Gamu ships agentic systems of its own — and advises SMEs and enterprises on the architecture, security and model choices behind theirs. From your first model choice to a secured agent fleet in production.
Four practices, one discipline. We put them through our own products before they reach a client engagement — which is why the advice holds up in production.
Multi-step agents that do real work — wired into your tools and data. Architecture chosen deliberately: graphs where you need control, loops where you need latitude. Evals and guardrails that hold under load, and token budgets that don't surprise you at month end.
Red-teaming, threat modelling and governance for LLM and agent systems. Prompt injection, data exfiltration and access control — found and closed before they ship. Including the hard case: agents that run where the data isn't allowed to leave.
Which model, running where, at what cost and what risk — answered from production experience rather than benchmark tables. Plus current best practice in agentic engineering for teams building their own, across AWS, GCP and Azure.
The web apps, APIs, mobile and cloud systems your business actually runs on — built with modern tooling, integrated cleanly, and engineered to last. The same engineering discipline, whether or not there is a model in the stack.
Technical due diligence for M&A and investment — two decades of it across the APAC tech sector, including Fillr → Rakuten and Tesserent → Thales.
Two products in active development. They are where the practices above get tested — at our own cost, before they reach a client engagement.
Where AI can plausibly augment the work an organisation already does — scored role by role against labour-economics data rather than guesswork. Built around a hand-labelled evaluation harness, because a number you cannot measure is not worth reporting.
Mobile products that use AI where it genuinely removes effort, rather than bolting a chat box onto an app. Australian market first.
Networks, filtering, proxies and security at scale — long before anyone called it AI infrastructure. It is why on-prem and hybrid deployment is not new ground for us: the data-boundary problem is the one we have been solving all along.
From our Melbourne incubator, we built core components for Family Zone (now Qoria) — internet-filtering tech now safeguarding children in 100+ countries.
Due-diligence and strategic advice through major acquisitions — Fillr → Rakuten, and Tesserent → Thales Australia.
Our swiss-army debugging browser became a trusted tool for education providers across Asia, Europe and the Americas.
A geo-aware news app that hit the Apple App Store top 10 — used by Australians everywhere, including a solo yachtswoman at sea.
Due-diligence support and advice ahead of Thales' strategic acquisition of Tesserent.
Supported Melbourne dev operations through a massive scale-up of their internet-filtering platform.
Built new online streaming services for the world's largest subscriber-driven broadcaster.
Most engagements begin with one of the first two. You get a deliverable either way — no open-ended discovery, no surprise invoice.
For teams choosing their first model, or unsure the one they picked is still right.
For teams with an agent built, or close to it, that has to survive contact with real data.
For teams who want systems and applications designed, built and supported — AI work, conventional software, or both.
Not sure which fits? Describe the problem and we will tell you — including if the answer is that you do not need us yet.
Tell us about the problem. We'll come back within 24 hours — usually with a sharper question than you arrived with.