Private AI Knowledge Systems for Australian Organisations
A RAG knowledge system is an AI assistant that answers from your own documents with cited, auditable sources instead of guessing. MicroPyramid builds private RAG-powered copilots, support assistants, and semantic document search for Australian government, healthtech, resources, and professional services teams: query your institutional knowledge in natural language, with source citations and role-based access.
The Australian Data-Sovereignty Challenge RAG Solves
Australia's public sector, health system, and growing technology sector face a distinct challenge: enormous institutional knowledge locked in documents, combined with privacy and sovereignty requirements that rule out processing sensitive data through overseas commercial AI services. Feeding government records or patient data into a US-hosted LLM API is not an option for many Australian organisations.
Private RAG systems solve this by running entirely within your environment. Documents are indexed and retrieved locally; the LLM inference can run on-premise or in AWS ap-southeast-2 (Sydney). No data crosses jurisdictional boundaries. The OAIC's accountability framework under APP 11 is met by design, not by policy statement.
We've been building secure, production AI systems for 12+ years. We understand that for Australian government and health clients, "private" is not a marketing word. It's a legal and technical requirement. Our architecture reflects that from the first line of code.
What We Build for Australian Teams
Six types of private RAG-powered knowledge systems, each shaped for Australian sector requirements and data-sovereignty obligations
Internal Knowledge Copilot
Give your Australian team a private retrieval assistant over internal policies, SOPs, and operational guides, with citations, role-based access, and audit trails aligned to your APP 11 security obligations.
- Document ingestion pipeline
- Semantic retrieval with citations
- Role-based access control
AI Support Assistant
Turn your support docs, product FAQs, and ticket history into an intelligent first-line assistant for your Australian customer base. Particularly effective for healthtech and professional services platforms managing sensitive support queries.
- Knowledge ingestion & indexing
- Retrieval-backed answers
- Fallback & escalation logic
Enterprise Document Search
Replace keyword search with semantic retrieval across contracts, tender documents, compliance filings, and technical specifications, built for the document-intensive realities of Australian resources, government, and infrastructure sectors.
- Semantic search & ranking
- Multi-format document support
- Filters & faceted navigation
Government & Health Records Q&A
Secure retrieval over policy documents, clinical guidelines, and regulatory correspondence, with full data sovereignty in ap-southeast-2 (Sydney). Built to meet the heightened sensitivity requirements of Australian government and health data.
- Sovereign data in ap-southeast-2
- Policy & guideline retrieval
- Access-controlled by role
Private Document Q&A
Access-controlled Q&A over sensitive documents (client files, board papers, health records, tender submissions) deployed in AWS ap-southeast-2 (Sydney) or entirely on-premise to meet agency data-residency requirements.
- On-premise or private cloud
- Australian data sovereignty
- Audit logging
Secure RAG with Citations
Every answer is attributed to its source with page-level citations: auditable, trustworthy, and safe for Australian regulated sectors including health, resources, financial services, and government procurement.
- Source-attributed answers
- Confidence scoring
- Hallucination mitigation
Where Australian Organisations Get ROI
The strongest use cases share one trait: large, growing bodies of knowledge that teams need to query, without risking data sovereignty
Australian Government & GovTech
Knowledge systems for federal and state agencies: policy retrieval, FOI preparation, and staff knowledge management with full data sovereignty in AWS ap-southeast-2 (Sydney).
Healthtech & Clinical Knowledge
Retrieval over clinical guidelines, formulary documents, and patient-facing FAQs, with privacy-by-design architecture that meets the sensitivity requirements of My Health Record and state health systems.
Resources & Infrastructure
Help operations teams in mining, energy, and infrastructure search across technical manuals, HSE procedures, regulatory approvals, and project documentation without hunting through contractor systems.
Australian SaaS Support
Reduce support ticket volume for SaaS products with large Australian and APAC customer bases: answers drawn from your docs, with escalation for anything outside the knowledge base.
Legal & Professional Services
Let Australian legal and advisory teams query matter files, precedent libraries, and regulatory guidance in natural language. Cited answers reduce risk and cut research time significantly.
Financial Services & AFSL Compliance
Secure retrieval over ASIC guidance, internal compliance manuals, and product disclosure statements: cited answers aligned to RG requirements for accurate financial services communications.
Custom RAG, Microsoft 365 Copilot, or Glean? How to Choose
Now that Microsoft 365 Copilot is rolling out across Australian government and enterprise, the real question isn't "AI or not". It's which approach fits your data, your sovereignty obligations, and how much you want to own. Here's the honest breakdown.
