Generative AI development for Australian teams that ship.

Strong. Indian Standard Time is UTC+5:30, AEDT is UTC+11, so our 9:30am IST is your 3pm AEDT — a four-hour afternoon overlap with Sydney, Melbourne, and Brisbane working days every weekday. AEST (winter) shifts the overlap by one hour but the pattern holds. For Perth (AWST, UTC+8), the overlap is even stronger — our 9:30am IST is your noon AWST, giving most of an afternoon together. Daily standups and twice-weekly demos with eval-run numbers run inside your business hours. Written async updates land before your morning standup. For engagements that need synchronous morning coverage as well, we can extend to early IST starts on a planned cadence — but it is rarely required.

Yes. Engagement defaults align with the Privacy Act 1988 and the 13 Australian Privacy Principles. Every model and tool call is audit-logged with prompt version, model name, input, output, and operator identity — exportable for an OAIC inquiry or an internal compliance review. A Privacy Impact Assessment is run for engagements processing personal information at scale or operating on sensitive information. For APP 8 (cross-border disclosure of personal information), we explicitly map the data flow in your DPA — exactly where personal information is processed and which overseas endpoints (if any) receive it. PII redaction patterns cover TFN, Medicare numbers, driver licence numbers, and ABNs in customer context. The NDB scheme breach playbook is referenced in the DPA and tabletop-exercised in week one.

Your call. We default to AWS ap-southeast-2 (Sydney), AWS ap-southeast-4 (Melbourne), Azure Australia East (Sydney), Azure Australia Central (Canberra) for federal-adjacent work, or GCP australia-southeast1 (Sydney) / australia-southeast2 (Melbourne). For LLM inference, we route Claude (Anthropic), GPT-4o (Azure OpenAI Service Australia East), or self-hosted Llama 3 on vLLM inside your VPC — picked per your DPA's APP 8 third-party processing terms. For clients with strict no-overseas-inference requirements (federal-adjacent, defence, healthcare), self-hosted Llama 3 70B is the default; we have the deployment runbook for it.

No — Aiinfox itself does not currently hold an IRAP assessment, and we will not pretend otherwise. We are a foreign engineering provider, not an Australian-hosted SaaS, so IRAP assessment of our own platform is not the relevant control. What we do for federal and defence-adjacent clients is structure the engagement so the generative AI workload runs inside the customer's existing IRAP-assessed cloud boundary (typically AWS Australia or Azure Australia Central at PROTECTED classification); our engineers connect over a privileged-access path the customer's security team controls. If your engagement requires our own IRAP assessment, we will say so on the first call and recommend an Australian provider that holds one.

Because every Australian generative AI engagement we have audited that failed in production failed because nobody wrote the eval set. The team tuned a prompt until it looked good on three examples, the model swapped underneath them in a vendor update, and quality regressed silently for weeks before someone noticed in a customer complaint. The eval harness is the regression test for the LLM — a fixed reference set of inputs, expected behaviours (faithful citation, refusal when out of scope, structured output validity), and pass-fail criteria. We wire it in week one and run it on every prompt or model change. It is the difference between shipping a generative AI system and shipping a demo.

Privacy Act-aligned DPAs covering APP entity obligations: documented instructions, APP 11 security, APP 8 cross-border disclosure safeguards, APP 12 / 13 access and correction support, NDB scheme breach notification, and deletion at end of engagement. Mutual NDAs before any technical detail is shared. MSAs for ongoing relationships and per-project SOWs for fixed-price builds. For APRA-regulated clients, our DPA includes the documentation required under CPS 230 (operational risk) and CPS 234 (information security) for third-party arrangements. For healthcare engagements, state-level health privacy agreements layer on top. Aiinfox Pvt. Ltd. is a registered Indian entity invoicing in AUD or USD — GST does not apply on B2B services supplied from India to Australia under the general rule, but your tax adviser should confirm.

Most v1 generative AI engagements at Aiinfox land between AUD $50,000 and AUD $190,000 fixed-price for a focused build — a copilot, a RAG-grounded GenAI app, a voice pipeline, or a fine-tuned domain model. Larger multi-quarter engagements with custom fine-tuning, bespoke evals, IRAP-boundary integration work, and a regulated platform integration typically reach AUD $230,000 to AUD $400,000. The cost difference versus a Sydney or Melbourne AI consultancy lands roughly 30 to 50 percent lower on senior rates — useful, but the headline is the engineer on your kickoff call writes your prompts, your evals, and your code through launch, with no swap-out to a junior pool mid-engagement.

Yes — takeover audits are routine. Step one is reading the code, the prompts, the eval results (if any exist), the data pipelines, and the cost telemetry. Step two is shipping the smallest valuable change to prove we understand the system — usually adding the eval harness or fixing the retrieval layer. Step three is the longer-term plan: incremental stabilisation, a parallel rebuild, or shutting it down and starting over. Most takeovers we see did not need a rewrite; they needed evals, guardrails, observability, and a senior engineer on the build.