites take days to build. Hallucination tuning takes iteration. Compared to traditional AI development pricing ($40,000-$300,000), our $5,990 Custom Trained AI is 5-10x cheaper — but it's still our premium service because it's real engineering, not configuration.
Build an AI that actually knows your business
A free 30-minute discovery call is the fastest way to find out which approach fits your use case — RAG, fine-tuning, or something simpler. We'll be honest about what's possible, what it costs, and whether you genuinely need a custom AI at all.
Book Your Free Discovery Call