Custom RAG (what we build)
Own it outright
A private retrieval system grounded in your own documents, with page-level citations, your own access rules, and deployment in AWS ap-southeast-2 (Sydney) or on-premise. You own the source code and IP, no per-seat licence.
Choose it when
your knowledge lives outside Microsoft 365, you need data sovereignty or on-premise, you want answers embedded in your own product, or compliance demands auditable citations and access control you govern.
Microsoft 365 Copilot
Productivity layer
Generative AI woven through Word, Outlook, Teams, and SharePoint. Strong when your knowledge already lives inside Microsoft 365 and generic, conversational answers are good enough for the task.
Choose it when
your content is already in M365, you accept per-seat licensing, and you don’t need custom citations, bespoke access rules, or data residency beyond what the tenant gives you.
Glean
Horizontal search
A SaaS enterprise-search platform with prebuilt connectors across many tools. Useful for large organisations wanting cross-app search out of the box, accepting a third-party platform in the data path.
Choose it when
you’re a large org that wants connector-based search across many SaaS tools immediately and you’re comfortable with a vendor platform processing your index.
In practice many Australian teams run both: Microsoft 365 Copilot for everyday productivity inside the Office suite, and a custom RAG system for the regulated, sovereign, or product-embedded knowledge Copilot can't reach. We'll tell you when off-the-shelf is the right call, including when not to hire us.
Best Fit For
- you have policies, clinical guidelines, compliance docs, or operational knowledge Australian teams need to query
- answers need citations and auditability, important under Privacy Act APP 1 and APP 11 obligations
- you require data sovereignty: all data stays in AWS ap-southeast-2 (Sydney) or on-premise
- you need retrieval-backed answers grounded in your own regulated Australian data
Not the Right Fit When
- you mainly need AI embedded inside an existing product workflow rather than a standalone knowledge system
- your source content is thin, inconsistent, or not yet ready to index
- you expect autonomous answers without guardrails in health, government, or regulated financial workflows
- the goal is a public-facing generic chatbot with no grounding in your own documents
If you need AI embedded inside an existing product workflow, start with AI Feature Development instead.
Related proof from our portfolio: Refactored.ai shows AI-assisted retrieval in a production learning platform, while Bough Digital demonstrates AI-powered search and recommendation in a client-facing product. See the full global service page at ai-rag-knowledge-systems.
Why Australian Teams Work With Us
12+ years of delivery experience, shaped to fit AEST working rhythms, Australian privacy law, and AUD commercial terms
Your Afternoon, Our Morning
AEST puts us 4-5 hours ahead of IST. Your afternoon standup reaches us at start-of-day. We turn around feedback and code overnight so you wake up to progress, not a blocked queue.
Privacy Act & APP Compliance
We build with the Australian Privacy Principles in mind from day one: APP 11 security obligations, data minimisation, audit trails, and default deployment in AWS ap-southeast-2 (Sydney) for full data sovereignty. OAIC-accountable architecture on request.
AUD Billing via Stripe
Invoiced in Australian dollars via Stripe. No US-dollar conversion surprises, no foreign-transaction overhead: clean, predictable commercial terms for Australian businesses of all sizes.
Senior Engineers, Not Account Managers
The engineers who scoped your system build it. You have direct Slack access to the people writing the code, not a ticketing relay through account management layers.
How We Deliver
A focused, low-risk process designed to get Australian teams from problem to working system fast
Discovery & Scoping
Map Australian use cases, identify data sources, define Privacy Act / APP requirements, and set success metrics
Data Preparation
Document ingestion, chunking strategy, embedding pipeline, and vector index, hosted in ap-southeast-2 by default
RAG Architecture
Retrieval system design, LLM selection (private or API), prompt engineering, and context management
Build & Deploy
UI integration, accuracy testing, staged deployment, and monitoring, with full handover documentation
RAG & AI Technology Stack
We select models and infrastructure based on your Australian data-sovereignty, privacy, and performance requirements, not on defaults
AI & Retrieval
Data & Storage
Infrastructure
How to Get Started
We recommend a Discovery Sprint: low risk, clear output, an Australian Privacy Act review, and a foundation for everything that follows
RAG Discovery Sprint
Map your use case, assess data sources, and get an architecture and Privacy Act-aligned implementation roadmap
- Use-case mapping & data review
- Architecture recommendation
- Privacy Act / APP compliance assessment
- Implementation roadmap
Knowledge Copilot MVP
Full build of a retrieval-based assistant with UI, source citations, and Australian data sovereignty
- Document ingestion pipeline
- Retrieval + LLM integration
- Web interface with access control
Ongoing RAG Expansion
Continued iteration on your AI knowledge system as your data and use cases grow
- Additional data sources
- Quality & accuracy improvements
- Analytics & monitoring
Client Projects & Case Studies
Products and platforms we've built for clients in Australia, UK, and worldwide over 12+ years
Refactored
AI-assisted Python learning platform with adaptive exercises and retrieval-backed tutoring at scale
See AI proof
Bough Digital
UK digital marketing agency: AI-powered search visibility and recommendation workflows
Read case study
PRO Music Tutor
Premium online learning platform connecting students with world-class instructors
See more work
CREDITABLE
Employee financial wellness platform for savings, loans, and workplace financial services
See portfolioFrequently Asked Questions
Straight answers to what Australian founders, CTOs, and compliance leads ask before building a RAG knowledge system.
