Service · Australia-wide

AI Integration Services

Practical AI for Australian businesses that ships to production and stays shipped. We build the boring guardrails — citation enforcement, confidence scoring, evals, fallbacks — that turn an LLM demo into a system you can put in front of customers without losing sleep.

What we actually build (and don't)

AI is in a hype cycle. Half of what we're asked to build, we talk clients out of. Here's what we believe ships reliably in 2026 and what doesn't.

What works

  • Internal document Q&A (RAG) — staff query policies, contracts, specs in plain English
  • Inbound triage — classifying emails, leads, support tickets to the right queue
  • Document extraction — invoices, applications, contracts → structured data into your systems
  • Drafting and summarisation — first-draft replies and proposals that a human reviews
  • Code-aware tooling — internal devtools that know your codebase
  • Search over your business — semantic search across CRM notes, tickets, documents

What doesn't ship reliably yet

  • Fully autonomous agents acting on real money or contracts without human approval
  • Customer-facing chat without escape hatches and clear scope boundaries
  • Anywhere a hallucination is genuinely dangerous (legal, clinical, financial advice)
  • Replacing skilled judgment workers who use tacit knowledge — you'll get a brittle system that breaks at the edges

Our build process

1. Use-case validation (1 week)

We run a paid 1-week validation: we build a working prototype on real data, measure it against a 50-question eval set, and report accuracy. Output: a written go/no-go recommendation. Roughly 30% of the prototypes we build come back as "don't proceed". That's a feature.

2. Production build with guardrails

Every production AI system we ship includes: RAG with citation enforcement, confidence scoring with human-review fallback, an eval suite that gates deploys, full prompt and response logging, and a kill switch. Without these you don't have a product — you have a liability.

3. Ongoing model evaluation

Models change underneath you (Claude 4.6 → 4.7, GPT-5, etc.). Our eval suites catch regressions before users do. We re-run evals on every model upgrade and prompt change.

Cost honesty

LLM API costs are the line item that surprises clients most. A high-volume customer-facing chat can run AU$2,000–$10,000/month in tokens alone — that's before our build fee. We always project 6-month token costs in the quote. For high-volume cases we model the crossover point where self-hosting open-weight models becomes cheaper than API calls.

Explore more

AI integration FAQ

Reliable wins: (1) document Q&A using RAG over your knowledge base — for support, sales enablement, internal compliance lookup; (2) classification and triage — categorising inbound emails, support tickets, leads; (3) document extraction — pulling structured data from invoices, contracts, applications; (4) drafting — first-draft proposals, replies, summaries that humans review. Less reliable in 2026: fully autonomous agents that take real-world actions without supervision, customer-facing chat without escape hatches, or anything where a wrong answer is dangerous (legal, medical, financial advice).
Generic ChatGPT doesn't know your business. RAG (Retrieval-Augmented Generation) connects an LLM to your documents — policies, product specs, knowledge base — so it answers questions grounded in your data, with citations to source documents. This dramatically reduces hallucination and produces answers staff can verify. RAG is the right architecture for any internal Q&A or support use-case.
For most Australian SMBs: start with Anthropic Claude or OpenAI GPT — pay per token, no infrastructure to manage, best capability. Move to open-weight (Llama, Mistral, Qwen) only when (a) data sovereignty rules forbid sending data to US providers, (b) you have a high-volume use case where token costs exceed the cost of running your own GPU infrastructure, or (c) you need fine-tuning that the closed providers don't offer at your tier.
Single use-case RAG (document Q&A on a defined corpus, basic web UI): AU$15,000–$35,000 build, plus AU$300–$2,000/month in LLM API costs. Production agent system with multiple tools and workflow integration: AU$50,000–$150,000 build. We always include a 6-month token cost projection in the quote so you don't get surprised by API bills.
Three-layer defence: (1) RAG with citation enforcement — every claim must be traceable to a source document; (2) confidence scoring — low-confidence answers get flagged for human review instead of being shown to users; (3) eval suites — we maintain a test set of known-answer questions and run it on every model/prompt change. We won't ship an AI feature without these guardrails — getting it wrong damages trust and is hard to recover from.
We default to Anthropic and OpenAI's enterprise tiers, which contractually exclude your data from training and retain it only briefly for safety review. For Australian Privacy Principles (APP) and notifiable-data-breach regulations, this is generally compliant when paired with a documented vendor risk assessment. For sectors with stricter rules (health, government), we use Azure OpenAI with Australian region pinning, or self-hosted open-weight models.

Test your AI use-case before you commit

Book a free 30-minute call. We'll listen to the use-case and tell you honestly whether it's ready for production AI in 2026 — or which boring rules-engine would solve it cheaper.

Book a Free 30-Min Discovery CallSee Pricing