02 · AI Infrastructure

AI infrastructure for SMEs in Hong Kong and Australia.

AI infrastructure is how AI shows up in Monday morning work, and where the model runs. That decision comes before you buy an agent.

The two questions we answer

Who this is for

Businesses that run on legacy systems: a tacit knowledge base of WhatsApp conversations, complex spreadsheets, and paper files. You know change is possible. You need guidance on how to execute it without stopping the business.

Your options, honestly

The order matters

Infrastructure comes first. Then governance shaped by how you deploy, then agents with trained staff in charge.

The test is simple: if the work still lives in WhatsApp, Excel, or paper, start here, not with an agent. Read what AI for an SME actually means →

Proof

Case study · 01

Food manufacturer

Multi-channel meal plan business, everything made fresh that morning.

Hong Kong · Multi-channel

Client anonymised. Advisory covered where each workload should run and how to migrate without downtime.

Case study · 02

Flavour manufacturer

Around one hundred staff, ready for AI, unsure where it pays off.

Australia · ~100 staff
3Builds from one audit

Client anonymised. Advisory covered embedding each build into the team\u2019s existing daily routine.

Case study · 03

Precious metals refinery

Regulated and sensitive. The cloud was never an option.

Australia · On-premise

Client anonymised. The cloud-versus-local decision was the engagement.

Modernise your business without stopping it.

Everything starts with a readiness audit: what is worth doing, what is not, and what to ignore.

FAQ

It depends on one question: can this data leave the building? For most workflows, cloud models are cheaper, faster to deploy, and good enough. For regulated businesses or sensitive data, customer records, financials, legal correspondence, a local LLM on your own hardware keeps everything inside your network. Most SMEs end up with a mix, and the audit tells you which workflow goes where.

Less than most owners expect. A dedicated on-site machine running open-source models handles serious workloads for a one-time hardware cost plus setup, with no per-seat subscriptions and no data leaving your premises. We scope the hardware to the workload, not the other way around.

Start from the work, not the tool. We map where work actually lives today, often WhatsApp threads, Excel files, and paper, then put AI next to the jobs your team already does: the overnight report, the quote comparison, the tracker. If staff have to go somewhere special to use it, it will not stick.

No. That is the point of our approach: modernise the business without stopping it. We replace spreadsheets and manual steps one at a time while daily production keeps running. No big-bang migration, no room full of consultants, months not millions.

Your procedures, product specs, and past correspondence turned into a searchable source an AI system can draw on. It means the AI answers from your business, not from the open internet, and it is a prerequisite for any useful agent.