What is a RAG knowledge system?
A RAG (retrieval-augmented generation) knowledge system is an AI assistant that retrieves the most relevant passages from your own documents and uses them to generate an answer with cited sources, instead of relying on what a language model memorised from the public internet. Because every answer is grounded in your content and attributed to its source, it stays accurate, auditable, and current as your data changes, which is what makes it safe for Australian regulated and government work.
Can you build a RAG system with full Australian data sovereignty?
Yes. We deploy by default in AWS ap-southeast-2 (Sydney) so your documents and embeddings never leave Australia, and we can run the entire system on-premise or in your own private cloud where an agency or DPO requires it. We build with the Australian Privacy Principles and the Privacy Act 1988 in mind from day one (data minimisation, role-based access, and full audit logging aligned to your APP 11 security obligations), and we architect with IRAP and agency data-residency expectations in view for government work.
How is a custom RAG system different from Microsoft 365 Copilot or Glean?
Microsoft 365 Copilot and Glean work well when your knowledge already lives inside their ecosystem and generic answers are acceptable. A custom RAG system is the better choice when you need answers grounded in data they don’t reach, page-level citations, your own access rules, Australian data residency or on-premise deployment, or a copilot embedded in your own product, and when you want to own the system outright rather than rent per-seat licences indefinitely. Many Australian teams run Copilot for everyday productivity and a custom RAG system for the sovereign or regulated knowledge it can’t touch.
How do you stop the AI from hallucinating or inventing answers?
Every answer is grounded in retrieved passages and attributed with page-level citations, so a user can verify the source before trusting it. We add confidence scoring, fallback and escalation logic when retrieval is weak, and an evaluation pass on your real questions before launch, so the system says “I don’t know” or escalates to a human rather than making something up. For Australian government and health teams, that auditability is the difference between a usable tool and a compliance risk.
Can it work for APRA-regulated or government teams?
Yes. Cited, access-controlled retrieval is a strong fit for APRA-regulated financial services, ASIC-accountable advice teams, and Australian government and health bodies: secure Q&A over compliance manuals, clinical guidelines, product disclosure statements, and policy libraries, with source attribution so nothing gets misquoted. Australian data residency, audit logging, and per-team access controls are designed in, not bolted on, with awareness of APRA CPS 234, APP 11, and FOI-sensitive workflows.
You’re an offshore team: how do you handle Australian time zones and seniority?
AEST puts your afternoon at the start of our India team’s day, around 4.5-5 hours of overlap, so we turn around feedback and code overnight and you wake up to progress rather than a blocked queue. You work directly with the senior engineers who scoped your system on Slack, not an account-manager relay, and we invoice in Australian dollars via Stripe with no US-dollar conversion overhead. Offshore done this way is an advantage: a near round-the-clock build cycle with senior people, Australian data sovereignty, and AUD commercial terms.
What drives the cost of an AI knowledge system?
Cost is driven by the number and messiness of your data sources, how much cleaning and chunking the documents need, your access-control and audit requirements, whether you deploy in AWS ap-southeast-2 or fully on-premise, and how deeply the copilot integrates with your existing systems. We scope the smallest valuable version first in a discovery sprint and give you a fixed estimate in AUD before any build begins, so there are no surprises. Pricing is handled directly in conversation, not published as a one-size band.
Do we own the source code and IP?
Yes. You own all source code and intellectual property we build, committed to your repositories as we go, so there is no vendor lock-in and no per-seat platform rent if you later bring the system fully in-house. The same senior engineers who run discovery write the code and stay reachable on Slack. You are not handed off to an account manager once the build starts.
Ready to Build Your Australian Knowledge System?
Start with a free discovery call. We'll assess your use case, your Privacy Act obligations, and your data sources, and propose a concrete first step with no obligation.