AI Integration Services

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.