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Retrieval-augmented generation

RAG development services in Australia

We build retrieval-augmented generation systems grounded in your actual content: answers with citations, Australian data residency options, and the assurance evidence government and regulated buyers ask for.

Talk to us about RAGAI for government
01 / How we build RAG systems

Build on your existing search

Query your existing index

If you already run Elasticsearch or Solr, your content is indexed. We connect a retrieval and generation layer directly to it.

Conversational answers

An AI model transforms search results into clear, natural responses your users can act on immediately, with the source cited.

Always in sync

The system reads from your live index. When content changes, answers update automatically.

Faster to deploy

No new content pipeline to build. Less duplication, less infrastructure and a shorter path to launch.

Custom RAG pipelines

Ingest any content source

PDFs, internal docs, legacy databases or content spread across systems. We process it all into a unified pipeline, and fix the corpus before we index it.

Vector embeddings index

Content is chunked, embedded and stored in a searchable vector index for fast semantic retrieval.

Grounded generation

A language model generates responses from retrieved content only, not from general training data, evaluated against a ground truth set before launch.

Citations and fallbacks

Every response links to source documents. When the system can't answer, it says so and routes users to the right channel.

02 / Benefits

Why grounded systems work

Grounded answers with citations

Every answer draws from your published content, not general AI knowledge, and links back to the source documents it came from. In regulated environments, that traceability is the difference between deployable and not.

Australian data residency options

Where residency matters, we design retrieval and inference to run in Australian regions, such as AWS Bedrock in Sydney. Our own AI workloads run in ap-southeast-2, so this is how we build by default, not an add-on.

Answers that stay current

Because the system retrieves from your live content or a regularly updated index, answers do not fall out of date when pages change.

Your guardrails, built in

You decide what the system can access, how it responds, and where it routes users when it cannot help. Retrieved documents are treated as untrusted input, with prompt-injection defence designed in.

Why grounded systems work

Grounded answers with citations

Every answer draws from your published content, not general AI knowledge, and links back to the source documents it came from. In regulated environments, that traceability is the difference between deployable and not.

Australian data residency options

Where residency matters, we design retrieval and inference to run in Australian regions, such as AWS Bedrock in Sydney. Our own AI workloads run in ap-southeast-2, so this is how we build by default, not an add-on.

Answers that stay current

Because the system retrieves from your live content or a regularly updated index, answers do not fall out of date when pages change.

Your guardrails, built in

You decide what the system can access, how it responds, and where it routes users when it cannot help. Retrieved documents are treated as untrusted input, with prompt-injection defence designed in.

ProofAll case studies
National Cancer Screening Register case study

National Cancer Screening Register / UX Research / Design Systems

Enterprise UX transformation & managed AEM support

A complete redesign with improved UX, a new design system, and ongoing managed support.

Read the case study →
03 / Further reading

How we think about RAG for regulated buyers

Most of our RAG engagements are for government and regulated organisations, so we have written up the decisions that matter before a system ships.

04 / Questions

RAG development: FAQ

RAG stands for retrieval-augmented generation. The system searches your content first, then uses an AI model to craft a response based on what it found. This keeps answers grounded in your actual content rather than relying on the AI’s general knowledge, and lets every answer cite its sources.

General AI tools are trained on broad internet data. They don’t know your specific content, policies or terminology. A RAG system connects directly to your content, so answers are specific to your organisation and verifiable against your source material.

No. If you have one of these in place, we can build on top of it. If not, we build a custom RAG pipeline that works with your content regardless of your current infrastructure.

Yes. Where data residency matters, we design the retrieval index and model inference to run in Australian regions, for example AWS Bedrock in Sydney (ap-southeast-2), which is where our own AI workloads run. Which jurisdiction your content fragments land in during inference is a procurement question, and we treat it as one from the first architecture conversation.

We design our implementations with guardrails that significantly reduce hallucination. The system responds based on retrieved content only, cites its sources, and acknowledges when it doesn’t have a relevant answer. No system is perfect, but grounding dramatically improves accuracy, and we measure it against a ground truth set before launch.

Most content types work: web pages, PDFs, Word documents, knowledge base articles, FAQs, product catalogues and structured databases.

Yes, and it is where most of our RAG work happens. Grounded systems suit regulated environments because responses are traceable to source documents, and we implement the controls those environments require: content filtering, audit logging, permission-aware retrieval and restricted knowledge boundaries. We are currently building the Knowledge Sharing Platform for the Victorian Collaborative Centre for Mental Health and Wellbeing, and we support the National Cancer Screening Register.

We work to our 2026 rate card (AUD 165 to AUD 275 per hour or AUD 1,320 to AUD 2,200 per day, ex GST). A system connected to an existing search index is a smaller engagement than a custom pipeline over a messy corpus, so we scope from your content and infrastructure, not from a template. We share an itemised estimate at the end of a scoping call.

A RAG assistant connected to an existing search index can be ready in weeks. A custom pipeline for more complex content typically takes a few weeks longer, with corpus preparation and evaluation the usual long poles. We’ll give you a clear timeline upfront.